CN116560895A - Fault diagnosis method for mechanical equipment - Google Patents

Fault diagnosis method for mechanical equipment Download PDF

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CN116560895A
CN116560895A CN202310839479.7A CN202310839479A CN116560895A CN 116560895 A CN116560895 A CN 116560895A CN 202310839479 A CN202310839479 A CN 202310839479A CN 116560895 A CN116560895 A CN 116560895A
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CN116560895B (en
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王宏伟
姚林虎
刘峰
李炙芫
王浩然
付翔
曹文艳
王洪利
陶磊
李永安
耿毅德
闫志蕊
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    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract

The disclosure provides a fault diagnosis method for mechanical equipment, relates to the technical field of mechanical equipment fault diagnosis, and can be used for a scene of fault diagnosis of the mechanical equipment based on rolling bearing vibration signals. The method comprises the following steps: determining a metric matrix according to the metric information of the vibration signal; determining distance information in the measurement information by constructing a nearest neighbor point neighbor graph; mapping different information represented by the distance information into a nuclear space and determining a nuclear matrix and a nuclear space pheromone; reconstructing the measurement matrix according to the nuclear matrix and the nuclear space pheromone fusion tag discrimination information; and performing dimension reduction processing on the characteristics of the reconstructed metric matrix, and inputting the dimension reduction processed result and label discrimination information into a classifier to obtain a fault classification result. The fault diagnosis method can reduce the influence of super parameters, reduce the loss of data in the characteristic extraction process and improve the accuracy of fault classification.

Description

用于机械装备的故障诊断方法Fault Diagnosis Method for Mechanical Equipment

技术领域technical field

本公开涉及机械装备故障诊断技术领域,具体而言,涉及一种用于机械装备的故障诊断方法。The present disclosure relates to the technical field of fault diagnosis of mechanical equipment, in particular, to a fault diagnosis method for mechanical equipment.

背景技术Background technique

传统故障诊断方法为提取信号中显著的故障特征奠定了基础,但在智能化发展的过程中,机械装备结构和运行状态的复杂程度都明显提高,研究智能故障诊断技术成为机械装备故障诊断的重要任务。人工智能作为智能故障诊断的常用工具,其中包含许多机器学习算法,这些算法可以有效地提炼数据集中的显著特征,并已经成功地应用于各类旋转设备的故障诊断中,与其他传统故障诊断技术相比有更高的准确率。Traditional fault diagnosis methods have laid the foundation for extracting significant fault features in signals. However, in the process of intelligent development, the complexity of mechanical equipment structure and operating status has increased significantly. Research on intelligent fault diagnosis technology has become an important aspect of mechanical equipment fault diagnosis. Task. As a common tool for intelligent fault diagnosis, artificial intelligence contains many machine learning algorithms, which can effectively extract the salient features in the data set, and have been successfully applied to the fault diagnosis of various rotating equipment. Compared with other traditional fault diagnosis techniques higher accuracy than .

目前,机械装备运行数据的数据量呈指数倍增长,用于机械装备故障诊断的机器学习算法会受到数据量的影响,并且在传统的机器学习算法没有将同一数据不同空间的物理信息进行融合,会造成数据的丢失,导致故障分类的准确性降低。At present, the data volume of mechanical equipment operation data is increasing exponentially, and the machine learning algorithm used for mechanical equipment fault diagnosis will be affected by the data volume, and the traditional machine learning algorithm does not integrate the physical information of the same data in different spaces. It will cause the loss of data and reduce the accuracy of fault classification.

需要说明的是,在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解,因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。It should be noted that the information disclosed in the above background section is only for enhancing the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art.

发明内容Contents of the invention

本公开实施例的目的在于提供一种故障诊断方法,进而可以降低超参数对机器学习算法的影响,并且可以将多空间的特征信息进行融合,减少数据的丢失去,从而提高故障分类器进行故障诊断的准确性。The purpose of the embodiments of the present disclosure is to provide a fault diagnosis method, which can reduce the influence of hyperparameters on machine learning algorithms, and can integrate multi-space feature information to reduce data loss, thereby improving the performance of fault classifiers. diagnostic accuracy.

根据本公开实施例的第一方面,提供了一种用于机械装备的故障诊断方法,包括:According to the first aspect of the embodiments of the present disclosure, there is provided a fault diagnosis method for mechanical equipment, including:

获取机械装备振动信号的度量信息,并通过度量信息确定度量矩阵;Obtain the measurement information of the vibration signal of the mechanical equipment, and determine the measurement matrix through the measurement information;

根据自适应近邻策略构建真近邻点近邻图,并通过真近邻点近邻图确定度量信息中的距离信息;Construct the true nearest neighbor neighbor graph according to the adaptive neighbor strategy, and determine the distance information in the measurement information through the true neighbor neighbor graph;

根据预设的指数线性核函数确定至少两个核空间;determining at least two kernel spaces according to a preset exponential linear kernel function;

将距离信息表示的不同信息映射到不同的核空间中,确定与核空间对应的核矩阵和核空间信息素;Map different information represented by distance information to different nuclear spaces, and determine the nuclear matrix and nuclear space pheromone corresponding to the nuclear space;

根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构;Reconstruct the metric matrix according to the discriminant information of the nuclear matrix and nuclear space pheromone fusion labels;

对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。The dimensionality reduction processing is performed on the features of the reconstructed measurement matrix, and the dimensionality reduction processing results and label discrimination information are input into the pre-trained fault classifier to obtain the fault classification results.

根据本公开实施例的第二方面,提供了一种用于机械装备的故障诊断装置,包括:According to the second aspect of the embodiments of the present disclosure, there is provided a fault diagnosis device for mechanical equipment, including:

度量信息获取模块,用于获取机械装备振动信号的度量信息,并通过度量信息确定度量矩阵;The measurement information acquisition module is used to obtain the measurement information of the vibration signal of the mechanical equipment, and determine the measurement matrix through the measurement information;

真近邻点近邻图构建模块,用于根据自适应近邻策略构建真近邻点近邻图,并通过真近邻点近邻图确定度量信息中的距离信息;A true neighbor neighbor graph building module, used to construct a true neighbor neighbor graph according to an adaptive neighbor strategy, and determine distance information in the measurement information through the true neighbor neighbor graph;

核空间构造模块,用于根据预设的指数线性核函数确定至少两个核空间;A kernel space construction module, configured to determine at least two kernel spaces according to a preset exponential linear kernel function;

特征提取模块,用于将距离信息表示的不同信息映射到不同的核空间中,确定与核空间对应的核矩阵和核空间信息素;The feature extraction module is used to map different information represented by the distance information into different nuclear spaces, and determine the nuclear matrix and nuclear space pheromone corresponding to the nuclear space;

度量矩阵重构模块,用于根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构;The metric matrix reconstruction module is used to reconstruct the metric matrix according to the kernel matrix and nuclear space pheromone fusion label discrimination information;

降维模块,用于对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。The dimensionality reduction module is used to perform dimensionality reduction processing on the features of the reconstructed measurement matrix, and input the dimensionality reduction processing results and label discrimination information into the pre-trained fault classifier to obtain fault classification results.

本公开的实施例提供的技术方案可以包括以下有益效果:The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

通过本公开实施例,获取振动信号的度量信息,并确定度量矩阵;通过构建真近邻点近邻图确定度量信息中的距离信息;再将距离信息表示的不同信息映射到不同的核空间中,并确定核矩阵和核空间信息素;然后根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构;对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入分类器中,得到故障分类结果。一方面,通过根据振动信号的特征数据构建真近邻点近邻图,可以通过对振动信号的信息进行筛选确定真近邻点近邻图,传统的流形学习方法通常依赖于设置超参数来确定近邻点的数量,并且传统的超参数需要手动设置,对算法的性能和结果有很大影响,而基于近邻点融合距离信息和角度信息动态的筛选真近邻点,算法可以自适应的选择合适的近邻点,而不依赖于手动设定的超参数,从而降低了超参数对机器学习算法的影响;另一方面,基于指数线性核函数确定核空间,并将多个高维空间中的同一特征融合到一起进行重构度量矩阵,通过核函数确定核空间可以更好地捕捉非线性模式和特征之间的复杂相互作用,从而提供更丰富的数据表示,将多个高维空间中的同一特征融合到一起进行重构度量矩阵可以反映数据的本质特征,更好地保留原始数据的结构和关系,这样可以避免信息的损失和失真,从而减少数据的丢失,保证特征数据的有效性以及准确性,从而提高故障分类的准确性。Through the embodiments of the present disclosure, the metric information of the vibration signal is obtained, and the metric matrix is determined; the distance information in the metric information is determined by constructing a true neighbor point neighbor graph; and then different information represented by the distance information is mapped to different kernel spaces, and Determine the kernel matrix and nuclear space pheromone; then reconstruct the measurement matrix according to the fusion label discrimination information of the nuclear matrix and nuclear space pheromone; perform dimensionality reduction processing on the features of the reconstructed measurement matrix, and reduce the dimensionality The results and label discrimination information are input into the classifier to obtain the fault classification result. On the one hand, by constructing a true neighbor map based on the characteristic data of the vibration signal, the true neighbor map can be determined by screening the information of the vibration signal. Traditional manifold learning methods usually rely on setting hyperparameters to determine the true neighbor map. Quantity, and the traditional hyperparameters need to be set manually, which has a great impact on the performance and results of the algorithm. Based on the fusion of distance information and angle information of neighboring points, the real neighboring points are dynamically screened, and the algorithm can adaptively select the appropriate neighboring points. It does not rely on manually set hyperparameters, thereby reducing the influence of hyperparameters on machine learning algorithms; on the other hand, the kernel space is determined based on the exponential linear kernel function, and the same features in multiple high-dimensional spaces are fused together Reconstructing the metric matrix and determining the kernel space through the kernel function can better capture the complex interaction between nonlinear patterns and features, thereby providing a richer data representation and fusing the same features in multiple high-dimensional spaces together Reconstructing the metric matrix can reflect the essential characteristics of the data and better retain the structure and relationship of the original data, which can avoid the loss and distortion of information, thereby reducing the loss of data, ensuring the validity and accuracy of feature data, and improving Accuracy of fault classification.

应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,并不能限制本公开。It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

附图说明Description of drawings

此处的附图被并入说明书中并构成本说明书的一部分,示出了符合本公开的实施例,并与说明书一起用于解释本公开的原理。显而易见地,下面描述中的附图仅仅是本公开的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure. Apparently, the drawings in the following description are only some embodiments of the present disclosure, and those skilled in the art can obtain other drawings according to these drawings without creative efforts.

图1示意性示出了一种用于机械装备的故障诊断方法的流程图。FIG. 1 schematically shows a flow chart of a fault diagnosis method for mechanical equipment.

图2示意性示出了根据本公开的一些实施例确定故障种类的流程图。Fig. 2 schematically shows a flowchart of determining a fault type according to some embodiments of the present disclosure.

图3示意性示出了根据本公开的一些实施例确定真近邻点近邻图的流程图。Fig. 3 schematically shows a flow chart of determining a neighbor graph of true neighbors according to some embodiments of the present disclosure.

图4示意性示出了根据本公开的一些实施例的确定将距离信息表示的不同的信息映射到不同的核空间的流程图。Fig. 4 schematically shows a flow chart of determining to map different information represented by distance information to different kernel spaces according to some embodiments of the present disclosure.

图5示意性示出了可以应用于本公开实施例的用于机械装备的故障诊断装置的示意图。Fig. 5 schematically shows a schematic diagram of a fault diagnosis device for mechanical equipment that can be applied to an embodiment of the present disclosure.

图6示意性示出了根据本公开的一些实施例的电子设备的计算机系统的结构示意图。Fig. 6 schematically shows a structural diagram of a computer system of an electronic device according to some embodiments of the present disclosure.

在附图中,相同或对应的标号表示相同或对应的部分。In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

具体实施方式Detailed ways

这里将详细地对示例性实施例进行说明,其示例表示在附图中。下面的描述涉及附图时,除非另有表示,不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本说明书相一致的所有实施方式。相反,它们仅是与如所附权利要求书中所详述的、本说明书的一些方面相一致方法的例子。Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with this specification. Rather, they are merely examples of approaches consistent with some aspects of the specification as recited in the appended claims.

在本说明书使用的术语是仅仅出于描述特定实施例的目的,而非旨在限制本说明书。在本说明书和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式,除非上下文清楚地表示其他含义。还应当理解,本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。The terms used in this specification are for the purpose of describing particular embodiments only, and are not intended to limit the specification. As used in this specification and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

应当理解,尽管在本说明书可能采用术语第一、第二、第三等来描述各种信息,但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如,在不脱离本说明书范围的情况下,第一信息也可以被称为第二信息,类似地,第二信息也可以被称为第一信息。取决于语境,如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”。It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be called second information, and similarly, second information may also be called first information. Depending on the context, the word "if" as used herein may be interpreted as "at" or "when" or "in response to a determination."

在相关技术中,存在以下技术问题:In related technologies, there are the following technical problems:

在现有的故障诊断技术中,为保证诊断的准确率,通常利用机器学习算法确定学习参数,并且将参数输入到预训练的故障分类器中进行分类,但由于机械装备运行数据的数据量呈指数倍增长,机器学习算法会超参数的影响,并且在机器学习算法进行确定学习参数时,会出现数据的欠拟合或过拟合现象,造成数据丢失从而降低故障分类的准确性。In the existing fault diagnosis technology, in order to ensure the accuracy of diagnosis, machine learning algorithms are usually used to determine the learning parameters, and the parameters are input into the pre-trained fault classifier for classification. Exponential growth, the machine learning algorithm will be affected by hyperparameters, and when the machine learning algorithm determines the learning parameters, data underfitting or overfitting will occur, resulting in data loss and reducing the accuracy of fault classification.

基于相关技术中的一个或者多个问题,本公开实施例首先提出了一种用于机械装备的故障诊断方法,该方法可以由机械装备上的计算模型执行,也可以由独立于机械设备的终端设备或者服务器执行,下面以服务器对从机械装备上采集的振动信号进行故障分析为例,对本实施例中的机械装备的故障诊断方法进行说明。Based on one or more problems in related technologies, the embodiments of the present disclosure firstly propose a fault diagnosis method for mechanical equipment, which can be executed by a calculation model on the mechanical equipment, or by a terminal independent of the mechanical equipment The device or the server executes the fault diagnosis method of the mechanical equipment in this embodiment by taking the server's fault analysis of the vibration signal collected from the mechanical equipment as an example.

如图1所述,图1是本公开根据一示例性实施例示出的一种用于机械装备的故障诊断方法的流程图,包括以下步骤:As shown in FIG. 1, FIG. 1 is a flow chart of a fault diagnosis method for mechanical equipment according to an exemplary embodiment of the present disclosure, including the following steps:

在步骤S110,获取机械装备振动信号的度量信息,并通过所述度量信息确定度量矩阵;In step S110, the metric information of the vibration signal of the mechanical equipment is acquired, and a metric matrix is determined through the metric information;

在步骤S120,根据自适应近邻策略构建真近邻点近邻图,并通过所述真近邻点近邻图确定所述度量信息中的的距离信息;In step S120, constructing a true neighbor graph according to an adaptive neighbor strategy, and determining distance information in the measurement information through the true neighbor graph;

在步骤S130,根据预设的指数线性核函数确定至少两个核空间;In step S130, at least two kernel spaces are determined according to a preset exponential linear kernel function;

在步骤S140,将所述距离信息表示的不同信息映射到不同的所述核空间中,确定与所述核空间对应的核矩阵和核空间信息素;In step S140, different information represented by the distance information is mapped to different nuclear spaces, and a nuclear matrix and nuclear space pheromones corresponding to the nuclear spaces are determined;

在步骤S150,根据所述核矩阵和所述核空间信息素融合标签判别信息对所述度量矩阵进行重构;In step S150, the metric matrix is reconstructed according to the kernel matrix and the nuclear space pheromone fusion label discrimination information;

在步骤S160,对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。In step S160, dimensionality reduction processing is performed on the features of the reconstructed measurement matrix, and the result after dimensionality reduction processing and label discrimination information are input into a pre-trained fault classifier to obtain a fault classification result.

根据本公开中的故障诊断方法,可以获取机械装备振动信号的特征数据,并通过特征数据确定真近邻点近邻图;根据预设的指数线性核函数确定至少两个核空间;将距离信息映射到核空间中,确定度量矩阵与所述核空间对应的核矩阵和核空间信息素;根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构。一方面,通过根据振动信号的特征数据构建真近邻点近邻图,可以通过对振动信号的信息进行筛选确定真近邻点近邻图,降低了超参数对机器学习算法的影响;另一方面,基于指数线性核函数确定核空间,并将多个高维空间中的同一特征融合到一起进行重构度量矩阵,减少数据丢失,从而提高故障分类的准确性。According to the fault diagnosis method in the present disclosure, the characteristic data of the vibration signal of the mechanical equipment can be obtained, and the true neighbor graph can be determined through the characteristic data; at least two kernel spaces can be determined according to the preset exponential linear kernel function; the distance information can be mapped to In the kernel space, determine the kernel matrix and the kernel space pheromone corresponding to the metric matrix and the kernel space; reconstruct the metric matrix according to the fusion label discrimination information of the kernel matrix and the kernel space pheromone. On the one hand, by constructing the true neighbor graph based on the characteristic data of the vibration signal, the true neighbor graph can be determined by screening the information of the vibration signal, which reduces the influence of hyperparameters on the machine learning algorithm; on the other hand, based on the index The linear kernel function determines the kernel space, and fuses the same features in multiple high-dimensional spaces together to reconstruct the metric matrix to reduce data loss and improve the accuracy of fault classification.

下面,对步骤S110至步骤S160进行详细说明。Next, step S110 to step S160 will be described in detail.

在步骤S110中,获取机械装备振动信号的度量信息,并通过所述度量信息确定度量矩阵;In step S110, the metric information of the vibration signal of the mechanical equipment is acquired, and a metric matrix is determined through the metric information;

在本公开的一示例实施例中,机械装备振动信号是指机械装备在运行过程中产生的震动和振动的物理量的表示,用于分析和评估机械装备的运行状态、性能和健康状况。获取机械装备的振动信号可以监测机械设备的运行状态,例如,获取机械装备振动信号的振动幅度可以反映机械装备的运行质量和平衡性;获取机械装备振动信号可以分析得到机械装备振动频率成分,从而确定机械装备的旋转速度、共振点等信息;当然,还可以通过分析机械装备振动信号的频谱和频率成分,判断装备是否存在不平衡、轴承故障、齿轮磨损、松动等故障问题。本示例实施例对于获取机械装备振动信号的作用不做特别限定。In an exemplary embodiment of the present disclosure, the mechanical equipment vibration signal refers to the vibration generated by the mechanical equipment during operation and the representation of the physical quantity of the vibration, which is used to analyze and evaluate the operating state, performance and health status of the mechanical equipment. Obtaining vibration signals of mechanical equipment can monitor the operating status of mechanical equipment. For example, obtaining the vibration amplitude of mechanical equipment vibration signals can reflect the operating quality and balance of mechanical equipment; obtaining mechanical equipment vibration signals can analyze the vibration frequency components of mechanical equipment, thereby Determine the rotation speed, resonance point and other information of mechanical equipment; of course, by analyzing the frequency spectrum and frequency components of the vibration signal of mechanical equipment, it is also possible to determine whether the equipment has imbalance, bearing failure, gear wear, looseness and other failure problems. In this example embodiment, there is no special limitation on the effect of acquiring vibration signals of mechanical equipment.

度量信息是指与振动信号相关的所有的特征数据,可以用于分析机械装备的工作状态并对机械装备进行故障诊断,例如,度量信息可以是表示采样时间和振动信号的随时间变化的时间域数据,也可以是用于分析振动信号在不同频率上的能量分布的频域数据,当然还可以是用于观察振动信号频谱特征的频谱图,或者是用于描述振动信号在时间域上振动特征的时域指标,不同的度量信息可以提供不同层次和角度的信息,本示例实施例对表征振动信号的度量信息的类型不做特别限定。Metric information refers to all characteristic data related to vibration signals, which can be used to analyze the working status of mechanical equipment and perform fault diagnosis on mechanical equipment. For example, metric information can be the time domain representing the sampling time and vibration signal over time The data can also be the frequency domain data used to analyze the energy distribution of the vibration signal at different frequencies, of course it can also be the spectrogram used to observe the frequency spectrum characteristics of the vibration signal, or it can be used to describe the vibration characteristics of the vibration signal in the time domain Different metric information can provide information of different levels and angles, and this example embodiment does not specifically limit the type of metric information that characterizes the vibration signal.

可选的,度量信息可以是表示振动信号中数据点之间的角度和距离的信息,角度信息为振动信号在高维欧式空间中数据点的角度关系,距离信息为振动信号在高维欧式空间中的数据点的距离,可以通过角度信息和距离信息确定振动信号中各数据点之间的关系。Optionally, the metric information can be the information representing the angle and distance between the data points in the vibration signal, the angle information is the angle relationship of the data points in the vibration signal in the high-dimensional Euclidean space, and the distance information is the vibration signal in the high-dimensional Euclidean space The distance of the data points in the vibration signal can determine the relationship between the data points in the vibration signal through the angle information and distance information.

其中,度量矩阵是指衡量特征度量空间中任意两个特征向量之间的距离的矩阵,用于描述度量空间中各特征向量之间的距离数据。可以根据度量矩阵中的每个元素确定两个特征向量之间的距离,例如,对于一个n维度量空间,度量矩阵的维度为n×n,其中第i行第j列的元素表示度量空间中第i个和第j个向量之间的距离。度量矩阵的对角线元素通常为0,表示一个向量与自身的距离为0,而非对角线元素表示不同向量之间的距离。通过度量矩阵,我们可以测量向量空间中的距离,从而计算出向量之间的相似度和差异度。可以根据具体任务的需求来定义距离的度量方式,从而更好地对数据进行分析和建模,本示例实施例对于度量矩阵的选择不做特别限定。Wherein, the metric matrix refers to a matrix that measures the distance between any two eigenvectors in the feature metric space, and is used to describe the distance data between the eigenvectors in the metric space. The distance between two eigenvectors can be determined according to each element in the metric matrix. For example, for an n-dimensional metric space, the dimension of the metric matrix is n×n, where the element in row i and column j represents the metric space The distance between the i-th and j-th vectors. The diagonal elements of the metric matrix are usually 0, indicating that a vector has a distance of 0 from itself, while the off-diagonal elements indicate the distance between different vectors. Through the metric matrix, we can measure the distance in the vector space to calculate the similarity and difference between the vectors. The distance measurement method can be defined according to the requirement of a specific task, so as to better analyze and model the data, and this example embodiment does not specifically limit the selection of the measurement matrix.

在步骤S120中根据自适应近邻策略构建真近邻点近邻图,并通过真近邻点近邻图确定度量信息中的的距离信息。In step S120, the true neighbor neighbor graph is constructed according to the adaptive neighbor strategy, and the distance information in the measurement information is determined through the true neighbor neighbor graph.

在本公开的一示例实施例中,真近邻点近邻图是指样本点与真近邻点构成的具有一定角度和距离的图,用于将振动信号高维欧式空间中的部分特征数据映射到低维空间中进行表示,真近邻点近邻图可以是有固定真近邻点数的真近邻点近邻图,也可以是根据真近邻点个数动态变化的真近邻点近邻图,本示例实施例对于真近邻点近邻图的具体状态不做特别限定。In an exemplary embodiment of the present disclosure, the true neighbor point neighbor graph refers to a graph with a certain angle and distance between the sample point and the true neighbor point, which is used to map part of the characteristic data in the high-dimensional Euclidean space of the vibration signal to the low-dimensional dimensional space, the true neighbor graph can be a true neighbor graph with a fixed number of true neighbors, or a true neighbor graph that dynamically changes according to the number of true neighbors. This example embodiment is for true neighbors The specific state of the point neighbor graph is not particularly limited.

举例而言,可以选取振动信号样本点的角度信息和距离信息构建动态的真近邻点近邻图,真近邻点近邻图可以将样本点以及真近邻点在高维欧式空间中的距离信息和角度信息降维到低维空间中表示,并且,通过真近邻点近邻图得到的样本点和真近邻点之间的角度信息和距离信息和高维空间中样本点和真近邻点之间的角度信息和距离信息保持一致,降低了获取高维空间中样本点和真近邻点之间的角度信息和距离信息的难度,并且保持了样本点和真近邻点之间关系的稳定性。For example, the angle information and distance information of the vibration signal sample points can be selected to construct a dynamic true neighbor neighbor map. The true neighbor neighbor map can combine the distance information and angle information of sample points and true neighbor points in high-dimensional Euclidean space Dimensionality reduction is expressed in a low-dimensional space, and the angle information and distance information between the sample point and the true neighbor point obtained through the true neighbor point neighbor graph and the angle information between the sample point and the true neighbor point in the high-dimensional space and The distance information is consistent, which reduces the difficulty of obtaining the angle information and distance information between the sample point and the true neighbor point in the high-dimensional space, and maintains the stability of the relationship between the sample point and the true neighbor point.

真近邻点近邻图确定度量信息中的的距离信息是指在样本点和真近邻点之间的测地距离信息,测地距离信息是用于计算振动信号高维空间中样本点和真近邻点之间的距离并且通过计算样本点和真近邻点之间最短路径的长度得到的。测地距离信息可以是球面上的测地距离,也可以是二维平面上的测地距离,当测地距离信息为二维平面上的测地距离时,通过用欧几里得距离来表示测地距离;当然,测地距离信息还可以是曲面上的测地距离,本示例实施例对测地距离信息的所在平面不做特别限定。The distance information in the measurement information of the true neighbor point neighbor map is the geodesic distance information between the sample point and the true neighbor point. The geodesic distance information is used to calculate the sample point and the true neighbor point in the high-dimensional space of the vibration signal. The distance between and is obtained by calculating the length of the shortest path between the sample point and the true neighbor point. The geodesic distance information can be the geodesic distance on the sphere or the geodesic distance on the two-dimensional plane. When the geodesic distance information is the geodesic distance on the two-dimensional plane, it is represented by the Euclidean distance Geodesic distance; of course, the geodesic distance information may also be a geodesic distance on a curved surface, and this example embodiment does not specifically limit the plane where the geodesic distance information is located.

在步骤S130,根据预设的指数线性核函数确定至少两个核空间。In step S130, at least two kernel spaces are determined according to a preset exponential linear kernel function.

在本公开的一示例实施例中,指数线性核函数是指基于指数线性核函数理论构造的用于机器学习算法的核函数,用于将振动信号的高维数据映射到核空间中。指数线性核函数通过选取合适的参数确定核空间,例如,可以根据先前类似问题的经验选择常见值来作为构造核空间的参数,也可以通过在一定范围内进行网格搜索来寻找最佳的参数,当然,也可以通过贝叶斯优化算法通过在每次迭代中根据上一次的结果调整参数,本示例实施例对指数线性核函数构建核空间时选取参数的方法以及核空间的构造方法不做特别限定。In an exemplary embodiment of the present disclosure, the exponential linear kernel function refers to a kernel function used in a machine learning algorithm constructed based on the exponential linear kernel function theory, and is used for mapping high-dimensional data of a vibration signal into a kernel space. The exponential linear kernel function determines the kernel space by selecting appropriate parameters. For example, common values can be selected as the parameters for constructing the kernel space based on previous experience of similar problems, or the best parameters can be found by performing grid search within a certain range. , of course, the Bayesian optimization algorithm can also be used to adjust the parameters according to the previous result in each iteration. This example embodiment does not discuss the method of selecting parameters when constructing the kernel space of the exponential linear kernel function and the construction method of the kernel space. special limited.

其中,根据预设的指数线性核函数确定至少两个核空间是为了存放振动信号中的不同的特征数据,例如,可以是根据指数线性核函数确定两个核空间,在两个核空间中分别存放两种不同的特征数据,也可以根据指数线性核函数确定三个或者三个以上的核空间,在三个或者三个以上核空间中分别存放三种或者三种以上的特征数据,指数线性核函数确定的核空间的数量和需要存放的特征数据的种类数一致,本示例实施例对指数线性核函数确定的核函数的个数不做特别限定。Wherein, at least two kernel spaces are determined according to the preset exponential linear kernel function in order to store different characteristic data in the vibration signal, for example, two kernel spaces may be determined according to the exponential linear kernel function, respectively To store two different feature data, three or more kernel spaces can also be determined according to the exponential linear kernel function, and three or more feature data are stored in three or more kernel spaces, and the exponential linear The number of kernel spaces determined by the kernel function is consistent with the number of types of feature data to be stored, and this example embodiment does not specifically limit the number of kernel functions determined by the exponential linear kernel function.

在步骤S140,将所述距离信息表示的不同信息映射到不同的所述核空间中,确定与所述核空间对应的核矩阵和核空间信息素;In step S140, different information represented by the distance information is mapped to different nuclear spaces, and a nuclear matrix and nuclear space pheromones corresponding to the nuclear spaces are determined;

在本公开的一示例实施例中,核矩阵是指根据指数线性核函数理论构造的矩阵,与指数线性核函数矩阵相同,用于表示振动信号的测地距离信息,核矩阵可以用于不同核空间中特征信息的融合,举例而言,可以通过将指数线性核函数理论中的线性权重设置为核空间中的相关数据,把该核空间的特征信息融合到核矩阵中,实现通过核矩阵完成不同空间的特征信息的融合。In an exemplary embodiment of the present disclosure, the kernel matrix refers to a matrix constructed according to the exponential linear kernel function theory, which is the same as the exponential linear kernel function matrix, and is used to represent the geodesic distance information of the vibration signal. The kernel matrix can be used for different kernels The fusion of feature information in the space, for example, can be done by setting the linear weight in the exponential linear kernel function theory as the relevant data in the kernel space, and fusing the feature information of the kernel space into the kernel matrix. Fusion of feature information from different spaces.

核空间信息素是指表示对应核空间内特征的数据,用于度量矩阵的重构以及构造核矩阵,能够确定的核空间信息素的个数通常和核空间的个数相同,例如,当核空间有两个时,可以根据核空间确定两个核空间信息素;当核空间有三个或者三个以上时,可以通过三个或者三个以上的核空间确定三个或者三个以上的核空间信息素,本示例实施例对于核空间信息素的个数没有特别限定,一般情况下能确定的核空间信息素的个数和核空间的个数相同。The nuclear space pheromone refers to the data representing the characteristics in the corresponding nuclear space, which is used for the reconstruction of the measurement matrix and the construction of the nuclear matrix. The number of nuclear space pheromones that can be determined is usually the same as the number of nuclear spaces. For example, when the nuclear space When there are two spaces, two nuclear space pheromones can be determined according to the nuclear space; when there are three or more nuclear spaces, three or more nuclear spaces can be determined through three or more nuclear spaces For pheromones, the number of nuclear space pheromones in this example embodiment is not particularly limited, and generally the number of nuclear space pheromones that can be determined is the same as the number of nuclear spaces.

在步骤S150,根据所述核矩阵和所述核空间信息素融合标签判别信息对所述度量矩阵进行重构;In step S150, the metric matrix is reconstructed according to the kernel matrix and the nuclear space pheromone fusion label discrimination information;

在本公开的一示例实施例中,标签判别信息是指通过分析振动信号中数据点的特征和属性,用于将数据点准确的分类为事先定义好的不同的类别或者标签,其中,融合标签判别信息对度量矩阵进行重构,可以将度量矩阵中一些无法区分为不同类别的数据点进行与标签判别信息融合,在重构度量矩阵时考虑不同类别之间的差异,提高度量矩阵的判别能力核和分类性能。In an exemplary embodiment of the present disclosure, label discrimination information refers to accurately classifying data points into different categories or labels defined in advance by analyzing the characteristics and attributes of data points in vibration signals, wherein the fusion label The discriminant information reconstructs the metric matrix, and some data points in the metric matrix that cannot be distinguished into different categories can be fused with the label discriminant information. When reconstructing the metric matrix, the differences between different categories are considered to improve the discriminative ability of the metric matrix. Kernel and classification performance.

通过核矩阵以及核空间信息素融合标签判别信息对度量矩阵进行重构可以增强原始数据中特征的表达能力,重构后的度量矩阵能够更好地捕捉输入数据的关键特征,从而提高后续处理任务的准确性和性能;此外,重构过程可以使度量矩阵更加聚焦于具有更高区分度的特征,减少冗余和无用信息的影响,有助于提高数据的选择性,可以更好地区分不同类别之间的差异,降低类别内部的变异性;同时,通过融合标签判别信息,重构的度量矩阵能够更好地结合数据的语义信息和类别关系,有助于在重构后的度量矩阵中保留更多类别间的差异性,提高分类或识别任务的性能;通过重构度量矩阵,可以提高数据特征的表达能力、选择性和信息整合,从而为故障分类提供更好的输入,获得更准确的结果和更高的性能。The reconstruction of the metric matrix through the fusion of the kernel matrix and the nuclear space pheromone label discriminant information can enhance the expressive ability of the features in the original data, and the reconstructed metric matrix can better capture the key features of the input data, thereby improving the subsequent processing tasks. accuracy and performance; in addition, the reconstruction process can make the measurement matrix more focused on features with higher discrimination, reduce the influence of redundant and useless information, help to improve the selectivity of data, and can better distinguish different The difference between categories can reduce the variability within the category; at the same time, by fusing the label discriminant information, the reconstructed metric matrix can better combine the semantic information of the data and the category relationship, which is helpful in the reconstructed metric matrix. Retain more differences between categories to improve the performance of classification or recognition tasks; by reconstructing the measurement matrix, the expressiveness, selectivity and information integration of data features can be improved, thereby providing better input for fault classification and obtaining more accurate results and higher performance.

在步骤S160,对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。In step S160, dimensionality reduction processing is performed on the features of the reconstructed measurement matrix, and the result after dimensionality reduction processing and label discrimination information are input into a pre-trained fault classifier to obtain a fault classification result.

在本公开的一示例实施例中,降维处理是指将重构后的度量矩阵中的特征进行转换和压缩,用于减少特征的维度,可以通过线性或者非线性的方式对重构后的度量矩阵进行降维处理,例如,可以通过特征值分解的非线性处理方法对重构后的度量矩阵进行降维处理,也可以是线性判别分析(LDA)的线性处理方法对重构后的度量矩阵进行降维处理,当然,还可以通过其他的方法对重构后的度量矩阵进行降维处理,本示例实施例对于对重构后的度量矩阵的降维方法不做特别限定。In an example embodiment of the present disclosure, the dimensionality reduction process refers to transforming and compressing the features in the reconstructed metric matrix to reduce the dimension of the features, and the reconstructed metric matrix can be linearly or nonlinearly Dimensionality reduction processing of the measurement matrix, for example, the dimensionality reduction processing of the reconstructed measurement matrix can be performed by the nonlinear processing method of eigenvalue decomposition, or the linear processing method of linear discriminant analysis (LDA) can be used to reduce the dimensionality of the reconstructed measurement matrix Dimensionality reduction processing is performed on the matrix. Of course, other methods may also be used to perform dimensionality reduction processing on the reconstructed measurement matrix. This example embodiment does not specifically limit the dimensionality reduction method of the reconstructed measurement matrix.

预训练的故障分类器是指通过大规模故障数据的训练,学习到故障的模式和特征,从而能够对新的输入数据进行故障分类的分类器,用于将输入的特征向量与标签进行分类。预训练的故障分类器可以是多种机器学习模型和深度学习的模型,例如,可以是支持向量机的SVM分类器,也可以是支持随机森林的分类器,当然,预训练的故障分类器可以根据实际的需求来选择,本示例实施例对于预训练的故障分类器的种类不做特别限定。The pre-trained fault classifier refers to a classifier that learns fault patterns and characteristics through training on large-scale fault data, so that it can classify faults on new input data, and is used to classify input feature vectors and labels. The pre-trained fault classifier can be a variety of machine learning models and deep learning models, for example, it can be the SVM classifier of the support vector machine, or it can be a classifier that supports random forests. Of course, the pre-trained fault classifier can be It is selected according to actual requirements, and this example embodiment does not specifically limit the types of pre-trained fault classifiers.

通过根据振动信号的角度信息和距离信息确定真近邻点近邻图,并基于真近邻点近邻图利用最短路径算法计算,得到测地距离矩阵和测地距离表述的度量信息,通过真近邻点近邻图确定测地距离矩阵和测地距离矩阵表示的度量信息可以减少超参数对机器算法模型的影响;并通过将测地距离矩阵和测地距离表述的度量信息分别映射到不同的核空间,然后将映射过程中确定的核矩阵和核空间信息素融合判别标签并根据指数线性核函数理论对度量空间进行重构,并进一步的利用重构后的度量矩阵确定另一个核空间信息素,依据该核空间信息素构建核矩阵,并通过核矩阵中表示的特征信息进行降维处理,最后将降维处理后的结果和标签判别信息一起输入到预训练的机器分类器中,得到分类结果,可以减少数据在提取过程中的丢失,从而提高分类器进行故障诊断的准确性。By determining the true neighbor graph based on the angle information and distance information of the vibration signal, and using the shortest path algorithm to calculate the true neighbor graph, the geodesic distance matrix and the metric information represented by the geodesic distance are obtained. Through the true neighbor graph Determining the metric information represented by the geodesic distance matrix and the geodesic distance matrix can reduce the influence of hyperparameters on the machine algorithm model; and by mapping the metric information expressed by the geodesic distance matrix and the geodesic distance matrix to different kernel spaces, and then The kernel matrix determined in the mapping process and the nuclear space pheromone are fused to distinguish labels, and the metric space is reconstructed according to the exponential linear kernel function theory, and the reconstructed metric matrix is further used to determine another nuclear space pheromone. Spatial pheromones construct a kernel matrix, and perform dimensionality reduction processing through the feature information represented in the kernel matrix, and finally input the result of dimensionality reduction processing and label discrimination information into the pre-trained machine classifier to obtain classification results, which can reduce The data is lost during the extraction process, thereby improving the accuracy of the classifier for fault diagnosis.

以下对于步骤S110至步骤S160中所涉及的技术方案进行详细解释。The technical solutions involved in steps S110 to S160 are explained in detail below.

在本公开一示例实施例中,可以通过以下步骤并参考图3实现对步骤S110中真近邻点近邻图的确定:In an exemplary embodiment of the present disclosure, the determination of the neighbor map of true neighbors in step S110 can be realized by the following steps with reference to FIG. 3 :

步骤S310,获取机械装备振动信号的样本点和初近邻点,初近邻点由样本点设置的近邻值确定;步骤S320,根据样本点与初近邻点之间的距离,确定真近邻点数和初近邻点数;步骤S330,根据真近邻点数和初近邻点数构建真近邻点近邻图。Step S310, obtain the sample point and the initial neighbor point of the mechanical equipment vibration signal, the initial neighbor point is determined by the neighbor value set by the sample point; Step S320, determine the number of true neighbor points and the initial neighbor point according to the distance between the sample point and the initial neighbor point Number of points; step S330 , constructing a true neighbor point neighbor graph according to the true neighbor point number and the initial neighbor point number.

其中,机械装备振动信号的样本点是指从振动信号中选取的一组数据点,这些数据点包含振动信号在时间或频率域上的角度特征信息和距离特征信息,用于对振动信号进行特征提取以及故障检测和诊断。样本点可以选取足够量的数据点,例如,样本点可以选取能充分代表整个振动信号特征并且在时间或频率域上均匀分布的数据点,样本点也可以根据振动信号的处理方法和应用场景来选取,本示例实施例对于样本点的选取不做特别限定。Among them, the sample points of vibration signals of mechanical equipment refer to a group of data points selected from the vibration signals. extraction and fault detection and diagnosis. A sufficient number of data points can be selected for the sample point. For example, the sample point can be selected to fully represent the characteristics of the entire vibration signal and uniformly distributed in the time or frequency domain. The sample point can also be selected according to the vibration signal processing method and application scenario. Selection, this example embodiment does not specifically limit the selection of sample points.

初近邻点是指与样本点有一定距离的数据点,用于选取样本点附近的合适的数据点来构建初近邻图,可以通过预设近邻值K来确定初近邻点,也可以通过网格划分确定初近邻点,当然,还可以通过设置一个固定范围,将离样本点在固定范围内的数据点作为初近邻点,初近邻点的选取方法可以根据需求来选择,本示例实施例对于初近邻点的选择不做特别限定。The initial neighbor point refers to the data point with a certain distance from the sample point. It is used to select the appropriate data point near the sample point to construct the initial neighbor graph. The initial neighbor point can be determined by preset neighbor value K, or through the grid Determining the initial neighbor points by dividing, of course, by setting a fixed range, the data points within the fixed range from the sample point can be used as the initial neighbor points. The selection method of the initial neighbor points can be selected according to the requirements. The selection of the neighbor points is not particularly limited.

可选的,通过预设近邻值K确定初近邻点,其中,通过预设的近邻值K确定每个样本点的近邻点并定义为初近邻点。举例而言,近邻值K设为5,则根据最短路径算法计算得到数据离样本点的最短距离,并选取5个离样本点距离较近的数据点构成近邻点,也就是初近邻点。Optionally, the initial neighbor point is determined by a preset neighbor value K, wherein the neighbor point of each sample point is determined by the preset neighbor value K and defined as the initial neighbor point. For example, if the neighbor value K is set to 5, then the shortest distance between the data and the sample point is calculated according to the shortest path algorithm, and 5 data points that are closer to the sample point are selected to form the neighbor point, that is, the initial neighbor point.

可选的,在构建真近邻点近邻图时需要对初近邻点进行预处理,得到欧几里得距离矩阵,并且对欧几里得距离矩阵进行归一化处理,用于消除不同样本点之间距离尺度上的差异,确保各个样本点之间的距离具有可比性,从而提高这些算法的性能和准确性,降低数据出现欠拟合的情况;并且在某些情况下,不同特征对距离计算的贡献可能是不均衡的,通过归一化处理距离矩阵,可以平衡不同特征之间的权重,避免某些特征对欧几里德距离的计算影响过大。Optionally, when constructing the neighbor map of true neighbor points, it is necessary to preprocess the initial neighbor points to obtain the Euclidean distance matrix, and normalize the Euclidean distance matrix to eliminate the difference between different sample points. The difference on the distance scale ensures that the distance between each sample point is comparable, thereby improving the performance and accuracy of these algorithms and reducing the underfitting of the data; and in some cases, different features have a significant impact on the distance calculation. The contribution of may be unbalanced. By normalizing the distance matrix, the weights between different features can be balanced, and certain features can be prevented from having too much influence on the calculation of Euclidean distance.

其中,欧几里得距离矩阵是指衡量初近邻点和样本点之间的直线距离的矩阵,用于表示初近邻点和样本点之间的欧几里得距离,对于高维空间的数据点,欧几里得距离矩阵的计算方法有类似,都是将坐标差的平方和开方的计算应用到所有维度上。举例而言,可以先根据确定的初近邻点构造初近邻图G;再计算初近邻余弦相似度矩阵,其中:/>,/>表示第i个样本点的第k个维度特征,表示第i个样本点的初近邻点集;然后,再构造近邻余弦相似度调整矩阵/>,其中/>,最后计算初近邻点与中心点间的欧几里德距离矩阵,其中,/>Among them, the Euclidean distance matrix refers to the matrix that measures the straight-line distance between the initial neighbor point and the sample point, and is used to represent the Euclidean distance between the initial neighbor point and the sample point. For data points in high-dimensional space , the calculation method of the Euclidean distance matrix is similar, and the calculation of the square and root of the coordinate difference is applied to all dimensions. For example, the initial neighbor graph G can be constructed based on the determined initial neighbor points; then the initial neighbor cosine similarity matrix can be calculated , where: /> , /> Represents the k-th dimension feature of the i-th sample point, Represents the initial neighbor point set of the i-th sample point; then, construct the neighbor cosine similarity adjustment matrix/> , where /> , and finally calculate the Euclidean distance matrix between the initial neighbor point and the center point , where /> .

归一化处理是指是将数据按比例缩放,使被处理的数据缩放在特定的范围或相对大小内,在数据处理和机器学习中,归一化通常指将数据转换为0到1之间的范围,也称为最小-最大缩放(Min-Max Scaling),用于消除特征之间的差异。当然还可以通过其他的方法进行归一化处理,例如,可以通过Z-score标准化的方法进行归一化处理,也可以通过正态分布进行归一化处理,本示例实施例对归一化处理的具体方法不做特别限定。Normalization processing refers to scaling the data so that the processed data is scaled within a specific range or relative size. In data processing and machine learning, normalization usually refers to converting data between 0 and 1 The range of , also known as Min-Max Scaling, is used to eliminate differences between features. Of course, normalization processing can also be performed by other methods, for example, normalization processing can be performed by Z-score normalization method, or normalization processing can be performed by normal distribution. The specific method is not particularly limited.

举例而言,通过最小-最大缩放的方法对全部样本点的初近邻点欧几里德距离矩阵进行归一化处理,得到N个近邻点归一化欧式距离矩阵:其中,将欧几里德距离矩阵中的数据转换为0到1之间的范围。For example, the initial neighbor Euclidean distance matrix of all sample points is normalized by the minimum-maximum scaling method to obtain the normalized Euclidean distance matrix of N nearest neighbors :in , which converts the data in the Euclidean distance matrix to a range between 0 and 1.

真近邻点是指样本点的每个权值与样本点的平均权值进行比较得到的样本点的权值小于或者等于样本点平均权值的样本点,用于确定真近邻点的个数,从而根据真近邻点的数量构建真近邻点近邻图。举例而言,可以通过构造全部样本点与其初近邻点间的权值矩阵:,其中/>;再计算全部样本点与其初近邻点集的平均权值/>,其中/>;然后将样本点xi的每个权值/>与该样本点的平均权值/>进行比较,若/>则认为xj为xi的伪近邻点,若/>则认为xj为xi的真近邻点;根据每个样本点对应的真近邻点的个数,动态调整样本点的近邻点数,得到动态的真近邻值K+,构建动态的真近邻点近邻图G+The true neighbor point refers to the sample point whose weight value is less than or equal to the average weight value of the sample point obtained by comparing each weight value of the sample point with the average weight value of the sample point, and is used to determine the number of true neighbor points. According to the number of true neighbors, the neighbor graph of true neighbors is constructed. For example, it is possible to construct a weight matrix between all sample points and their initial neighbors: , where /> ; Then calculate the average weight of all sample points and their initial neighbor point set /> , where /> ; Then each weight of the sample point x i /> and the average weight of the sample point /> for comparison, if /> Then it is considered that x j is the pseudo neighbor point of x i , if /> Then it is considered that x j is the true neighbor point of x i ; according to the number of true neighbor points corresponding to each sample point, dynamically adjust the number of neighbor points of the sample point, obtain the dynamic true neighbor value K + , and construct the dynamic true neighbor point neighbor Figure G + .

在本公开一示例实施例中,可以通过以下步骤实现对步骤S140中将距离信息表示的不同信息映射到不同的核空间中:In an exemplary embodiment of the present disclosure, the following steps may be implemented to map different information represented by the distance information in step S140 into different kernel spaces:

计算距离信息之间的相似度以及内积,并根据相似度以及内积将距离信息表示的不同信息映射到不同的核空间中。Calculate the similarity and inner product between the distance information, and map different information represented by the distance information into different kernel spaces according to the similarity and inner product.

可选的,将距离信息映射到不同的核空间有多种方式,例如可以通过双中心化的方法将测地距离表示的度量信息映射到核空间中;也可以通过核函数映射方法将测地距离矩阵映射到另一个核空间中;当然,还可以通过特征值提取的方法将距离信息映射到核空间中,本示例实施例对于将距离信息映射到核空间中的方法不做特别限定。Optionally, there are many ways to map the distance information to different kernel spaces. For example, the metric information represented by the geodesic distance can be mapped to the kernel space through the method of dual centering; the geodesic distance can also be mapped to the kernel space through the kernel function mapping method. The distance matrix is mapped to another kernel space; of course, the distance information can also be mapped to the kernel space by means of eigenvalue extraction, and the method of mapping the distance information to the kernel space is not particularly limited in this example embodiment.

举例而言,可以基于指数线性核函数理论确定指数线性核函数,其中,指数线性核函数是一种核函数,用于将输入空间中的数据映射到高维特征空间中,可以衡量两个样本点之间的相似度,并且由指数线性核函数确定的核矩阵具有半正定性质,核矩阵的半正定性质可以保证机器学习算法的稳定性和正确性,由指数线性核函数确定的核矩阵具有半正定性质可以利用测地距离信息以及核函数的充要条件证明。举例而言,根据测地距离信息以及指数线性核函数理论确定指数线性核函数矩阵,其中,/>为线性权重,b(0<b≤1)为偏移系数,/>为样本xi与xj之间的测地距离;并根据核函数的充要条件:对于任何数据样本,其相应的Gram矩阵/>是半正定矩阵,N为样本数量。对指数线性核函数矩阵半正定性质的证明如下:For example, the exponential linear kernel function can be determined based on the exponential linear kernel function theory, where the exponential linear kernel function is a kernel function used to map the data in the input space to a high-dimensional feature space, which can measure two samples The similarity between points, and the kernel matrix determined by the exponential linear kernel function has a positive semi-definite property. The semi-positive definite property of the kernel matrix can ensure the stability and correctness of the machine learning algorithm. The kernel matrix determined by the exponential linear kernel function has The positive semi-definite property can be proved by using the geodesic distance information and the necessary and sufficient conditions of the kernel function. For example, the exponential linear kernel function matrix is determined according to the geodesic distance information and the exponential linear kernel function theory , where /> is the linear weight, b (0<b≤1) is the offset coefficient, /> is the geodesic distance between samples x i and x j ; and according to the necessary and sufficient conditions of the kernel function: for any data sample , and its corresponding Gram matrix /> is a positive semi-definite matrix, and N is the number of samples. The proof of the positive semi-definite property of the exponential linear kernel function matrix is as follows:

指数线性核函数结构可知其利用测地矩阵信息,则显然矩阵为对称矩阵,因此只需证明/>为半正定矩阵;推导指数线性函数的映射:若样本xj是xi的近邻点,则有/>,则其中映射/>,由此可知当样本xj是xi的近邻点时指数线性函数满足核函数充要条件。The structure of the exponential linear kernel function shows that it utilizes the information of the geodesic matrix, then the matrix is a symmetric matrix, so it is only necessary to prove that /> is a positive semidefinite matrix; derive the mapping for exponential linear functions: If the sample x j is the neighbor point of x i , then there is /> ,but which maps /> , it can be seen that when the sample x j is the neighbor point of x i , the exponential linear function satisfies the necessary and sufficient conditions of the kernel function.

若样本xj不是xi的近邻点,则有,/>为有限项dE的和,故不妨令/>有z项dE且/>,则有:/>If the sample x j is not the neighbor point of x i , then there is , /> is the sum of finite terms d E , so let /> There are z items d E and /> , then there is: /> ,

由此可知当样本xj不是xi的近邻点时指数线性函数也满足核函数充要条件。It can be seen that when the sample x j is not the neighbor point of x i , the exponential linear function also satisfies the necessary and sufficient conditions of the kernel function.

根据上述证明结果可知对称矩阵具有半正定性质,满足核函数的充要条件,因此由指数线性核函数确定的核矩阵具有半正定性质。According to the above proof results, we know that the symmetric matrix It has a positive semi-definite property and satisfies the necessary and sufficient conditions of the kernel function, so the kernel matrix determined by the exponential linear kernel function has a positive semi-definite property.

其中,利用指数线性核函数计算距离信息之间的相似度可以是使用指数线性核函数计算两个样本点x和y之间的相似度,这可以通过计算样本点之间的欧几里得距离,然后将欧几里得距离作为指数线性核函数的指数来实现,从而使较小的距离产生较大的相似度值,较大的距离产生较小的相似度值;利用指数线性核函数计算距离信息之间的内积可以使用指数线性核函数的定义来计算。Among them, using the exponential linear kernel function to calculate the similarity between distance information can be to use the exponential linear kernel function to calculate the similarity between two sample points x and y, which can be calculated by calculating the Euclidean distance between the sample points , and then implement the Euclidean distance as the exponent of the exponential linear kernel function, so that a smaller distance produces a larger similarity value, and a larger distance produces a smaller similarity value; using the exponential linear kernel function to calculate The inner product between distance information can be calculated using the definition of exponential linear kernel function.

通过计算距离信息之间的相似度和内积,可以得到样本点之间的相似度值以及在核空间中的内积值。这些相似度值和核空间的内积值可以用于特征提取,将样本点的距离信息映射到核空间中。通过指数线性核函数计算距离信息之间的相似度和内积,并将不同的距离信息映射到不同的核空间中,提供了一种有效的方式来处理非线性可分的样本点数据,并在预训练机器学习任务中发挥重要作用。By calculating the similarity and inner product between distance information, the similarity value between sample points and the inner product value in the kernel space can be obtained. These similarity values and the inner product value of the kernel space can be used for feature extraction, and the distance information of the sample points is mapped to the kernel space. Calculate the similarity and inner product between distance information through exponential linear kernel function, and map different distance information into different kernel spaces, which provides an effective way to deal with non-linearly separable sample point data, and Play an important role in pre-training machine learning tasks.

在本公开一示例实施例中,可以通过以下步骤并参考图4实现将测地距离表示的度量信息映射到第一核空间中:In an exemplary embodiment of the present disclosure, the mapping of the metric information represented by the geodesic distance into the first kernel space may be implemented by referring to FIG. 4 through the following steps:

步骤S410,根据真近邻点近邻图确定测地距离矩阵和平方测地距离矩阵;步骤S420,获取预先设置好的固定核矩阵;步骤S430,根据双中心化方法在所述平方测地距离矩阵的两侧分别与所述固定核矩阵进行乘法运算,将测地距离表示的度量信息映射到第一核空间中。Step S410, determine the geodesic distance matrix and the square geodesic distance matrix according to the true neighbor point neighbor graph; step S420, obtain the preset fixed kernel matrix; The two sides are respectively multiplied by the fixed kernel matrix, and the metric information represented by the geodesic distance is mapped into the first kernel space.

其中,测地距离矩阵是指基于真近邻点近邻图来确定的一种距离矩阵,用于衡量数据点之间的相似性和差异性。测地距离矩阵可以基于真近邻点近邻图利用最短路径算法确定,例如,可以通过Dijkstra算法,或者Floyd算法计算真近邻点近邻图中每个数据点之间的测地距离,然后根据确定的测地距离确定测地距离矩阵,可以根据需求选择合适的算法确定测地距离矩阵本示例实施例对测地距离矩阵的确定算法不做特别限定。Among them, the geodesic distance matrix refers to a distance matrix determined based on the true nearest neighbor graph, which is used to measure the similarity and difference between data points. The geodesic distance matrix can be determined using the shortest path algorithm based on the true nearest neighbor graph, for example, the Dijkstra algorithm or the Floyd algorithm can be used to calculate the geodesic distance between each data point in the true neighbor graph, and then according to the determined geodesic distance The geodesic distance matrix is determined by the geodesic distance, and an appropriate algorithm may be selected according to requirements to determine the geodesic distance matrix. In this exemplary embodiment, the algorithm for determining the geodesic distance matrix is not particularly limited.

测地距离表示的度量信息是指测地距离矩阵中表示的测地距离,用于度量数据点之间的最短路径的距离。测地距离表示的度量信息即为测地距离矩阵在跟据最短路径算法计算得到的测地距离,本示例实施例对于测定矩阵表示的度量信息的确定方法不再赘述。The metric information represented by the geodesic distance refers to the geodesic distance represented in the geodesic distance matrix, which is used to measure the distance of the shortest path between data points. The metric information represented by the geodesic distance is the geodesic distance calculated by the geodesic distance matrix according to the shortest path algorithm, and the method for determining the metric information represented by the measurement matrix will not be repeated in this example embodiment.

固定核矩阵是指用于对数据进行非线性映射,并将数据映射到核空间中的矩阵。固定核矩阵将数据映射到核空间中是通过将平方测地距离矩阵的两侧与固定核矩阵进行乘法运算实现的,固定核矩阵可以通过自定义的核函数确定,举例而言,根据指数线性核函数选择适当的核宽度参数σ,计算每对样本之间的核值并将它们组成一个核矩阵,从而确定固定核矩阵。当然还可以根据其他的核函数来确定固定核矩阵,例如高斯核或者多项式核,本示例实施例对于核矩阵的确定不做特别限定。The fixed kernel matrix refers to the matrix used to nonlinearly map the data and map the data into the kernel space. The fixed kernel matrix maps the data into the kernel space by multiplying both sides of the square geodesic distance matrix with the fixed kernel matrix, which can be determined by a custom kernel function, for example, according to the exponential linear The kernel function selects the appropriate kernel width parameter σ, calculates the kernel values between each pair of samples and composes them into a kernel matrix, thus determining the fixed kernel matrix. Certainly, the fixed kernel matrix may also be determined according to other kernel functions, such as a Gaussian kernel or a polynomial kernel, and the determination of the kernel matrix is not particularly limited in this example embodiment.

第一核空间核矩阵是指根据测地距离矩阵和平方测地距离矩阵计算得到的表示测地距离的矩阵,用于将测地距离矩阵中表示的度量信息的映射到此第一核空间中。其中,第一核空间核矩阵可以由测地距离矩阵确定,举例而言,可以通过自适应近邻策略获得真近邻点近邻图G+,计算得到测地距离矩阵DG和平方测地距离矩阵,同时计算矩阵操作算子/>即第一核空间核矩阵。The first kernel space kernel matrix refers to the matrix representing the geodesic distance calculated according to the geodesic distance matrix and the square geodesic distance matrix, which is used to map the metric information represented in the geodesic distance matrix to the first kernel space . Among them, the kernel matrix of the first kernel space can be determined by the geodesic distance matrix. For example, the true neighbor point neighbor graph G + can be obtained through the adaptive neighbor strategy, and the geodesic distance matrix D G and the squared geodesic distance matrix can be obtained by calculation , while computing the matrix operator /> That is, the first kernel space kernel matrix.

将测地距离表示的度量信息映射到第一核空间中可以根据测地距离矩阵DG计算得到平方测地距离矩阵,其中平方测地矩阵为然后通过双中心化的方法在平方测地距离矩阵的左右分别与H矩阵相乘,即/>,通过矩阵之间的运算,将测地矩阵表示的度量信息映射到了第一核空间中。其中H矩阵在空间的映射过程中是一个已知固定的核矩阵,本公开不再详细说明。Mapping the metric information expressed by the geodesic distance into the first kernel space can calculate the square geodesic distance matrix according to the geodesic distance matrix D G , where the square geodesic matrix is Then multiply the left and right sides of the square geodesic distance matrix with the H matrix by the method of double centering, that is, /> , through the operation between the matrices, the metric information represented by the geodesic matrix is mapped to the first kernel space. The H matrix is a known and fixed kernel matrix in the space mapping process, which will not be described in detail in this disclosure.

在本公开一示例实施例中,可以通过以下步骤并参考图4实现将测地距离矩阵映射到第二核空间中:In an example embodiment of the present disclosure, the mapping of the geodesic distance matrix into the second kernel space may be implemented by referring to FIG. 4 through the following steps:

步骤S440,将测地距离矩阵中的元素设置为指数线性核函数矩阵的元素的测地距离信息,将测地距离矩阵映射到第二核空间中。Step S440, setting the elements in the geodesic distance matrix as the geodesic distance information of the elements of the exponential linear kernel function matrix, and mapping the geodesic distance matrix into the second kernel space.

可选的,通过指数线性核函数将测地距离矩阵映射到核空间中,其中,指数线性核函数是核函数的一种,并具有半正定性质,指数线性核函数的半正定性质可以保证数据点在映射过程的稳定性。可以通过先计算得到测地距离矩阵,再将测地距离矩阵中的每一个元素映射到指数线性核函数中,再根据指数线性核函数确定第二核空间,即将测地距离矩阵中的每一个元素映射到第二核空间中。Optionally, the geodesic distance matrix is mapped to the kernel space by an exponential linear kernel function, wherein the exponential linear kernel function is a kind of kernel function and has a positive semi-definite property. The semi-positive definite property of the exponential linear kernel function can ensure that the data points in the stability of the mapping process. The geodesic distance matrix can be obtained by first calculating, and then each element in the geodesic distance matrix is mapped to the exponential linear kernel function, and then the second kernel space is determined according to the exponential linear kernel function, that is, each element in the geodesic distance matrix Elements are mapped into the second kernel space.

举例而言,指数线性核函数矩阵为,并且,将测地距离矩阵DG中的元素/>代入到指数线性核函数矩阵中的元素中,从而将测地距离矩阵DG通过指数线性核函数映射到第二核空间中,完成特征提取,并得到第二核空间核矩阵,即/>For example, the exponential linear kernel function matrix is ,and , the elements in the geodesic distance matrix D G /> Substitute into the elements in the exponential linear kernel function matrix, so that the geodesic distance matrix D G is mapped to the second kernel space through the exponential linear kernel function, and the feature extraction is completed, and the second kernel space kernel matrix is obtained, that is, .

根据指数线性核函数进行非线性映射,可以捕捉到样本之间的非线性关系,并在第二核空间中进行更有效的特征表示和学习,可以在保留关键信息的同时减少特征的维度,提高特征的表达能力,有助于更好地进行机器学习任务。Non-linear mapping according to the exponential linear kernel function can capture the nonlinear relationship between samples, and perform more effective feature representation and learning in the second kernel space, which can reduce the dimension of features while retaining key information, and improve The expressive ability of features helps to better perform machine learning tasks.

在本公开一示例实施例中,可以通过以下步骤实现通过第一核空间信息素确定第一核空间中的特征表示:In an exemplary embodiment of the present disclosure, the following steps may be used to determine the feature representation in the first kernel space through the first kernel space pheromone:

根据测地距离表示的度量信息确定第一核空间矩阵,再根据第一核空间矩阵设置第一核空间矩阵谱半径,并通过第一核空间矩阵谱半径确定第一核空间信息素,再根据第一核空间信息素确定第一核空间中的特征表示。Determine the first kernel space matrix according to the metric information represented by the geodesic distance, then set the spectral radius of the first kernel space matrix according to the first kernel space matrix, and determine the first kernel space pheromone through the spectrum radius of the first kernel space matrix, and then according to The first kernel space pheromone determines a feature representation in the first kernel space.

第一核空间信息素是指表示第一核空间中特征的数据,用于对度量矩阵的重构,第一核空间信息素由第一核空间核矩阵确定,举例而言,可以通过第一核Hilbert空间核矩阵构造分块矩阵/>并计算第一核空间矩阵谱半径/>,并且令第一核Hilbert空间信息素/>,确定第一核空间信息素。The first nuclear space pheromone refers to the data representing the characteristics in the first nuclear space, which is used to reconstruct the metric matrix. The first nuclear space pheromone is determined by the first nuclear space kernel matrix. For example, it can be obtained through the first kernel Hilbert space kernel matrix Construct block matrix /> and calculate the first kernel space matrix spectral radius /> , and let the first kernel Hilbert space pheromone /> , to determine the first nuclear spatial pheromone.

通过第一核空间信息素确定第一核空间中的特征表示,可以简化在后续运算中对第一核空间中的特征进行操作的方式,从而在提高运算效率的同时保证了数据的准确性。Determining the feature representation in the first kernel space by using the first kernel space pheromone can simplify the way of operating the features in the first kernel space in subsequent operations, thereby ensuring data accuracy while improving operation efficiency.

在本公开一示例实施例中,可以通过以下步骤实现对步骤150中根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构:In an exemplary embodiment of the present disclosure, the reconstruction of the metric matrix in step 150 according to the fusion label discrimination information of the kernel matrix and the kernel space pheromone can be realized through the following steps:

通过核矩阵以及核空间信息素对度量矩阵进行非线性运算,对度量矩阵进行重构。The measurement matrix is reconstructed by performing nonlinear operation on the measurement matrix through the kernel matrix and the nuclear space pheromone.

其中,对度量矩阵进行重构可以采用线性修正的方法,也可以采用非线性修正的方法,采用线性修正的方法可以是通过线性变换来重构度量矩阵,例如,主成分分析(PCA)和线性判别分析(LDA);采用非线性修正的方法可以是采用非线性函数来重构度量矩阵,例如指数线性核函数,采用指数线性核函数对度量矩阵进行重构,可以将提取的特征映射到一个高维的特征空间中,然后在高维的 特征空间中进行线性修正;当然还可以采用流行学习的非线性修正方法,利用样本的局部几何结构来重构度量矩阵,能更好地保留数据的流形结构,本示例实施例对于对度量矩阵进行重构的方法不做特别限定。Among them, the reconstruction of the metric matrix can adopt the method of linear correction or non-linear correction. The method of linear correction can be to reconstruct the metric matrix through linear transformation, for example, principal component analysis (PCA) and linear Discriminant analysis (LDA); the method of using nonlinear correction can be to use a nonlinear function to reconstruct the measurement matrix, such as an exponential linear kernel function, using an exponential linear kernel function to reconstruct the measurement matrix, and the extracted features can be mapped to a In the high-dimensional feature space, and then perform linear correction in the high-dimensional feature space; of course, the nonlinear correction method of popular learning can also be used to reconstruct the metric matrix by using the local geometric structure of the sample, which can better retain the data. Manifold structure, the method for reconstructing the metric matrix is not particularly limited in this example embodiment.

对度量矩阵进行重构可以提高数据的表征能力、降低维度、增强分类性能、便于数据可视化和关联分析等,从而对机器学习任务有很大的帮助。Reconstructing the measurement matrix can improve the representation ability of data, reduce dimensionality, enhance classification performance, facilitate data visualization and association analysis, etc., which is of great help to machine learning tasks.

在本公开一示例实施例中,可以通过以下步骤实现对度量矩阵进行非线性修正,从而对度量矩阵进行重构:In an exemplary embodiment of the present disclosure, the non-linear correction to the metric matrix may be implemented through the following steps, so as to reconstruct the metric matrix:

通过对度量矩阵进行非线性运算,并融和标签判别信息、第一核空间信息素、第二核空间核矩阵和指数线性核函数理论对度量矩阵进行非线性修正,确定重构后的度量矩阵。The reconstructed metric matrix is determined by performing nonlinear operation on the metric matrix and integrating the label discriminant information, the first kernel space pheromone, the second kernel space kernel matrix and the exponential linear kernel function theory to modify the metric matrix.

对度量矩阵进行重构可以通过第一核空间信息素、标签判别信息、第二核空间核矩阵以及指数线性核函数理论对度量矩阵进行非线性运算,通过在特征提取过程中得到的部分矩阵或者数据改变度量矩阵中的参数对度量矩阵进行非线性修正,从而实现对度量矩阵的重构。举例而言,其中,度量矩阵的表达式为:,通过融合标签判别信息、第二核Hilbert空间核矩阵和指数线性核函数理论对度量矩阵进行非线性修正:其中:/>为偏距系数。Reconstructing the metric matrix can perform nonlinear operations on the metric matrix through the first kernel space pheromone, label discrimination information, second kernel space kernel matrix and exponential linear kernel function theory, and through the partial matrix obtained in the feature extraction process or The data changes the parameters in the measurement matrix to perform nonlinear correction on the measurement matrix, so as to realize the reconstruction of the measurement matrix. For example, where the expression of the metric matrix is: , the metric matrix is nonlinearly corrected by fusing the label discriminant information, the second kernel Hilbert space kernel matrix and the exponential linear kernel function theory: where: /> is the offset coefficient.

在本公开一示例实施例中,可以通过以下步骤实现将第一核空间信息素融合进第二核空间核矩阵中:In an exemplary embodiment of the present disclosure, the fusion of the first nuclear space pheromone into the second nuclear space kernel matrix can be achieved through the following steps:

其中,线性权重是指基于指数线性核函数理论确定指数线性核函数矩阵的一个参数,用于构造指数线性核函数矩阵。指数线性核函数矩阵的表达式为:,指数线性核函数矩阵中的每一项数据为即为线性权重,可以通过改变线性权重的值改变指数线性核函数矩阵中的特征。Wherein, the linear weight refers to a parameter of the exponential linear kernel function matrix determined based on the exponential linear kernel function theory, and is used to construct the exponential linear kernel function matrix. The expression of the exponential linear kernel function matrix is: , each item of data in the exponential linear kernel function matrix is It is a linear weight, and the features in the exponential linear kernel function matrix can be changed by changing the value of the linear weight.

可选的,令指数线性核函数理论中的线性权重,可以将第一核Hilbert空间信息素融合进指数线性核函数矩阵中,并且第二核空间核矩阵由指数线性核函数矩阵确定,因此通过将指数线性核函数中的线性权重设置为第一核空间信息素,可以将第一核空间的特征融合到第二核空间核矩阵中,完成不同高维空间中的特征的融合。Optionally, let the linear weights in exponential linear kernel function theory , the first kernel Hilbert space pheromone can be fused into the exponential linear kernel function matrix, and the second kernel space kernel matrix is determined by the exponential linear kernel function matrix, so by setting the linear weight in the exponential linear kernel function to the first kernel Spatial pheromones can fuse the features of the first kernel space into the kernel matrix of the second kernel space to complete the fusion of features in different high-dimensional spaces.

在本公开一示例实施例中,可以通过以下步骤构造Mercer核矩阵:In an exemplary embodiment of the present disclosure, the Mercer kernel matrix can be constructed through the following steps:

根据重构的度量矩阵,确定第二核空间矩阵谱半径,并根据第二核空间矩阵谱半径确定第二核空间信息素,再根据重构的度量矩阵与第二核空间信息素构造Mercer核矩阵。According to the reconstructed metric matrix, determine the spectral radius of the second nuclear space matrix, and determine the second nuclear space pheromone according to the spectral radius of the second nuclear space matrix, and then construct the Mercer kernel according to the reconstructed metric matrix and the second nuclear space pheromone matrix.

第二核空间信息素是指代表重构后度量矩阵中的特征的数据,用于构造Mercer核矩阵,第二核空间信息素由重构后的度量矩阵确定,举例而言,利用度量矩阵非线性修正矩阵再次构造分块矩阵,并计算第二核空间矩阵谱半径,/>,令第二核Hilbert空间信息素/>The second nuclear spatial pheromone refers to the data representing the features in the reconstructed metric matrix, which is used to construct the Mercer kernel matrix, and the second nuclear spatial pheromone is determined by the reconstructed metric matrix. For example, using the metric matrix The linear correction matrix again constructs the block matrix , and calculate the second kernel space matrix spectral radius, /> , let the second kernel Hilbert space pheromone /> .

Mercer核矩阵是指具有对称正定性质的核矩阵,用于方便对重构后的度量矩阵中的特征进行降维。Mercer核矩阵可以用于各种预训练机器学习任务,例如Mercer核矩阵可以支持向量机(SVM)分类器学习,还可以支持核主成分分析(Kernel PCA),本示例实施例对于Mercer核矩阵用于学习的预训练机器不做特别限定。The Mercer kernel matrix refers to a kernel matrix with symmetric positive definite properties, which is used to facilitate dimensionality reduction of the features in the reconstructed metric matrix. The Mercer kernel matrix can be used for various pre-training machine learning tasks. For example, the Mercer kernel matrix can support vector machine (SVM) classifier learning, and can also support kernel principal component analysis (Kernel PCA). This example embodiment is used for the Mercer kernel matrix The pre-training machine for learning is not particularly limited.

Mercer核矩阵可以根据第二核Hilbert空间信息素、以及重构的平方度量矩阵/>和重构的度量矩阵通过双中心化方法映射的对应的核空间来构造,具体的构造方法为:/>The Mercer kernel matrix can be based on the second kernel Hilbert space pheromone , and the reconstructed squared metric matrix /> And the corresponding kernel space mapped by the reconstructed metric matrix by the dual center method To construct, the specific construction method is: /> .

通过利用重构后的度量矩阵确定第二核空间信息素,并构造Mercer核矩阵,By using the reconstructed metric matrix to determine the second kernel spatial pheromone and constructing the Mercer kernel matrix,

可以避免在高维特征空间中进行显式计算,从而降低计算复杂度,提高计算的准确率。It can avoid explicit calculation in high-dimensional feature space, thereby reducing computational complexity and improving calculation accuracy.

在本公开一示例实施例中,可以通过以下步骤对重构后的度量矩阵的特征进行降维处理,并将处理的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果:In an exemplary embodiment of the present disclosure, the following steps can be used to perform dimensionality reduction processing on the features of the reconstructed metric matrix, and input the processing result and label discrimination information into the pre-trained fault classifier to obtain the fault classification result :

将Mercer核矩阵中的特征进行特征值分解,得到Mercer核矩阵的协方差矩阵,再根据协方差矩阵确定Mercer核矩阵中对应的特征值和特征向量,并根据特征值和特征向量对Mercer核矩阵的特征进行降维,得到Mercer核矩阵所对应的低维空间嵌入向量,然后根据低维空间嵌入向量与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。The features in the Mercer kernel matrix are decomposed into eigenvalues to obtain the covariance matrix of the Mercer kernel matrix, and then the corresponding eigenvalues and eigenvectors in the Mercer kernel matrix are determined according to the covariance matrix, and the Mercer kernel matrix is calculated according to the eigenvalues and eigenvectors Dimensionality reduction is performed on the features of the Mercer kernel matrix to obtain the low-dimensional space embedding vector corresponding to the Mercer kernel matrix, and then the low-dimensional space embedding vector and label discrimination information are input into the pre-trained fault classifier to obtain the fault classification result.

其中,将Mercer核矩阵中的特征进行特征值分解的方法是现有技术,在此不再赘述,可以通过特征值分解方法获得的低维空间嵌入坐标/>,完成特征提取的降维过程。Among them, the method of performing eigenvalue decomposition of the features in the Mercer kernel matrix is an existing technology, and will not be repeated here, and can be obtained by the eigenvalue decomposition method The low-dimensional space embedding coordinates of /> , to complete the dimensionality reduction process of feature extraction.

通过对Mercer核矩阵的特征进行降维,并将对Mercer核矩阵降维得到的低维空间嵌入向量与标签判别信息输入到预训练的故障分类器中,最终得到故障分类结果,可以帮助技术人员快速准确地找到故障原因,能及时的采取相应的措施来解决问题,并且通过预训练的故障分类器进行故障诊断可以提高故障诊断的效率和准确性。By reducing the dimensionality of the features of the Mercer kernel matrix, and inputting the low-dimensional space embedding vector and label discrimination information obtained by reducing the dimensionality of the Mercer kernel matrix into the pre-trained fault classifier, the fault classification result is finally obtained, which can help technicians Find the cause of the fault quickly and accurately, and take corresponding measures in time to solve the problem, and the fault diagnosis through the pre-trained fault classifier can improve the efficiency and accuracy of fault diagnosis.

图2示意性示出了根据本公开的一些实施例对机械装备进行故障诊断的流程图。Fig. 2 schematically shows a flow chart of performing fault diagnosis on mechanical equipment according to some embodiments of the present disclosure.

参考图2所示,可以通过步骤S201至步骤S207实现对机械装备进行故障诊断,其中:Referring to Fig. 2, the fault diagnosis of mechanical equipment can be realized through steps S201 to S207, wherein:

步骤S201,获取振动信号的角度信息和距离信息;Step S201, acquiring angle information and distance information of the vibration signal;

步骤S202,根据角度信息和距离信息得到真近邻点近邻图;其中,通过设置初近邻值确定样本点的初近邻点,并且通过将样本点和初近邻点之间距离的权值和平均权值进行比较,得到真近邻点和伪近邻点,并根据确定的样本点周围的真近邻点构建真近邻点近邻图,并且真近邻点近邻图是可以根据真近邻点的数量进行动态变化的。Step S202, according to the angle information and the distance information to obtain the neighbor map of the true neighbor points; wherein, the initial neighbor point of the sample point is determined by setting the initial neighbor value, and the weight and the average weight of the distance between the sample point and the initial neighbor point Compare to get true neighbors and false neighbors, and build a true neighbor graph based on the real neighbors around the determined sample points, and the true neighbor graph can be dynamically changed according to the number of true neighbors.

步骤S203,根据真近邻点近邻图确定测地距离矩阵;其中,根据真近邻点近邻图确定测地距离矩阵是通过最短路径算法得到的,可以计算真近邻点近邻图中元素的最短路径确定测地距离的元素,从而确定测地距离矩阵。Step S203, determine the geodesic distance matrix according to the true neighbor point neighbor map; wherein, according to the true neighbor point neighbor map to determine the geodesic distance matrix is obtained by the shortest path algorithm, the shortest path of the elements in the true neighbor point neighbor map can be calculated to determine the measure elements of the geodesic distance, thereby determining the geodesic distance matrix.

步骤S204,将测地距离矩阵以及测地距离表示的度量信息分别映射到核空间中;其中,通过双中心化的方法将测地距离矩阵表示的度量信息映射到第一核空间中,通过指数线性核函数将测地距离矩阵映射到第二核空间中。Step S204, map the geodesic distance matrix and the metric information represented by the geodesic distance into the kernel space respectively; wherein, map the metric information represented by the geodesic distance matrix into the first kernel space through the method of dual centralization, and use the index The linear kernel function maps the geodesic distance matrix into the second kernel space.

步骤S205,根据特征映射过程中确定的核矩阵和核空间信息素,并融合标签判别信息与指数线性核函数理论对度量矩阵进行重构;其中在特征映射的过程中确定的核矩阵和核空间信息素分别是第二核空间核矩阵和第一核空间信息素,第二核空间核矩阵是根据指数线性核函数构造的,通过改变第二核空间矩阵在确定的过程中的线性权重,将第一核空间信息素融合到第二核空间核矩阵中,从而实现第一核空间和第二核空间信息的融合,并且通过融合标签判别信息对度量矩阵进行非线性运算,从而得到重构后的度量矩阵。Step S205, according to the kernel matrix and kernel space pheromone determined in the feature mapping process, and fusing label discrimination information and exponential linear kernel function theory to reconstruct the measurement matrix; wherein the kernel matrix and kernel space determined in the feature mapping process The pheromones are the second kernel space kernel matrix and the first kernel space pheromone respectively, the second kernel space kernel matrix is constructed according to the exponential linear kernel function, by changing the linear weight of the second kernel space matrix in the determination process, the The pheromone of the first kernel space is fused into the kernel matrix of the second kernel space, so as to realize the fusion of the information of the first kernel space and the second kernel space, and perform nonlinear operation on the metric matrix by fusing the label discriminant information, so as to obtain the reconstructed metric matrix.

步骤S206,将重构的度量矩阵的特征用核空间信息素表示,并构建Mercer核矩阵;其中,重构后的度量矩阵的特征用的是第二核空间信息素表示的,第一核空间信息素融入到了第二核空间核矩阵中,因此第二核空间信息素可以表征重构后的度量矩阵中的特征。In step S206, the features of the reconstructed metric matrix are represented by nuclear space pheromones, and a Mercer kernel matrix is constructed; wherein, the features of the reconstructed metric matrix are represented by the second nuclear space pheromones, and the first nuclear space The pheromone is integrated into the second kernel space kernel matrix, so the second kernel space pheromone can characterize the features in the reconstructed metric matrix.

步骤S207,对Mercer核矩阵中的特征进行降维处理,将得到的降维处理结果与标签判别信息输入到预训练的分类器中,得到故障分类结果;其中,降维处理得到的结果为低维空间嵌入向量,通过将低维空间向量和标签判别信息输入到预训练的故障分类器中,得到故障分类结果。Step S207, perform dimensionality reduction processing on the features in the Mercer kernel matrix, and input the obtained dimensionality reduction processing results and label discrimination information into the pre-trained classifier to obtain fault classification results; wherein, the dimensionality reduction processing results are low dimensional space embedding vector, by inputting the low-dimensional space vector and label discriminant information into the pre-trained fault classifier, the fault classification result is obtained.

接下来,参考图5对本公开示例性实施方式的用于机械装备的故障诊断装置进行介绍。Next, a fault diagnosis device for machinery equipment according to an exemplary embodiment of the present disclosure will be described with reference to FIG. 5 .

如图5所示,用于机械装备的故障诊断装置500可以包括度量信息获取模块510,真近邻点近邻图构建模块520,核空间构造模块530以及特征提取模块540,度量矩阵重构模块550,降维模块560。As shown in Figure 5, the fault diagnosis device 500 for mechanical equipment may include a metric information acquisition module 510, a true neighbor point neighbor graph construction module 520, a kernel space construction module 530 and a feature extraction module 540, a metric matrix reconstruction module 550, Dimensionality reduction module 560 .

度量信息获取模块,用于获取机械装备振动信号的度量信息,并通过度量信息确定度量矩阵;The measurement information acquisition module is used to obtain the measurement information of the vibration signal of the mechanical equipment, and determine the measurement matrix through the measurement information;

真近邻点近邻图构建模块,用于根据自适应近邻策略构建真近邻点近邻图,并通过真近邻点近邻图确定度量信息中的距离信息;A true neighbor neighbor graph building module, used to construct a true neighbor neighbor graph according to an adaptive neighbor strategy, and determine distance information in the measurement information through the true neighbor neighbor graph;

核空间构造模块,用于根据预设的指数线性核函数确定至少两个核空间;A kernel space construction module, configured to determine at least two kernel spaces according to a preset exponential linear kernel function;

特征提取模块,用于将距离信息表示的不同信息映射到不同的核空间中进行特征提取,确定与核空间对应的核矩阵和核空间信息素;The feature extraction module is used to map the different information represented by the distance information to different nuclear spaces for feature extraction, and determine the nuclear matrix and nuclear space pheromone corresponding to the nuclear space;

度量矩阵重构模块,用于根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构;The metric matrix reconstruction module is used to reconstruct the metric matrix according to the kernel matrix and nuclear space pheromone fusion label discrimination information;

降维模块,用于对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。The dimensionality reduction module is used to perform dimensionality reduction processing on the features of the reconstructed measurement matrix, and input the dimensionality reduction processing results and label discrimination information into the pre-trained fault classifier to obtain fault classification results.

获取振动信号的度量信息确定度量矩阵,并根据度量信息中的角度信息和距离信息构建真近邻点近邻图,通过真近邻点近邻图确定振动信号在高维空间的数据点之间的测地距离矩阵以及测地距离表示的度量信息,并通过核函数确定核空间,将测地距离表示的度量信息映射到第一核空间中,将测地距离矩阵映射到第二核空间中,并且,通过将度量信息映射到核空间的过程中确定的第二核空间核矩阵以及第一核空间信息素,融合标签判别信息和指数线性核函数理论对度量矩阵进行重构,从而实现多空间中的信息的融合,并通过第二核空间信息素确定重构的度量矩阵中的特征表示,利用重构的度量矩阵的平方以及第二核空间信息素构建Mercer核矩阵,并根据Mercer核矩阵利用特征值分解的方法得到Mercer核矩阵中的特征对应的低维空间嵌入向量,最后将低维空间嵌入向量和标签判别信息输入到预训练的分类器中,从而得到故障分类结果。Obtain the metric information of the vibration signal to determine the metric matrix, and construct a true neighbor graph according to the angle information and distance information in the metric information, and determine the geodesic distance between the data points of the vibration signal in the high-dimensional space through the true neighbor graph Matrix and the metric information represented by the geodesic distance, and determine the kernel space through the kernel function, map the metric information represented by the geodesic distance into the first kernel space, and map the geodesic distance matrix into the second kernel space, and, by The second kernel space kernel matrix and the first kernel space pheromone determined in the process of mapping the metric information to the kernel space, and the metric matrix is reconstructed by fusing the label discriminant information and the exponential linear kernel function theory, so as to realize the information in the multi-space , and determine the feature representation in the reconstructed metric matrix through the second nuclear spatial pheromone, use the square of the reconstructed metric matrix and the second nuclear spatial pheromone to construct the Mercer kernel matrix, and use the eigenvalues according to the Mercer kernel matrix The decomposition method obtains the low-dimensional space embedding vector corresponding to the features in the Mercer kernel matrix, and finally inputs the low-dimensional space embedding vector and label discrimination information into the pre-trained classifier to obtain the fault classification result.

需要说明的是,尽管在附图中以特定顺序描述了本公开中方法的各个步骤,但是,这并非要求或者暗示必须按照该特定顺序来执行这些步骤,或是必须执行全部所示的步骤才能实现期望的结果。附加的或备选的,可以省略某些步骤,将多个步骤合并为一个步骤执行,以及/或者将一个步骤分解为多个步骤执行等。It should be noted that although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be performed in this specific order, or that all shown steps must be performed to achieve achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution, etc.

此外,在本公开的示例性实施例中,还提供了一种能够实现上述故障诊断方法的电子设备。In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above fault diagnosis method is also provided.

所属技术领域的技术人员能够理解,本公开的各个方面可以实现为系统、方法或程序产品。因此,本公开的各个方面可以具体实现为以下形式,即:完全的硬件实施例、完全的软件实施例(包括固件、微代码等),或硬件和软件方面结合的实施例,这里可以统称为“电路”、“模块”或“系统”。Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be embodied in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as "circuit", "module" or "system".

下面参照图6来描述根据本公开的这种实施例的电子设备600。图6所示的电子设备600仅仅是一个示例,不应对本公开实施例的功能和使用范围带来任何限制。An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to FIG. 6 . The electronic device 600 shown in FIG. 6 is only an example, and should not limit the functions and application scope of the embodiments of the present disclosure.

如图6所示,电子设备600以通用计算设备的形式表现。电子设备600的组件可以包括但不限于:上述至少一个处理单元610、上述至少一个存储单元620、连接不同系统组件(包括存储单元620和处理单元610)的总线630、显示单元640。As shown in FIG. 6, electronic device 600 takes the form of a general-purpose computing device. Components of the electronic device 600 may include, but are not limited to: at least one processing unit 610 , at least one storage unit 620 , a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610 ), and a display unit 640 .

其中,所述存储单元存储有程序代码,所述程序代码可以被所述处理单元610执行,使得所述处理单元610执行本说明书上述“示例性方法”部分中描述的根据本公开各种示例性实施例的步骤。例如,所述处理单元610可以执行如图1中所示的步骤S110,获取机械装备振动信号的度量信息,并通过所述度量信息确定度量矩阵;步骤S120,根据自适应近邻策略构建真近邻点近邻图,并通过真近邻点近邻图确定度量信息中的距离信息;步骤S130,根据预设的指数线性核函数确定至少两个核空间;步骤S140,将距离信息表示的不同信息映射到不同的核空间中,确定与核空间对应的核矩阵和核空间信息素;步骤S150,根据核矩阵和核空间信息素融合标签判别信息对度量矩阵进行重构;步骤S160,对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes various exemplary methods according to the present disclosure described in the “Exemplary Methods” section above in this specification. Example steps. For example, the processing unit 610 may execute step S110 as shown in FIG. 1 to obtain the metric information of the mechanical equipment vibration signal, and determine the metric matrix through the metric information; step S120, construct the true neighbor point according to the adaptive neighbor strategy Neighbor graph, and determine the distance information in the metric information through the true neighbor point neighbor graph; Step S130, determine at least two kernel spaces according to the preset exponential linear kernel function; Step S140, map different information represented by the distance information to different In the nuclear space, determine the nuclear matrix and nuclear space pheromone corresponding to the nuclear space; step S150, reconstruct the measurement matrix according to the fusion label discrimination information of the nuclear matrix and nuclear space pheromone; step S160, reconstruct the measurement matrix Dimensionality reduction processing is carried out on the features, and the result of dimensionality reduction processing and label discrimination information are input into the pre-trained fault classifier to obtain the fault classification result.

存储单元620可以包括易失性存储单元形式的可读介质,例如随机存取存储单元(RAM)621和/或高速缓存存储单元622,还可以进一步包括只读存储单元(ROM)623。The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 621 and/or a cache storage unit 622 , and may further include a read-only storage unit (ROM) 623 .

存储单元620还可以包括具有一组(至少一个)程序模块625的程序/实用工具624,这样的程序模块625包括但不限于:操作系统、一个或者多个应用程序、其它程序模块以及程序数据,这些示例中的每一个或某种组合中可能包括网络环境的实现。The storage unit 620 may also include a program/utility 624 having a set (at least one) of program modules 625, such program modules 625 including but not limited to: an operating system, one or more application programs, other program modules, and program data, Implementations of networked environments may be included in each or some combination of these examples.

总线630可以为表示几类总线结构中的一种或多种,包括存储单元总线或者存储单元控制器、外围总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。Bus 630 may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local area using any of a variety of bus structures. bus.

电子设备600也可以与一个或多个外部设备670(例如键盘、指向设备、蓝牙设备等)通信,还可与一个或者多个使得用户能与该电子设备600交互的设备通信,和/或与使得该电子设备600能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口650进行。并且,电子设备600还可以通过网络适配器660与一个或者多个网络(例如局域网(LAN),广域网(WAN)和/或公共网络,例如因特网)通信。如图所示,网络适配器660通过总线630与电子设备600的其它模块通信。应当明白,尽管图中未示出,可以结合电子设备600使用其它硬件和/或软件模块,包括但不限于:微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。The electronic device 600 can also communicate with one or more external devices 670 (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 600, and/or communicate with Any device (eg, router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 650 . Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 660 . As shown, the network adapter 660 communicates with other modules of the electronic device 600 through the bus 630 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

通过以上的实施例的描述,本领域的技术人员易于理解,这里描述的示例实施例可以通过软件实现,也可以通过软件结合必要的硬件的方式来实现。因此,根据本公开实施例的技术方案可以以软件产品的形式体现出来,该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM,U盘,移动硬盘等)中或网络上,包括若干指令以使得一台计算设备(可以是个人计算机、服务器、终端装置、或者网络设备等)执行根据本公开实施例的方法。Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiment of the present disclosure.

在本公开的示例性实施例中,还提供了一种计算机可读存储介质,其上存储有能够实现本说明书上述方法的程序产品。在一些可能的实施例中,本公开的各个方面还可以实现为一种程序产品的形式,其包括程序代码,当所述程序产品在终端设备上运行时,所述程序代码用于使所述终端设备执行本说明书上述“示例性方法”部分中描述的根据本公开各种示例性实施例的步骤。In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium on which a program product capable of implementing the above-mentioned method in this specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code, and when the program product is run on a terminal device, the program code is used to make the The terminal device executes the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section above in this specification.

根据本公开的实施例的用于实现上述用于机械装备的故障诊断方法的程序产品,其可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码,并可以在终端设备,例如个人电脑上运行。然而,本公开的程序产品不限于此,在本文件中,可读存储介质可以是任何包含或存储程序的有形介质,该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。According to the embodiment of the present disclosure, the program product for implementing the above-mentioned fault diagnosis method for mechanical equipment may adopt a portable compact disk read-only memory (CD-ROM) and include program codes, and may be used in terminal equipment such as personal run on the computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus or device.

所述程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件,或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括:具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。The program product may reside on any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号,其中承载了可读程序代码。这种传播的数据信号可以采用多种形式,包括但不限于电磁信号、光信号或上述的任意合适的组合。可读信号介质还可以是可读存储介质以外的任何可读介质,该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。A computer readable signal medium may include a data signal carrying readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

可读介质上包含的程序代码可以用任何适当的介质传输,包括但不限于无线、有线、光缆、RF等等,或者上述的任意合适的组合。Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

可以以一种或多种程序设计语言的任意组合来编写用于执行本公开操作的程序代码,所述程序设计语言包括面向对象的程序设计语言—诸如Java、C++等,还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中,远程计算设备可以通过任意种类的网络,包括局域网(LAN)或广域网(WAN),连接到用户计算设备,或者,可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming language - such as "C" or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it may be connected to an external computing device (for example, using an Internet service provider). business to connect via the Internet).

此外,上述附图仅是根据本公开示例性实施例的方法所包括的处理的示意性说明,而不是限制目的。易于理解,上述附图所示的处理并不表明或限制这些处理的时间顺序。另外,也易于理解,这些处理可以是例如在多个模块中同步或异步执行的。In addition, the above-mentioned drawings are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not imply or limit the chronological order of these processes. In addition, it is also easy to understand that these processes may be executed synchronously or asynchronously in multiple modules, for example.

通过以上的实施例的描述,本领域的技术人员易于理解,这里描述的示例实施例可以通过软件实现,也可以通过软件结合必要的硬件的方式来实现。因此,根据本公开实施例的技术方案可以以软件产品的形式体现出来,该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM,U盘,移动硬盘等)中或网络上,包括若干指令以使得一台计算设备(可以是个人计算机、服务器、触控终端、或者网络设备等)执行根据本公开实施例的方法。Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) execute the method according to the embodiment of the present disclosure.

本领域技术人员在考虑说明书及实践这里公开的发明后,将容易想到本公开的其它实施例。本申请旨在涵盖本公开的任何变型、用途或者适应性变化,这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的,本公开的真正范围和精神由权利要求指出。Other embodiments of the disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure . The specification and examples are to be considered exemplary only, with the true scope and spirit of the disclosure indicated by the appended claims.

应当理解的是,本公开并不局限于上面已经描述并在附图中示出的精确结构,并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。It should be understood that the present disclosure is not limited to the precise constructions which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (10)

1.一种用于机械装备的故障诊断方法,其特征在于,所述故障诊断方法包括:1. A fault diagnosis method for mechanical equipment, characterized in that, the fault diagnosis method comprises: 获取机械装备振动信号的度量信息,并通过所述度量信息确定度量矩阵;Acquiring metric information of vibration signals of mechanical equipment, and determining a metric matrix through the metric information; 根据自适应近邻策略构建真近邻点近邻图,并通过所述真近邻点近邻图确定所述度量信息中的距离信息;Constructing a true neighbor neighbor graph according to an adaptive neighbor strategy, and determining distance information in the metric information through the true neighbor neighbor graph; 根据预设的指数线性核函数确定至少两个核空间;determining at least two kernel spaces according to a preset exponential linear kernel function; 将所述距离信息表示的不同信息映射到不同的所述核空间中,确定与所述核空间对应的核矩阵和核空间信息素;Mapping different information represented by the distance information into different nuclear spaces, and determining a nuclear matrix and a nuclear space pheromone corresponding to the nuclear space; 根据所述核矩阵和所述核空间信息素融合标签判别信息对所述度量矩阵进行重构;reconstructing the metric matrix according to the kernel matrix and the nuclear space pheromone fusion label discrimination information; 对重构后的度量矩阵的特征进行降维处理,并将降维处理后的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。The dimensionality reduction processing is performed on the features of the reconstructed measurement matrix, and the dimensionality reduction processing results and label discrimination information are input into the pre-trained fault classifier to obtain the fault classification results. 2.根据权利要求1所述的故障诊断方法,其特征在于,所述根据自适应近邻策略构建真近邻点近邻图包括:2. fault diagnosis method according to claim 1, is characterized in that, described according to self-adaptive neighbor strategy construction real neighbor point neighbor map comprises: 获取机械装备振动信号的样本点和初近邻点,所述初近邻点由所述样本点设置的近邻值确定;Obtaining the sample point and the initial neighbor point of the vibration signal of the mechanical equipment, the initial neighbor point is determined by the neighbor value set by the sample point; 根据所述样本点与所述初近邻点之间的距离,确定真近邻点数和初近邻点数;Determine the number of true neighbor points and the number of initial neighbor points according to the distance between the sample point and the initial neighbor point; 根据所述真近邻点数和所述初近邻点数构建所述真近邻点近邻图。Constructing the neighbor graph of true neighbors according to the number of true neighbors and the number of initial neighbors. 3.根据权利要求1所述的故障诊断方法,其特征在于,所述将所述距离信息表示的不同信息映射到不同的所述核空间中,包括:3. The fault diagnosis method according to claim 1, wherein the mapping of different information represented by the distance information into different kernel spaces comprises: 计算距离信息之间的相似度以及内积,并根据所述相似度以及所述内积将距离信息表示的不同信息映射到不同的所述核空间中。The similarity and the inner product between the distance information are calculated, and different information represented by the distance information is mapped into different kernel spaces according to the similarity and the inner product. 4.根据权利要求3所述的故障诊断方法,所述核空间包括第一核空间和第二核空间,其特征在于,所述将所述距离信息表示的不同信息映射到所述不同的核空间中,包括:4. The fault diagnosis method according to claim 3, wherein the kernel space includes a first kernel space and a second kernel space, wherein the different information represented by the distance information is mapped to the different kernels space, including: 根据所述真近邻点近邻图确定测地距离矩阵和平方测地距离矩阵;determining a geodesic distance matrix and a squared geodesic distance matrix according to the true neighbor point neighbor graph; 获取预先设置好的固定核矩阵;Obtain a pre-set fixed kernel matrix; 根据双中心化方法在所述平方测地距离矩阵的两侧分别与所述固定核矩阵进行乘法运算,将测地距离表示的度量信息映射到所述第一核空间中;performing multiplication with the fixed kernel matrix on both sides of the square geodesic distance matrix according to the dual centering method, and mapping the metric information represented by the geodesic distance into the first kernel space; 将所述测地距离矩阵中的元素设置为指数线性核函数矩阵的元素的测地距离信息,将所述测地距离矩阵映射到所述第二核空间中。The elements in the geodesic distance matrix are set as the geodesic distance information of the elements of the exponential linear kernel function matrix, and the geodesic distance matrix is mapped into the second kernel space. 5.根据权利要求4所述的故障诊断方法,其特征在于,所述将测地距离表示的度量信息映射到所述第一核空间中,还包括:5. The fault diagnosis method according to claim 4, wherein the mapping of the metric information represented by the geodesic distance into the first kernel space further comprises: 根据所述测地距离表示的度量信息确定第一核空间矩阵;determining a first kernel space matrix according to the metric information represented by the geodesic distance; 根据所述第一核空间矩阵设置第一核空间矩阵谱半径,并通过所述第一核空间矩阵谱半径确定第一核空间信息素;Set the spectral radius of the first nuclear space matrix according to the first nuclear space matrix, and determine the first nuclear space pheromone through the spectral radius of the first nuclear space matrix; 根据所述第一核空间信息素确定第一核空间中的特征表示。A feature representation in the first kernel space is determined according to the first kernel space pheromone. 6.根据权利要求1所述的故障诊断方法,其特征在于,所述根据所述核矩阵和所述核空间信息素融合标签判别信息对所述度量矩阵进行重构,包括:6. The fault diagnosis method according to claim 1, wherein said metric matrix is reconstructed according to said kernel matrix and said kernel space pheromone fusion label discrimination information, comprising: 通过所述核矩阵以及核空间信息素对所述度量矩阵进行非线性运算,对所述度量矩阵进行重构。The measurement matrix is reconstructed by performing nonlinear operation on the measurement matrix through the kernel matrix and the nuclear space pheromone. 7.根据权利要求6所述的故障诊断方法,其特征在于,所述核矩阵为第二核空间核矩阵,所述核空间信息素为第一核空间信息素,所述通过所述核矩阵以及核空间信息素对所述度量矩阵进行非线性运算,对所述度量矩阵进行重构包括:7. fault diagnosis method according to claim 6, is characterized in that, described kernel matrix is the second nuclear space kernel matrix, and described nuclear space pheromone is the first nuclear space pheromone, and described through described kernel matrix And the nuclear spatial pheromone performs a nonlinear operation on the metric matrix, and reconstructing the metric matrix includes: 通过对所述度量矩阵进行非线性运算,并融和标签判别信息、第一核空间信息素、第二核空间核矩阵和指数线性核函数理对所述度量矩阵进行非线性修正,确定重构后的度量矩阵。By performing nonlinear operations on the metric matrix, and fusing the label discrimination information, the first nuclear space pheromone, the second nuclear space kernel matrix and the exponential linear kernel function, the metric matrix is nonlinearly corrected, and the reconstructed metric matrix. 8.根据权利要求7所述的故障诊断方法,其特征在于,所述通过所述核矩阵以及核空间信息素对所述度量矩阵进行非线性运算,对所述度量矩阵进行重构还包括:8. fault diagnosis method according to claim 7, is characterized in that, described measurement matrix is carried out non-linear operation to described measurement matrix by described nuclear matrix and nuclear space pheromone, described measurement matrix is reconstructed and also comprises: 将所述第一核空间中的特征融合到第二核空间中;fusing features in the first kernel space into a second kernel space; 所述将所述第一核空间中的特征融合到第二核空间中包括:Said fusing the features in the first kernel space into the second kernel space includes: 将所述第一核空间信息素设置为所述指数线性核函数的线性权重,从而使第一核空间的特征融合到第二核空间核矩阵中。The first kernel space pheromone is set as the linear weight of the exponential linear kernel function, so that the features of the first kernel space are fused into the second kernel space kernel matrix. 9.根据权利要求1所述的故障诊断方法,其特征在于,所述对重构后的度量矩阵的特征进行降维处理,包括:9. The fault diagnosis method according to claim 1, wherein said dimensionality reduction process is carried out to the features of the reconstructed metric matrix, comprising: 构造核矩阵;Construct the kernel matrix; 所述核矩阵包括:The kernel matrix includes: 根据所述重构的度量矩阵,确定第二核空间矩阵谱半径,并根据所述第二核空间矩阵谱半径确定第二核空间信息素;Determine a second nuclear space matrix spectral radius according to the reconstructed metric matrix, and determine a second nuclear space pheromone according to the second nuclear space matrix spectral radius; 根据所述重构的度量矩阵与所述第二核空间信息素构造核矩阵。A kernel matrix is constructed according to the reconstructed metric matrix and the second kernel space pheromone. 10.根据权利要求1所述的故障诊断方法,其特征在于,所述对重构后的度量矩阵的特征进行降维处理,并将处理的结果与标签判别信息输入到预训练的故障分类器中,得到故障分类结果,包括:10. The fault diagnosis method according to claim 1, characterized in that, the dimensionality reduction process is carried out to the features of the reconstructed metric matrix, and the result of the processing and the label discrimination information are input to the pre-trained fault classifier , the fault classification results are obtained, including: 将分类后的特征进行特征值分解,得到所述重构度量矩阵的协方差矩阵;Decomposing the classified features into eigenvalues to obtain the covariance matrix of the reconstructed metric matrix; 根据所述协方差矩阵确定所述重构度量矩阵对应的特征值和特征向量,并根据所述特征值和特征向量对重构度量矩阵中分好类的特征进行降维,得到所述重构度量矩阵所对应的低维空间嵌入向量;Determine the eigenvalues and eigenvectors corresponding to the reconstructed metric matrix according to the covariance matrix, and perform dimensionality reduction on the classified features in the reconstructed metric matrix according to the eigenvalues and eigenvectors, to obtain the reconstructed The low-dimensional space embedding vector corresponding to the metric matrix; 根据所述低维空间嵌入向量与标签判别信息输入到预训练的故障分类器中,得到故障分类结果。According to the low-dimensional space embedding vector and the label discriminant information, it is input into a pre-trained fault classifier to obtain a fault classification result.
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