CN116226784A - Federal domain adaptive fault diagnosis method based on statistical feature fusion - Google Patents

Federal domain adaptive fault diagnosis method based on statistical feature fusion Download PDF

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CN116226784A
CN116226784A CN202310124876.6A CN202310124876A CN116226784A CN 116226784 A CN116226784 A CN 116226784A CN 202310124876 A CN202310124876 A CN 202310124876A CN 116226784 A CN116226784 A CN 116226784A
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苏常伟
缪旭弘
高晟耀
王雪仁
周涛
李沂滨
张艳涛
唐宇航
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People's Liberation Army 92578
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Abstract

The invention discloses a federal domain adaptive fault diagnosis method based on statistical feature fusion, wherein a source domain client receives statistical features of a target domain client and statistical features of active domain data and respectively performs data standardization on a source domain data set; the central server sends the fault diagnosis model to all source domain clients, and then the source domain clients train the received global model in the source domain clients by using two standardized source domain data as input; the trained models are returned to a central server, and the central server carries out global model aggregation on the received models of all source domain clients; when the training round number reaches a set value, the training is finished to obtain a final fault diagnosis model, the final fault diagnosis model is sent to a target domain client, the target domain client obtains mechanical fault data to be diagnosed, and a fault diagnosis result is obtained through the fault diagnosis model. The invention utilizes the statistical distribution difference of the data between the target domain and the source domain to improve the generalization capability of the fault diagnosis model in the target domain.

Description

基于统计特征融合的联邦域适应故障诊断方法Federated Domain Adaptive Fault Diagnosis Method Based on Statistical Feature Fusion

技术领域technical field

本发明涉及故障诊断技术,尤其涉及一种基于统计特征融合的联邦域适应故障诊断方法。The invention relates to fault diagnosis technology, in particular to a federal domain adaptive fault diagnosis method based on statistical feature fusion.

背景技术Background technique

旋转机械数据通常来自型号和工况不同、运行和使用环境不同的装备,使用这些数据协同训练的故障诊断模型对新数据的预测准确率低、泛化能力差。Rotating machinery data usually comes from equipment with different models and working conditions, and different operating and use environments. The fault diagnosis model trained with these data has low prediction accuracy and poor generalization ability for new data.

文献“Toward Secure Data Fusion in Industrial IoT Using TransferLearning”提出迁移学习中的域泛化和域适应方法可通过将数据特征空间对齐解决域漂移问题。文献“Deep Domain Generalization Combining A Priori Diagnosis KnowledgeToward Cross-Domain Fault Diagnosis of Rolling Bearing”提出基于域泛化的滚动轴承故障诊断方法。该方法在目标域只有健康样本的条件下,消除了多个域之间的潜在差异,实现了高效故障诊断。文献“Conditional Adversarial Domain Generalization With aSingle Discriminator for Bearing Fault Diagnosis”提出带有判别器的条件对抗域泛化方法,其目的是从工况不同的数据中提取域不变特征,并将这些特征推广到新故障数据中。为了实现条件对抗训练,设计了一种新的条件对抗策略,即特征提取器可以让判别器区分故障类别,但不能区分域,以更好地混淆判别器并泛化特征。文献“Intelligent FaultIdentification Based on Multisource Domain Generalization Towards ActualDiagnosis Scenario”提出了一种新的基于多源域的智能故障识别方法。该方法使用局部Fisher判别分析,将每个源域的判别结构描述为Grassmann流形的一个点。通过保留类内局部结构,局部Fisher判别分析可以从多模态故障数据中学习有效的判别器。文献“ANewMultiple Source Domain Adaptation Fault Diagnosis Method Between DifferentRotating Machines”提出了一种基于多源域自适应的迁移学习方法。该方法使用多对抗学习策略来获得域对齐的特征表示,同时对目标域具有判别性。文献“Deep AdversarialDomain Adaptation Model for Bearing Fault Diagnosis”提出了一种用于滚动轴承故障诊断的深度对抗域自适应模型。该模型构建了一个对抗域适应网络来解决源域和目标域特征分布不一致的问题。The document "Toward Secure Data Fusion in Industrial IoT Using Transfer Learning" proposes that domain generalization and domain adaptation methods in transfer learning can solve the domain drift problem by aligning the data feature space. The document "Deep Domain Generalization Combining A Priori Diagnosis Knowledge Toward Cross-Domain Fault Diagnosis of Rolling Bearing" proposes a rolling bearing fault diagnosis method based on domain generalization. Under the condition that the target domain has only healthy samples, the method eliminates the potential differences among multiple domains and realizes efficient fault diagnosis. The document "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnosis" proposes a conditional adversarial domain generalization method with a discriminator, the purpose of which is to extract domain invariant features from data with different working conditions and extend these features to new in the fault data. To achieve conditional adversarial training, a new conditional adversarial strategy is designed, that is, the feature extractor can allow the discriminator to distinguish fault categories, but not domains, to better confuse the discriminator and generalize features. The document "Intelligent FaultIdentification Based on Multisource Domain Generalization Towards ActualDiagnosis Scenario" proposes a new intelligent fault identification method based on multisource domains. The method uses local Fisher discriminant analysis, describing the discriminative structure of each source domain as a point on a Grassmann manifold. By preserving the intra-class local structure, local Fisher discriminant analysis can learn effective discriminators from multimodal fault data. The document "ANewMultiple Source Domain Adaptation Fault Diagnosis Method Between DifferentRotating Machines" proposes a migration learning method based on multi-source domain adaptation. The method uses a multi-adversarial learning strategy to obtain domain-aligned feature representations while being discriminative to the target domain. The literature "Deep AdversarialDomain Adaptation Model for Bearing Fault Diagnosis" proposes a deep adversarial domain adaptation model for rolling bearing fault diagnosis. This model constructs an adversarial domain adaptation network to solve the problem of inconsistent feature distribution of source and target domains.

数据是深度学习故障诊断算法的基础,为了保证深度学习的有效性,需要将尽可能多的数据聚合使用。为了能将不同客户端的数据安全、有效的聚合使用,解决深度学习过程中的“数据孤岛”问题,联邦学习应运而生。在联邦学习中学习任务是在中央服务器的协调下由多个参与设备(即客户端)以松散联邦的形式解决。联邦学习使用不同客户端的数据协作训练模型,但是由于工作条件或者型号不同,这些数据通常存在域漂移问题,因此联邦迁移学习受到越来越多研究人员的关注。Data is the basis of deep learning fault diagnosis algorithms. In order to ensure the effectiveness of deep learning, it is necessary to aggregate and use as much data as possible. In order to safely and effectively aggregate data from different clients and solve the problem of "data islands" in the process of deep learning, federated learning emerged as the times require. In federated learning, the learning task is solved in the form of loose federation by multiple participating devices (ie clients) under the coordination of the central server. Federated learning uses data from different clients to collaboratively train models, but due to different working conditions or models, these data usually have domain drift problems, so federated transfer learning has attracted more and more attention from researchers.

文献“Federated Transfer Learning for Intelligent Fault DiagnosticsUsing Deep Adversarial Networks With Data Privacy”提出了一种用于故障诊断的联邦迁移学习方法,该方法为不同的边设计不同的网络模型结构,并使用深度对抗学习实现联邦通信。文献“Data privacy preserving federated transfer learning inmachinery fault diagnostics using prior distributions.Structural HealthMonitoring”提出了一种用于机械故障诊断的联邦迁移学习方法。该方法提出使用先验分布来间接解决域漂移问题,并通过提取不同用户的域不变特征进行故障诊断。The document "Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy" proposes a federated transfer learning method for fault diagnosis, which designs different network model structures for different edges, and uses deep adversarial learning to achieve federation communication. The document "Data privacy preserving federated transfer learning inmachinery fault diagnostics using prior distributions. Structural Health Monitoring" proposes a federated transfer learning method for mechanical fault diagnosis. This method proposes to use prior distributions to indirectly address the domain drift problem and perform fault diagnosis by extracting domain-invariant features of different users.

虽然联邦迁移学习在故障诊断方面取得了与迁移学习相当的性能,但现有的联邦域适应方法通常需要将所有客户端的特征数据传输到中心服务器实现特征空间对齐,这增加了客户端与模型之间的通信代价。此外,现有联邦域适应方法缺少对客户端多传感器输入信号特征融合方法的研究。Although federated transfer learning has achieved comparable performance to transfer learning in fault diagnosis, existing federated domain adaptation methods usually need to transmit feature data from all clients to the central server to achieve feature space alignment, which increases the distance between the client and the model. communication cost between them. In addition, existing federated domain adaptation methods lack research on feature fusion methods for client-side multi-sensor input signals.

发明内容Contents of the invention

为解决现有技术中存在的不足,本发明的目的在于,提供一种基于统计特征融合的联邦域适应故障诊断方法。In order to solve the deficiencies in the prior art, the object of the present invention is to provide a federal domain adaptive fault diagnosis method based on statistical feature fusion.

为实现本发明的目的,本发明所采用的技术方案是:For realizing the purpose of the present invention, the technical scheme adopted in the present invention is:

一种基于统计特征融合的联邦域适应故障诊断方法,包括步骤:A federal domain adaptive fault diagnosis method based on statistical feature fusion, including steps:

(1)目标域客户端将未标记故障数据的统计特征发送到中心服务器,中心服务器再将这些统计特征发送到所有的源域客户端;源域客户端使用接收的统计特征和自有源域数据的统计特征分别对源域数据集进行数据标准化;(1) The target domain client sends the statistical features of unmarked fault data to the central server, and the central server sends these statistical features to all source domain clients; the source domain client uses the received statistical features and its own source domain The statistical characteristics of the data perform data standardization on the source domain data set respectively;

(2)中心服务器的故障诊断模型包括特征提取网络和分类网络,中心服务器将全局模型发送给所有源域客户端,然后源域客户端使用两种标准化后的源域数据作为输入,基于相关性对齐方法在源域客户端中训练接收到的全局模型;(2) The fault diagnosis model of the central server includes a feature extraction network and a classification network. The central server sends the global model to all source domain clients, and then the source domain clients use two standardized source domain data as input, based on correlation The alignment method trains the received global model in the source domain client;

(3)在所有源域客户端的模型训练一轮后,训练的模型都会被发送至中心服务器,中心服务器对接收到的所有源域客户端的模型做全局模型聚合;然后被发送到所有源域客户端中进行下一轮训练,当训练轮数达到设定值时,训练任务结束,得到最终的故障诊断模型;(3) After a round of model training for all source domain clients, the trained models will be sent to the central server, and the central server will perform global model aggregation on the received models of all source domain clients; and then send them to all source domain clients The next round of training is carried out in the terminal. When the number of training rounds reaches the set value, the training task ends and the final fault diagnosis model is obtained;

(4)在测试阶段,中心服务器将训练好的故障诊断模型发送给目标域客户端,目标域客户端获取待诊断的机械故障数据,通过故障诊断模型得到故障诊断结果。(4) In the test phase, the central server sends the trained fault diagnosis model to the target domain client, and the target domain client obtains the mechanical fault data to be diagnosed, and obtains the fault diagnosis result through the fault diagnosis model.

进一步地,步骤(1)中,统计特征为均值和标准差。Further, in step (1), the statistical features are mean and standard deviation.

进一步地,步骤(2)中,在特征提取网络中,采用不同特征提取通道的统计特征交换实现本通道输出特征的标准化,采用交换标准化的特征作为分类网络的输入。Further, in step (2), in the feature extraction network, the statistical feature exchange of different feature extraction channels is used to standardize the output features of this channel, and the exchanged standardized features are used as the input of the classification network.

进一步地,步骤(2)中,源域客户端收到的全局模型包括特征提取网络和分类网络。Further, in step (2), the global model received by the source domain client includes a feature extraction network and a classification network.

进一步地,基于相关性对齐方法在源域客户端中训练接收到的全局模型,具体为,源域客户端还包括特征空间度量,特征空间度量使用相关性对齐方法约束同种传感器数据不同特征之间的距离。Further, the received global model is trained in the source domain client based on the correlation alignment method. Specifically, the source domain client also includes a feature space metric, and the feature space metric uses the correlation alignment method to constrain the relationship between different features of the same sensor data. distance between.

进一步地,特征空间度量的输入为,采用不同特征提取通道的统计特征交换实现标准化的特征。Furthermore, the input of the feature space metric is the standardized feature achieved by the statistical feature exchange of different feature extraction channels.

进一步地,步骤(3)中,中心服务器上的全局模型聚合为,Further, in step (3), the global model on the central server is aggregated as,

将N个源域客户端上的N个全局模型的特征提取器

Figure BDA0004081799760000031
和N个源域客户端上的N个全局模型的分类器/>
Figure BDA0004081799760000032
平均化后,更新中心服务器上的全局模型的特征提取器/>
Figure BDA0004081799760000033
和分类器/>
Figure BDA0004081799760000034
其中,/>
Figure BDA0004081799760000035
为第i个源域客户端上的全局模型的特征提取器,/>
Figure BDA0004081799760000036
为第i个源域客户端上的全局模型的分类器。The feature extractors of N global models on N source domain clients
Figure BDA0004081799760000031
and classifiers of N global models on N source domain clients />
Figure BDA0004081799760000032
After averaging, update the feature extractor of the global model on the central server />
Figure BDA0004081799760000033
and classifier />
Figure BDA0004081799760000034
where, />
Figure BDA0004081799760000035
is the feature extractor of the global model on the i-th source domain client, />
Figure BDA0004081799760000036
is the classifier of the global model on the i-th source domain client.

进一步地,步骤(4)中,目标域客户端收到的模型包括特征提取网络和分类网络。Further, in step (4), the model received by the target domain client includes a feature extraction network and a classification network.

进一步地,特征提取网络两个通道的结构相同,特征提取网络由三组依次连接的卷积层、正则化层、线性修正单元和最大池化层组成;特征提取网络的输入首先进入第一组的卷积层,第一组的最大池化层连接第二组的卷积层,第二组的最大池化层连接第三组的卷积层。Furthermore, the structure of the two channels of the feature extraction network is the same, and the feature extraction network consists of three groups of sequentially connected convolutional layers, regularization layers, linear correction units, and maximum pooling layers; the input of the feature extraction network first enters the first group The convolutional layers of the first set of max pooling layers are connected to the second set of convolutional layers, and the second set of max pooling layers are connected to the third set of convolutional layers.

进一步地,分类网络由依次连接的展平层、第一全连接层、正则化层、修正线性单元层、第二全连接层和softmax函数层组成。Further, the classification network consists of sequentially connected flattening layer, first fully connected layer, regularization layer, modified linear unit layer, second fully connected layer and softmax function layer.

本发明的有益效果在于,与现有技术相比,本发明基于统计特征融合的联邦域适应故障诊断方法,利用目标域与源域之间数据的统计分布差异、源域数据多传感器信号的统计分布差异,提高故障诊断模型在目标域的泛化能力。本发明中只有目标域数据的统计特征等被传输到各源域客户端,减少了通信负担。此外,在源域客户端,多传感器数据特征的统计特征将彼此交换,实现特征域空间增强。The beneficial effect of the present invention is that, compared with the prior art, the federal domain adaptive fault diagnosis method based on statistical feature fusion of the present invention utilizes the difference in statistical distribution of data between the target domain and the source domain, and the statistics of multi-sensor signals of source domain data The difference in distribution improves the generalization ability of the fault diagnosis model in the target domain. In the present invention, only the statistical features of the target domain data are transmitted to each source domain client, which reduces the communication burden. In addition, at the source domain client, the statistical features of multi-sensor data features will be exchanged with each other to achieve feature domain space enhancement.

附图说明Description of drawings

图1是本发明所述的基于统计特征融合的联邦域适应故障诊断方法流程图;Fig. 1 is the flowchart of the federal domain adaptive fault diagnosis method based on statistical feature fusion according to the present invention;

图2是特征提取网络示意图;Fig. 2 is a schematic diagram of feature extraction network;

图3是客户端训练模型示意图。Fig. 3 is a schematic diagram of a client training model.

具体实施方式Detailed ways

下面结合附图和实施例对本发明的技术方案作进一步的说明。以下实施例仅用于更加清楚地说明本发明的技术方案,而不能以此来限制本申请的保护范围。The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.

如图1所示,本发明所述的基于统计特征融合的联邦域适应故障诊断方法,包括以下步骤:As shown in Figure 1, the federal domain adaptive fault diagnosis method based on statistical feature fusion described in the present invention includes the following steps:

(1)首先进行源域数据标准化,具体地,目标域客户端将未标记故障数据的统计特征(均值和标准差)发送到中心服务器,中心服务器再将这些统计特征发送到所有的源域客户端;源域客户端使用接收的统计特征和自有源域数据的统计特征分别对源域数据集进行数据标准化;(1) First, standardize the source domain data. Specifically, the target domain client sends the statistical characteristics (mean and standard deviation) of unmarked fault data to the central server, and the central server sends these statistical characteristics to all source domain clients end; the source domain client uses the received statistical characteristics and the statistical characteristics of its own source domain data to standardize the source domain data set;

设N个源域客户端中的N个数据集为

Figure BDA0004081799760000041
源域客户端k中的数据集为
Figure BDA0004081799760000042
其中,mk为源域客户端k中包含的数据样本数,/>
Figure BDA0004081799760000043
为第k个源域客户端中的第j个数据样本,/>
Figure BDA0004081799760000044
为第k个源域客户端中的的第j个数据样本的标签。Let the N data sets in the N source domain clients be
Figure BDA0004081799760000041
The data set in source domain client k is
Figure BDA0004081799760000042
Among them, m k is the number of data samples contained in the source domain client k, />
Figure BDA0004081799760000043
is the jth data sample in the kth source domain client, />
Figure BDA0004081799760000044
is the label of the jth data sample in the kth source domain client.

目标域客户端的数据集

Figure BDA0004081799760000045
在第N+1个客户端中,nt为目标域样本数,/>
Figure BDA0004081799760000046
为目标域客户端中的第i个样本。设目标域数据集的均值为/>
Figure BDA0004081799760000047
标准差/>
Figure BDA0004081799760000048
Datasets for target domain clients
Figure BDA0004081799760000045
In the N+1th client, n t is the number of samples in the target domain, />
Figure BDA0004081799760000046
is the i-th sample in the target domain client. Let the mean of the target domain dataset be />
Figure BDA0004081799760000047
standard deviation />
Figure BDA0004081799760000048

标准化后的目标域数据为:The normalized target domain data is:

Figure BDA0004081799760000049
Figure BDA0004081799760000049

目标域客户端将μt和δt发送给中心服务器,中心服务器再将其发送给各个源域客户端。在第k个源域客户端中,

Figure BDA00040817997600000410
从μt和δt随机选取一组μii,对/>
Figure BDA00040817997600000411
进行标准化,即:The target domain client sends μ t and δ t to the central server, and the central server sends them to each source domain client. In the kth source domain client,
Figure BDA00040817997600000410
Randomly select a set of μ i , δ i from μ t and δ t , for />
Figure BDA00040817997600000411
to standardize, i.e.:

Figure BDA00040817997600000412
Figure BDA00040817997600000412

Figure BDA00040817997600000413
为第k个源域客户端使用目标域的统计特征标准化的数据。
Figure BDA00040817997600000413
Data normalized for the kth source domain client using the statistical characteristics of the target domain.

此外,设

Figure BDA0004081799760000051
的均值和标准差分别为/>
Figure BDA0004081799760000052
Figure BDA0004081799760000053
使用/>
Figure BDA0004081799760000054
和/>
Figure BDA0004081799760000055
标准化/>
Figure BDA0004081799760000056
的结果为:In addition, set
Figure BDA0004081799760000051
The mean and standard deviation of
Figure BDA0004081799760000052
Figure BDA0004081799760000053
use />
Figure BDA0004081799760000054
and />
Figure BDA0004081799760000055
Standardization />
Figure BDA0004081799760000056
The result is:

Figure BDA0004081799760000057
Figure BDA0004081799760000057

Figure BDA0004081799760000058
为第k个源域客户端使用自有源域数据的统计特征标准化的数据。
Figure BDA0004081799760000058
Data normalized using the statistical characteristics of the own source domain data for the kth source domain client.

标准化的源域数据

Figure BDA0004081799760000059
和/>
Figure BDA00040817997600000510
用于在源域客户端中训练故障诊断模型。Normalized source domain data
Figure BDA0004081799760000059
and />
Figure BDA00040817997600000510
It is used to train the fault diagnosis model in the source domain client.

(2)源域客户端使用两种标准化后的源域数据作为输入,基于相关性对齐(Correlation Alignment,CORAL)方法,在源域客户端中训练故障诊断模型;(2) The source domain client uses two standardized source domain data as input, and trains the fault diagnosis model in the source domain client based on the Correlation Alignment (CORAL) method;

中心服务器的故障诊断模型包括一个特征提取网络(特征提取器)和一个分类网络(分类器)。The fault diagnosis model of the central server includes a feature extraction network (feature extractor) and a classification network (classifier).

如图3所示,源域客户端收到的模型包括特征提取网络和分类网络;源域客户端还包括特征距离度量方法CORAL,作用是限制同种传感器数据的不同特征之间的距离。As shown in Figure 3, the model received by the source domain client includes the feature extraction network and the classification network; the source domain client also includes the feature distance measurement method CORAL, which is used to limit the distance between different features of the same sensor data.

中心服务器上的特征提取网络和分类网络分别记为

Figure BDA00040817997600000511
和/>
Figure BDA00040817997600000512
在训练模型时,中心服务器首先将/>
Figure BDA00040817997600000513
和/>
Figure BDA00040817997600000514
发送给所有源域客户端,然后源域客户端k使用其本地训练数据集训练接收到的全局模型。The feature extraction network and classification network on the central server are denoted as
Figure BDA00040817997600000511
and />
Figure BDA00040817997600000512
When training the model, the central server first sends the />
Figure BDA00040817997600000513
and />
Figure BDA00040817997600000514
Sent to all source domain clients, then source domain client k uses its local training dataset to train the received global model.

在源域客户端k中,

Figure BDA00040817997600000515
和/>
Figure BDA00040817997600000516
将分别作为/>
Figure BDA00040817997600000517
和/>
Figure BDA00040817997600000518
的输入。
Figure BDA00040817997600000519
由电流数据/>
Figure BDA00040817997600000520
和振动数据/>
Figure BDA00040817997600000521
组成,同样地,/>
Figure BDA00040817997600000522
由电流数据/>
Figure BDA00040817997600000523
和振动数据/>
Figure BDA00040817997600000524
组成。In source domain client k,
Figure BDA00040817997600000515
and />
Figure BDA00040817997600000516
will be respectively as />
Figure BDA00040817997600000517
and />
Figure BDA00040817997600000518
input of.
Figure BDA00040817997600000519
By current data />
Figure BDA00040817997600000520
and vibration data />
Figure BDA00040817997600000521
Composition, likewise, />
Figure BDA00040817997600000522
By current data />
Figure BDA00040817997600000523
and vibration data />
Figure BDA00040817997600000524
composition.

如图2所示,在特征提取网络,通过交换不同传感器数据的统计特征增强特征域适应能力。特征提取网络

Figure BDA00040817997600000525
有两个特征提取通道,通道1的输入为电流数据,通道2的输入为振动数据。通道1和通道2输出的特征将使用两个通道交换的统计特征实现标准化。通道1和通道2的输出的特征交换标准化后将作为分类网络/>
Figure BDA00040817997600000526
和CORAL的输入。As shown in Figure 2, in the feature extraction network, the feature domain adaptability is enhanced by exchanging the statistical features of different sensor data. Feature Extraction Network
Figure BDA00040817997600000525
There are two feature extraction channels, the input of channel 1 is current data, and the input of channel 2 is vibration data. The features output by channel 1 and channel 2 will be normalized using the statistical features exchanged by the two channels. The feature exchange of the output of channel 1 and channel 2 after normalization will be used as a classification network />
Figure BDA00040817997600000526
and CORAL input.

Figure BDA00040817997600000527
经过通道1处理后的输出为/>
Figure BDA00040817997600000528
经过通道2处理后的输出为/>
Figure BDA00040817997600000529
经过通道1处理后的输出为/>
Figure BDA00040817997600000530
经过通道2处理后的输出为/>
Figure BDA00040817997600000531
的均值和标准差用于标准化/>
Figure BDA0004081799760000061
的均值和标准差用于标准化/>
Figure BDA0004081799760000062
同理,/>
Figure BDA0004081799760000063
的均值和标准差用于标准化/>
Figure BDA0004081799760000064
的均值和标准差用于标准化/>
Figure BDA0004081799760000065
标准化后的/>
Figure BDA0004081799760000066
Figure BDA0004081799760000067
将作为/>
Figure BDA0004081799760000068
和CORAL的输入。
Figure BDA00040817997600000527
The output after processing by channel 1 is />
Figure BDA00040817997600000528
The output after channel 2 processing is />
Figure BDA00040817997600000529
The output after processing by channel 1 is />
Figure BDA00040817997600000530
The output after channel 2 processing is />
Figure BDA00040817997600000531
The mean and standard deviation of are used for standardization />
Figure BDA0004081799760000061
The mean and standard deviation of are used for standardization />
Figure BDA0004081799760000062
In the same way, />
Figure BDA0004081799760000063
The mean and standard deviation of are used for standardization />
Figure BDA0004081799760000064
The mean and standard deviation of are used for standardization />
Figure BDA0004081799760000065
Normalized />
Figure BDA0004081799760000066
Figure BDA0004081799760000067
will be used as />
Figure BDA0004081799760000068
and CORAL input.

Figure BDA0004081799760000069
是源域客户端k中分类网络的分类损失值,其公式为:
Figure BDA0004081799760000069
is the classification loss value of the classification network in the source domain client k, and its formula is:

Figure BDA00040817997600000610
Figure BDA00040817997600000610

其中,

Figure BDA00040817997600000611
为数据样本j的真实标签,/>
Figure BDA00040817997600000612
为源域客户端k中分类网络对数据样本j的预测结果。in,
Figure BDA00040817997600000611
is the real label of data sample j, />
Figure BDA00040817997600000612
is the prediction result of the classification network on the data sample j in the source domain client k.

此外,为了约束同种传感器数据的不同特征之间的距离,使用基于相关性对齐(Correlation Alignment,CORAL)的特征距离度量损失,loss1,CORAL,loss2,CORAL计算过程如下:In addition, in order to constrain the distance between different features of the same sensor data, the feature distance measurement loss based on Correlation Alignment (CORAL) is used, loss 1, CORAL , loss 2, CORAL calculation process is as follows:

Figure BDA00040817997600000613
Figure BDA00040817997600000613

Figure BDA00040817997600000614
Figure BDA00040817997600000614

其中,

Figure BDA00040817997600000615
和C1,g分别为标准化后的/>
Figure BDA00040817997600000616
的特征协方差矩阵,/>
Figure BDA00040817997600000617
和C2,g分别为标准化后的/>
Figure BDA00040817997600000618
的特征协方差矩阵;d代表特征的维度,||·||F代表Frobenius范数。in,
Figure BDA00040817997600000615
and C 1, g are the normalized />
Figure BDA00040817997600000616
The characteristic covariance matrix of , />
Figure BDA00040817997600000617
and C 2,g are the normalized />
Figure BDA00040817997600000618
The feature covariance matrix of ; d represents the dimension of the feature, ||·|| F represents the Frobenius norm.

因此,lossk包含源域数据集的源域客户端k中的分类损失和特征距离度量损失的加权和,即:Therefore, loss k contains the weighted sum of classification loss and feature distance metric loss in source domain client k of the source domain dataset, namely:

Figure BDA00040817997600000619
Figure BDA00040817997600000619

其中,lossk表示损失之和,

Figure BDA00040817997600000620
表示源域客户端k中的分类损失,loss1,CORAL表示电流数据的特征距离度量损失,loss2,CORAL表示振动数据的特征距离度量损失,β=0.01。Among them, loss k represents the sum of losses,
Figure BDA00040817997600000620
Indicates the classification loss in source domain client k, loss 1, CORAL represents the characteristic distance metric loss of current data, loss 2, CORAL represents the characteristic distance metric loss of vibration data, β = 0.01.

(3)在所有源域客户端的模型训练一轮后,所有在源域客户端上训练的全局模型都会被发送至中心服务器;中心服务器对接收到的所有源域客户端故障诊断模型做平均,得到最终的故障诊断模型;(3) After one round of model training for all source domain clients, all the global models trained on the source domain clients will be sent to the central server; the central server averages all received source domain client fault diagnosis models, Get the final fault diagnosis model;

中心服务器上的全局模型聚合,将N个源域客户端上的N个全局模型的特征提取器

Figure BDA00040817997600000621
和N个源域客户端上的N个全局模型的分类器/>
Figure BDA00040817997600000622
平均化后,更新中心服务器上的全局模型的特征提取器/>
Figure BDA0004081799760000071
和分类器/>
Figure BDA0004081799760000072
其中,/>
Figure BDA0004081799760000073
为第i个源域客户端上的全局模型的特征提取器,/>
Figure BDA0004081799760000074
为第i个源域客户端上的全局模型的分类器。Global model aggregation on the central server, feature extractors of N global models on N source domain clients
Figure BDA00040817997600000621
and classifiers of N global models on N source domain clients />
Figure BDA00040817997600000622
After averaging, update the feature extractor of the global model on the central server />
Figure BDA0004081799760000071
and classifier />
Figure BDA0004081799760000072
where, />
Figure BDA0004081799760000073
is the feature extractor of the global model on the i-th source domain client, />
Figure BDA0004081799760000074
is the classifier of the global model on the i-th source domain client.

在本实施例中,全局模型没有在中心服务器上进行进一步的训练,而是被发送到所有源域客户端中进行下一轮训练。In this embodiment, the global model is not further trained on the central server, but is sent to all source domain clients for the next round of training.

(4)在测试阶段,中心服务器将训练好的故障诊断模型发送给目标域客户端进行故障诊断。(4) In the testing phase, the central server sends the trained fault diagnosis model to the target domain client for fault diagnosis.

当训练轮数达到设定值时,训练任务结束,中心服务器将全局模型发送到目标域客户端进行故障诊断,目标域客户端获取待诊断的机械故障数据,通过中心服务器发送的全局模型,得到故障诊断结果。When the number of training rounds reaches the set value, the training task ends, and the central server sends the global model to the client in the target domain for fault diagnosis. The client in the target domain obtains the mechanical fault data to be diagnosed, and through the global model sent by the central server, obtains Fault diagnosis results.

目标域客户端收到的模型包括特征提取网络和分类网络。The model received by the target domain client includes a feature extraction network and a classification network.

特征提取网络两个通道的结构相同,特征提取网络由三组依次连接的卷积层、正则化层、线性修正单元和最大池化层组成;特征提取网络的输入首先进入第一组的卷积层,第一组的最大池化层连接第二组的卷积层,第二组的最大池化层连接第三组的卷积层。三个卷积层的卷积核数都是128,第一组、第二组和第三组中的卷积层的卷积核大小分别为17、17和3;第一组、第二组和第三组中的最大池化层的参数依次为16、16和2。The structure of the two channels of the feature extraction network is the same, and the feature extraction network consists of three groups of sequentially connected convolutional layers, regularization layers, linear correction units, and maximum pooling layers; the input of the feature extraction network first enters the convolutional layer of the first group The first set of max pooling layers is connected to the second set of convolutional layers, and the second set of max pooling layers is connected to the third set of convolutional layers. The number of convolution kernels of the three convolutional layers is 128, and the convolution kernel sizes of the convolutional layers in the first group, the second group and the third group are 17, 17 and 3 respectively; the first group, the second group and the parameters of the max pooling layer in the third group are 16, 16 and 2 in order.

分类网络由依次连接的展平层、第一全连接层、正则化层、修正线性单元层、第二全连接层和softmax函数层组成;其中,第一全连接层的参数为512,第二全连接层的参数为故障种类数。The classification network is composed of a flattening layer, the first fully connected layer, a regularization layer, a modified linear unit layer, a second fully connected layer and a softmax function layer connected in sequence; the parameters of the first fully connected layer are 512, and the parameters of the second The parameter of the fully connected layer is the number of fault types.

以下以Paderborn大学轴承故障案列对本发明进行详细说明。The present invention will be described in detail below with the Paderborn University bearing failure case.

实验中使用的数据集是Paderborn数据集。使用的轴承代号详见表1。每个轴承的传感器数据包括振动数据和电流数据。The dataset used in the experiments is the Paderborn dataset. The bearing code used is shown in Table 1. Sensor data for each bearing includes vibration data and current data.

该数据集包含三种不同状态下的轴承:内圈故障(IR)、外圈故障(OR)和健康状态(H)。数据集来自于不同转速、径向力以及负载扭矩下工作的轴承。本发明使用的轴承工况详见表2。This dataset contains bearings in three different states: Inner Race Failure (IR), Outer Race Failure (OR) and Health Status (H). The data sets are from bearings operating at different speeds, radial forces and load torques. The working conditions of the bearings used in the present invention are shown in Table 2.

本发明假设A、B、C、D分布在四个客户端中,并使用其中的两个或者三个作为源域客户端,基于所提出的基于统计特征融合的联邦域适应故障诊断方法(FDG),在不产生数据聚合的情况下协同训练模型。训练好的模型将在目标域客户端上进行测试。The present invention assumes that A, B, C, and D are distributed in four clients, and uses two or three of them as source domain clients, based on the proposed federated domain adaptive fault diagnosis method based on statistical feature fusion (FDG ), to co-train the model without generating data aggregation. The trained model will be tested on the target domain client.

表1 Paderborn数据集实验轴承代号Table 1 Paderborn data set experimental bearing code

轴承代号Bearing Code 轴承状态Bearing status KA15,KA16,KA30,KA04,KA22KA15, KA16, KA30, KA04, KA22 OROR KI14,KI17,KI21,KI16,KI18KI14, KI17, KI21, KI16, KI18 IRIR K001,K005,K002,K004,K003K001, K005, K002, K004, K003 Hh

表2不同工况下Paderborn数据集Table 2 Paderborn dataset under different working conditions

数据集代号Data set code 转速(rpm)speed(rpm) 径向力(N)Radial force (N) 负载扭矩(Nm)Load torque (Nm) AA 15001500 10001000 0.70.7 BB 15001500 10001000 0.10.1 CC 15001500 400400 0.70.7 DD. 900900 10001000 0.70.7

在Paderborn数据集上,本发明的结果以及与其他方法的对比如表3所示。On the Paderborn data set, the results of the present invention and the comparison with other methods are shown in Table 3.

表3 Paderborn数据集实验结果Table 3 Experimental results of Paderborn dataset

源域数据集source domain dataset 测试数据集test data set FedAvgFedAvg FDGFDG A,BA,B CC 0.88420.8842 0.90720.9072 B,DB,D CC 0.91170.9117 0.93730.9373 A,B,DA,B,D CC 0.91370.9137 0.94220.9422 B,C,DB,C,D AA 0.99780.9978 1.01.0 A,C,DA,C,D BB 0.99420.9942 1.01.0

与联邦平均(FedAvg)相比,本发明提出的方法取得了更好的结果。这说明与其他方法相比,本发明提出的方法具有更良好的泛化能力,意味着本发明提出的方法在源域客户端上训练的模型可以适应其他领域。Compared with the federated average (FedAvg), the proposed method achieves better results. This shows that compared with other methods, the method proposed in the present invention has better generalization ability, which means that the model trained on the source domain client by the method proposed in the present invention can be adapted to other fields.

本发明的有益效果在于,与现有技术相比,本发明基于统计特征融合的联邦域适应故障诊断方法,利用目标域与源域之间数据的统计分布差异、源域数据多传感器信号的统计分布差异,提高故障诊断模型在目标域的泛化能力。本发明中只有目标域数据的统计特征等被传输到各源域客户端,减少了通信负担。此外,在源域客户端,多传感器数据特征的统计特征将彼此交换,实现特征域空间增强。The beneficial effect of the present invention is that, compared with the prior art, the federal domain adaptive fault diagnosis method based on statistical feature fusion of the present invention utilizes the difference in statistical distribution of data between the target domain and the source domain, and the statistics of multi-sensor signals of source domain data The difference in distribution improves the generalization ability of the fault diagnosis model in the target domain. In the present invention, only the statistical features of the target domain data are transmitted to each source domain client, which reduces the communication burden. In addition, at the source domain client, the statistical features of multi-sensor data features will be exchanged with each other to achieve feature domain space enhancement.

本发明申请人结合说明书附图对本发明的实施示例做了详细的说明与描述,但是本领域技术人员应该理解,以上实施示例仅为本发明的优选实施方案,详尽的说明只是为了帮助读者更好地理解本发明精神,而并非对本发明保护范围的限制,相反,任何基于本发明的发明精神所作的任何改进或修饰都应当落在本发明的保护范围之内。The applicant of the present invention has made a detailed description and description of the implementation examples of the present invention in conjunction with the accompanying drawings, but those skilled in the art should understand that the above implementation examples are only preferred implementations of the present invention, and the detailed description is only to help readers better To understand the spirit of the present invention rather than limit the protection scope of the present invention, on the contrary, any improvement or modification made based on the spirit of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. A federal domain adaptive fault diagnosis method based on statistical feature fusion is characterized by comprising the following steps:
(1) The target domain client transmits the statistical features of the unlabeled fault data to the central server, and the central server transmits the statistical features to all source domain clients; the source domain client uses the received statistical characteristics and the statistical characteristics of the active domain data to respectively carry out data standardization on the source domain data set;
(2) The fault diagnosis model of the central server comprises a feature extraction network and a classification network, the central server sends a global model to all source domain clients, and then the source domain clients use two standardized source domain data as input and train the received global model in the source domain clients based on a correlation alignment method;
(3) After the models of all source domain clients are trained for one round, the trained models are sent to a central server, and the central server carries out global model aggregation on the received models of all source domain clients; then the training data are sent to all source domain clients to carry out the next training, and when the training round number reaches a set value, the training task is ended, so that a final fault diagnosis model is obtained;
(4) In the test stage, the central server sends the trained fault diagnosis model to the target domain client, the target domain client acquires mechanical fault data to be diagnosed, and a fault diagnosis result is obtained through the fault diagnosis model.
2. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 1, wherein in step (1), the statistical features are mean and standard deviation.
3. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 1, wherein in step (2), in the feature extraction network, the standardization of the output features of the present channel is achieved by adopting the statistical feature exchange of different feature extraction channels, and the features of the exchange standardization are adopted as the input of the classification network.
4. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 1, wherein in step (2), the global model received by the source domain client includes a feature extraction network and a classification network.
5. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 4, wherein the received global model is trained in the source domain client based on a correlation alignment method, in particular, the source domain client further comprises feature space metrics, which constrain the distance between different features of the same sensor data using the correlation alignment method.
6. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 5, wherein the input of feature space metrics is to implement standardized features using statistical feature exchange of different feature extraction channels.
7. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 1, wherein in step (3), the global model on the central server is aggregated as,
feature extractor for N global models on N source domain clients
Figure FDA0004081799710000011
Classifier of N global models on N source domain clients +.>
Figure FDA0004081799710000021
After averaging, the feature extractor of the global model on the central server is updated +.>
Figure FDA0004081799710000022
And classifier->
Figure FDA0004081799710000023
Wherein (1)>
Figure FDA0004081799710000024
Feature extractor for global model on ith source domain client,/i>
Figure FDA0004081799710000025
A classifier that is a global model on the ith source domain client.
8. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 1, wherein in step (4), the model received by the target domain client includes a feature extraction network and a classification network.
9. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 8, wherein the two channels of the feature extraction network have the same structure, and the feature extraction network is composed of three groups of convolution layers, regularization layers, linear correction units and a maximum pooling layer which are sequentially connected; the input of the feature extraction network first enters the first set of convolution layers, the first set of largest pooling layers is connected to the second set of convolution layers, and the second set of largest pooling layers is connected to the third set of convolution layers.
10. The federal domain adaptive fault diagnosis method based on statistical feature fusion according to claim 8, wherein the classification network is composed of a flattening layer, a first fully-connected layer, a regularization layer, a modified linear unit layer, a second fully-connected layer and a softmax function layer, which are sequentially connected.
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