CN114913302B - Rotary joint life prediction system and method based on multi-sensor fusion - Google Patents
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Abstract
本发明公开了一种基于多传感器融合的旋转接头寿命预测系统及方法,属于工业设备寿命预测领域。对旋转接头在空间中进行三维网格状划分,同时对多传感器设定数据采集频率,按照数据采集频率进行旋转接头指标数据采集,建立数据库,随后建立旋转接头寿命预测模型,预测每个子网格的使用寿命,最后对所有子网格使用寿命取最小值,作为旋转接头整体使用寿命。本发明既能了解到旋转接头整体的情况,又能对局部细节进行观察,获得的信息更全面;从多个角度反应旋转接头的实际情况,用多种指标数据有助于提升预测的准确度,实现对旋转接头的状态从多维度进行准确的评价与预测,且预测结果准确可靠,有利于减少故障发生的概率。
The invention discloses a rotary joint life prediction system and method based on multi-sensor fusion, belonging to the field of industrial equipment life prediction. Divide the rotary joint into a three-dimensional grid in space, and set the data collection frequency for multiple sensors at the same time, collect the index data of the rotary joint according to the data collection frequency, establish a database, and then establish a life prediction model for the rotary joint to predict each sub-grid Finally, take the minimum value of the service life of all sub-grids as the overall service life of the rotary joint. The invention can not only understand the overall situation of the rotary joint, but also observe local details, and obtain more comprehensive information; reflect the actual situation of the rotary joint from multiple angles, and use various index data to help improve the accuracy of prediction , to achieve accurate evaluation and prediction of the state of the rotary joint from multiple dimensions, and the prediction results are accurate and reliable, which is conducive to reducing the probability of failure.
Description
技术领域technical field
本发明涉及工业设备寿命预测领域,并具体涉及一种旋转接头寿命预测装置及方法。The invention relates to the field of life prediction of industrial equipment, and in particular to a device and method for life prediction of a rotary joint.
背景技术Background technique
旋转接头是一种360°旋转输送介质的密闭旋转连接器。旋转接头的作用是将液体从管道的这边输入到旋转或往复运动的设备中,再从其中排出的连接用的密封装置。旋转接头的应用领域几乎覆盖各个加工制造行业。如:冶金、机床、发电、石油、橡胶、塑料、纺织、印染、制药、卷烟、造纸、食品、饲料加工等。The rotary joint is a closed rotary connector that rotates 360° to convey the medium. The role of the rotary joint is to input the liquid from the side of the pipeline into the rotating or reciprocating equipment, and then discharge it from the sealing device for the connection. The application fields of rotary joints cover almost every processing and manufacturing industry. Such as: metallurgy, machine tools, power generation, petroleum, rubber, plastics, textiles, printing and dyeing, pharmaceuticals, cigarettes, papermaking, food, feed processing, etc.
随着经济的快速发展,工业设备使用量与日俱增,随之带来的问题也愈发明显,工业设备使用的时间越长,设备内部的机械结构会发生不同程度的损坏,导致使用性能不断下降。旋转接头内部损坏会导致密封性下降,其中输送的介质会有渗漏的风险,甚至高温高压的介质会导致设备炸裂。常规的旋转接头检修不能满足日益增长的需要。而现在对于旋转接头的故障检测多通过出厂验收、首检、定校等方法确认,却无法确认旋转接头的实际运行状态是否健康良好,在校验周期内是否存在安全或者设备隐患,这些都大大影响着旋转接头作为输送设备的稳定性和可靠性。而旋转接头的实际运行状态是否健康和检测周期内的安全或者设备隐患均表现在旋转接头的寿命上。所以提出了对旋转接头的使用寿命进行预测的合理方案来提前避免因旋转接头内部机械结构的损耗导致工业生产中的故障发生。在实际使用和大多数专利中,很少考虑准确预测旋转接头的使用寿命,往往都是在发生故障之后才会采取补救措施。因此本发明考虑到这一情况,对传统方法进行改进,能及时预测旋转接头的使用寿命,更好的了解旋转接头的情况,让企业做出合理的生产计划。With the rapid development of the economy, the use of industrial equipment is increasing day by day, and the resulting problems are becoming more and more obvious. The longer the industrial equipment is used, the mechanical structure inside the equipment will be damaged to varying degrees, resulting in continuous decline in performance. Damage to the inside of the rotary joint will lead to a decrease in sealing performance, and there is a risk of leakage of the conveyed medium, and even the high temperature and high pressure medium will cause the equipment to burst. Conventional rotary joint overhaul cannot meet the increasing demands. However, the fault detection of rotary joints is mostly confirmed through factory acceptance, first inspection, calibration and other methods, but it is impossible to confirm whether the actual operating status of the rotary joint is healthy or not, and whether there are safety hazards or equipment hidden dangers during the calibration cycle. It affects the stability and reliability of the rotary joint as a conveying device. Whether the actual operating state of the rotary joint is healthy or not, and the safety or hidden dangers of the equipment during the inspection period are all reflected in the life of the rotary joint. Therefore, a reasonable plan to predict the service life of the rotary joint is proposed to avoid the failure in industrial production caused by the loss of the internal mechanical structure of the rotary joint in advance. In actual use and in most patents, little consideration is given to accurately predicting the service life of rotary joints, and remedial measures are often taken after a failure occurs. Therefore, the present invention takes this situation into consideration, improves the traditional method, can predict the service life of the rotary joint in time, better understand the situation of the rotary joint, and allow the enterprise to make a reasonable production plan.
发明内容Contents of the invention
本发明的目的是针对现有技术存在的不足,提供一种基于多传感器融合的旋转接头寿命预测系统及方法。The object of the present invention is to provide a life prediction system and method for a rotary joint based on multi-sensor fusion to address the shortcomings of the prior art.
技术方案:本发明解决问题所采用的技术方案为:Technical scheme: the technical scheme adopted by the present invention to solve the problem is:
一种基于多传感器融合的旋转接头寿命预测系统,包括网格划分模块、多传感器数据采集模块、数据库模块、模型建立模块、寿命预测模块和结果分析模块,所述网格划分模块实现对旋转接头在空间中进行三维网格状划分,对每个子网格的坐标进行标号;所述多传感器数据采集模块对多传感器设定数据采集频率,按照数据采集频率进行旋转接头指标数据采集,并将旋转接头指标数据存储至数据库模块内;所述模型建立模块调取数据库模块内所有旋转接头指标数据并中进行训练,得到使用寿命与其他指标之间的关系,从而获得训练好的旋转接头寿命预测模型,并保存在寿命预测模块中;寿命预测模块保存训练好的旋转接头寿命预测模型,将待预测的旋转接头指标数据输入寿命预测模块,可以得到每个旋转接头子网格的使用寿命,所述结果分析模块排序所有子网格的使用寿命,筛选出所有子网格中使用寿命最短的作为旋转接头整体使用寿命。A life prediction system for rotary joints based on multi-sensor fusion, including a grid division module, a multi-sensor data acquisition module, a database module, a model building module, a life prediction module and a result analysis module, the grid division module implements the rotary joint Carry out three-dimensional grid-like division in space, and label the coordinates of each sub-grid; the multi-sensor data acquisition module sets the data acquisition frequency for multi-sensors, and performs the rotary joint index data acquisition according to the data acquisition frequency, and rotates The joint index data is stored in the database module; the model building module calls all the rotary joint index data in the database module and performs training in it to obtain the relationship between the service life and other indexes, thereby obtaining the trained rotary joint life prediction model , and stored in the life prediction module; the life prediction module saves the trained rotary joint life prediction model, and inputs the index data of the rotary joint to be predicted into the life prediction module, and the service life of each rotary joint subgrid can be obtained. The result analysis module sorts the service life of all sub-grids, and selects the one with the shortest service life among all sub-grids as the overall service life of the rotary joint.
进一步地,传感器包括采集转速S的速度传感器、采集扭矩N的扭矩传感器、采集使用寿命H的计时器、采集振动信号V的振动传感器、采集介质温度T的温度传感器和采集介质压力P的压力传感器。Further, the sensors include a speed sensor for collecting rotational speed S, a torque sensor for collecting torque N, a timer for collecting service life H, a vibration sensor for collecting vibration signal V, a temperature sensor for collecting medium temperature T, and a pressure sensor for collecting medium pressure P .
本发明还公开一种基于多传感器融合的旋转接头寿命预测方法,包括以下步骤:The invention also discloses a method for predicting the life of a rotary joint based on multi-sensor fusion, which includes the following steps:
S1:对旋转接头在空间中进行三维网格状划分,并对划分的每个子网格的坐标进行标号;S1: Divide the rotary joint into a three-dimensional grid in space, and label the coordinates of each divided sub-grid;
S2:采集旋转接头的指标数据并保存;S2: Collect and save the index data of the rotary joint;
S3:对采集到的所有旋转接头指标数据进行训练,得到使用寿命与其他指标之间的关系,从而获得训练好的旋转接头寿命预测模型;S3: Train all the collected rotary joint index data to obtain the relationship between the service life and other indicators, so as to obtain the trained rotary joint life prediction model;
S4:将待预测的旋转接头指标数据输入寿命预测模型,得到每个旋转接头子网格的使用寿命,排序所有子网格的使用寿命,筛选出所有子网格中使用寿命最短的作为旋转接头整体使用寿命。S4: Input the index data of the rotary joint to be predicted into the life prediction model to obtain the service life of each rotary joint sub-grid, sort the service life of all sub-grids, and select the one with the shortest service life among all sub-grids as the rotary joint overall service life.
进一步地,步骤S1中,三维网格的划分范围至少包含旋转接头主体部分,以旋转接头主体部分直径的十分之一的长度为边长进行三维立方体网格划分,对每个子网格按照行和列坐标进行标号。Further, in step S1, the division range of the three-dimensional grid includes at least the main part of the rotary joint, and the three-dimensional cube grid is divided with the length of one tenth of the diameter of the main part of the rotary joint as the side length, and each sub-grid is divided into rows Label with column coordinates.
进一步地,步骤S2中,采集的指标数据包括转速S、扭矩N、使用寿命H、振动信号V、介质温度T和介质压力P,各指标数据的采集频率为0.1s。Further, in step S2, the collected index data includes rotational speed S, torque N, service life H, vibration signal V, medium temperature T and medium pressure P, and the collection frequency of each index data is 0.1s.
进一步地,采集到的振动信号V通过如下公式转换成声音信号D的分贝值。Further, the collected vibration signal V is converted into the decibel value of the sound signal D by the following formula.
式中,a为振动加速度总体平均值;为放大系数;D为声音分贝平均值,单位为dB;a由振动传感器获取。In the formula, a is the overall average value of the vibration acceleration; is the amplification factor; D is the average sound decibel, in dB; a is obtained by the vibration sensor.
进一步地,将旋转接头指标数据中的使用寿命H作为输出预测值,其余的指标作为预测模型输入值,数据持续记录至旋转接头完全损坏后10小时。Further, the service life H in the rotary joint index data is used as the output prediction value, and the remaining indicators are used as the input value of the prediction model, and the data is continuously recorded until 10 hours after the rotary joint is completely damaged.
进一步地,在步骤S3中,构建旋转接头寿命预测模型表达式,具体表达式为:Further, in step S3, construct the rotary joint life prediction model expression, the specific expression is:
Ht,i=f{St,i,Nt,gi(d)·Dt,i,gi(d)·Tt,i,Pt,i}H t, i = f{S t, i , N t , g i (d) D t, i , g i (d) T t, i , P t, i }
式中,t为某一时刻;i为第几个子网格,按照行的顺序进行标号;Ht,i为第i个子网格t时刻的使用寿命;Nt为待测旋转接头t时刻的扭矩,所有子网格t时刻的扭矩值一样;f为输入输出指标数据的对应法则;St,i为第i个子网格t时刻的转速;Dt,i为第i个子网格t时刻的声音分贝;Tt,i为第i个子网格t时刻的温度值;Pt,i为第i个子网格t时刻的压力值;gi(d)为随距离改变的相关系数函数,当i为传感器监测点所在的子网格时gi(d)为1,其余子网格均符合g(d)分布,g(d)为指数衰减函数;d为两个子网格之间的距离。In the formula, t is a certain moment; i is the number of sub-grids, which are labeled in the order of rows; H t, i is the service life of the i-th sub-grid at time t; N t is the service life of the rotary joint to be tested at time t Torque, the torque value of all sub-grids at time t is the same; f is the corresponding law of input and output index data; S t, i is the speed of the i-th sub-grid at time t; D t, i is the i-th sub-grid at time t T t, i is the temperature value of the i-th sub-grid at time t; P t, i is the pressure value of the i-th sub-grid at time t; g i (d) is the correlation coefficient function that changes with distance, When i is the sub-grid where the sensor monitoring point is located, g i (d) is 1, and the rest of the sub-grids conform to g(d) distribution, and g(d) is an exponential decay function; d is the distance between two sub-grids distance.
进一步地,两个子网格的距离d计算公式具体为:Further, the formula for calculating the distance d between two sub-grids is specifically:
式中,p和q表示3维空间中任意两个子网格;pj和qj分别表示p和q在三维空间中的对应分量。In the formula, p and q represent any two sub-grids in 3-dimensional space; p j and q j represent the corresponding components of p and q in 3-dimensional space, respectively.
进一步地,由对应法则f和每个子网格的输入指标数据预测出每个子网格的使用寿命Ht,i。Further, the service life H t,i of each sub-grid is predicted from the corresponding rule f and the input index data of each sub-grid.
有益效果:本发明与现有技术相比,具有以下优点:Beneficial effect: compared with the prior art, the present invention has the following advantages:
(1)与传统旋转接头利用传感器进行整体监测的方式不同的是,本发明将旋转接头主体部分在空间中进行三维网格划分,划分成若干子网格,对每个子网格的状态进行评估预测,既能了解到旋转接头整体的情况,又能对局部细节进行观察,获得的信息更全面。(1) Unlike traditional rotary joints that use sensors for overall monitoring, the present invention divides the main part of the rotary joint into three-dimensional grids in space, divides them into several sub-grids, and evaluates the state of each sub-grid Prediction can not only understand the overall situation of the rotary joint, but also observe the local details, and obtain more comprehensive information.
(2)采集的指标数据包括了5种不同传感器获取的指标,能从多个角度反应旋转接头的实际情况,用多种指标数据有助于提升预测的准确度。(2) The collected index data includes indexes obtained by five different sensors, which can reflect the actual situation of the rotary joint from multiple angles, and the use of various index data can help improve the accuracy of prediction.
(3)通过多传感器采集旋转接头的指标数据并建立寿命预测模型,并考虑到不同子网格的指标随距离的变化情况,给出了基于多贝叶斯估计预测回归器,用于预测各个子网格的使用寿命,将最小使用寿命作为旋转接头整体使用寿命有利于减少故障发生的概率。(3) The index data of the rotary joint is collected by multiple sensors and the life prediction model is established. Taking into account the changes of the index of different sub-grids with the distance, a predictive regressor based on multi-Bayesian estimation is given to predict each The service life of the subgrid, taking the minimum service life as the overall service life of the rotary joint is beneficial to reduce the probability of failure.
附图说明Description of drawings
图1为本发明的基于多传感器融合的旋转接头寿命预测系统框图;Fig. 1 is the block diagram of the rotary joint life prediction system based on multi-sensor fusion of the present invention;
图2为本发明的基于多传感器融合的旋转接头寿命预测系统的传感器示意图;Fig. 2 is the sensor schematic diagram of the rotary joint life prediction system based on multi-sensor fusion of the present invention;
图3为本发明的基于多传感器融合的旋转接头寿命预测方法流程图。Fig. 3 is a flow chart of the life prediction method of a rotary joint based on multi-sensor fusion in the present invention.
具体实施方式Detailed ways
下面结合附图和具体实施例,进一步阐明本发明,本实施例在以本发明技术方案为前提下进行实施,应理解这些实施例仅用于说明本发明而不用于限制本发明的范围。The present invention will be further illustrated below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.
如图1所示,本发明公开一种基于多传感器融合的旋转接头寿命预测系统,包括网格划分模块1、多传感器数据采集模块2、数据库模块3、模型建立模块4、寿命预测模块5和结果分析模块6。As shown in Figure 1, the present invention discloses a life prediction system for rotary joints based on multi-sensor fusion, including a grid division module 1, a multi-sensor data acquisition module 2, a database module 3, a model building module 4, a life prediction module 5 and Results analysis module6.
其中,网格划分模块1实现对旋转接头在空间中进行三维网格状划分,对每个子网格的坐标进行标号。Among them, the grid division module 1 realizes three-dimensional grid division of the rotary joint in space, and labels the coordinates of each sub-grid.
多传感器数据采集模块2对多传感器设定数据采集频率,按照数据采集频率进行旋转接头指标数据采集,并将旋转接头指标数据存储至数据库模块3内;如图2所示,传感器包括采集转速S的速度传感器、采集扭矩N的扭矩传感器、采集使用寿命H的计时器、采集振动信号V的振动传感器、采集介质温度T的温度传感器和采集介质压力P的压力传感器。The multi-sensor data acquisition module 2 sets the data acquisition frequency for the multi-sensor, collects the index data of the rotary joint according to the data acquisition frequency, and stores the index data of the rotary joint into the database module 3; as shown in Figure 2, the sensor includes acquisition speed S Speed sensor, torque sensor to collect torque N, timer to collect service life H, vibration sensor to collect vibration signal V, temperature sensor to collect medium temperature T, and pressure sensor to collect medium pressure P.
模型建立模块4调取数据库模块3内所有旋转接头指标数据并中进行训练,得到使用寿命与其他指标之间的关系,从而获得训练好的旋转接头寿命预测模型。The model building module 4 retrieves all the index data of the rotary joint in the database module 3 and conducts training in it to obtain the relationship between the service life and other indexes, so as to obtain a well-trained rotary joint life prediction model.
寿命预测模块5保存训练好的旋转接头寿命预测模型,将待预测的旋转接头指标数据输入寿命预测模块5,可以得到每个旋转接头子网格的使用寿命。The life prediction module 5 saves the trained rotary joint life prediction model, and inputs the index data of the rotary joint to be predicted into the life prediction module 5 to obtain the service life of each rotary joint subgrid.
结果分析模块6排序所有子网格的使用寿命,筛选出所有子网格中使用寿命最短的作为旋转接头整体使用寿命。The result analysis module 6 sorts the service life of all sub-grids, and selects the one with the shortest service life among all sub-grids as the overall service life of the rotary joint.
本发明还公开一种基于多传感器融合的旋转接头寿命预测方法,基于上述系统实现,对旋转接头在空间中进行三维网格状划分,同时对多传感器设定数据采集频率,按照数据采集频率进行旋转接头指标数据采集,建立数据库,随后建立旋转接头寿命预测模型,预测每个子网格的使用寿命,最后对所有子网格使用寿命取最小值,作为旋转接头整体使用寿命。如图3所示,具体包括以下步骤:The invention also discloses a life prediction method for rotary joints based on multi-sensor fusion. Based on the realization of the above-mentioned system, the three-dimensional grid-like division of the rotary joint in space is carried out, and at the same time, the data collection frequency is set for multiple sensors, and the data collection frequency is carried out according to the data collection frequency. Collect the index data of the rotary joint, establish the database, and then establish the life prediction model of the rotary joint to predict the service life of each sub-grid, and finally take the minimum value of the service life of all sub-grids as the overall service life of the rotary joint. As shown in Figure 3, it specifically includes the following steps:
步骤S1:利用网格划分模块1对旋转接头在空间中进行三维网格状划分,对每个子网格的坐标进行标号。Step S1: Use the mesh division module 1 to divide the rotary joint into a three-dimensional grid in space, and label the coordinates of each sub-grid.
划分范围至少应包含旋转接头主体部分,可以保证旋转接头绝大部分的空间都能进行预测,以旋转接头主体部分直径的十分之一的长度为边长进行三维立方体网格划分,对每个子网格按照行和列坐标进行标号。划分子网格的边长越小,最终预测的效果越好,旋转接头每个部分的情况了解的越清楚。The division range should at least include the main part of the rotary joint, which can ensure that most of the space of the rotary joint can be predicted. The three-dimensional cube grid is divided with the length of one tenth of the diameter of the main part of the rotary joint as the side length. Grids are numbered by row and column coordinates. The smaller the side length of the sub-grid, the better the final prediction effect, and the clearer the situation of each part of the rotary joint is known.
步骤S2:采用多传感器数据采集模块2对多传感器设定数据采集频率,按照数据采集频率进行旋转接头指标数据采集,并将旋转接头指标数据存储至数据库模块3内。Step S2: Use the multi-sensor data acquisition module 2 to set the data acquisition frequency for the multi-sensors, collect the index data of the rotary joint according to the data acquisition frequency, and store the index data of the rotary joint in the database module 3 .
在多传感器数据采集模块2中,设定采集的指标及采集频率为:转速S每0.1s、扭矩N每0.1s、使用寿命H每1s、振动信号V每0.1s、介质温度T每0.1s、介质压力P每0.1s。所采集到的振动信号V通过如下公式转换成声音信号D的分贝值;In the multi-sensor data acquisition module 2, the collection index and collection frequency are set as follows: speed S every 0.1s, torque N every 0.1s, service life H every 1s, vibration signal V every 0.1s, medium temperature T every 0.1s , Medium pressure P every 0.1s. The collected vibration signal V is converted into the decibel value of the sound signal D by the following formula;
式中,a为振动加速度总体平均值;为放大系数;D为声音分贝平均值,单位为dB;a由振动传感器获取。In the formula, a is the overall average value of the vibration acceleration; is the amplification factor; D is the average sound decibel, in dB; a is obtained by the vibration sensor.
在数据库模块3中,将旋转接头指标数据中的使用寿命H作为输出预测值,其余的指标作为预测模型输入值,数据持续记录至旋转接头完全损坏后10小时,记录足够多的性能完好到完全损坏的指标数据有利于后续回归预测器进行拟合,使后续的预测结构更为准确。In the database module 3, the service life H in the index data of the rotary joint is used as the output prediction value, and the rest of the indexes are used as the input value of the prediction model. Damaged index data is beneficial to the fitting of the subsequent regression predictor, making the subsequent prediction structure more accurate.
步骤S3:采用模型建立模块4调取数据库模块3内所有旋转接头指标数据并中进行训练,得到使用寿命与其他指标之间的关系,从而获得训练好的旋转接头寿命预测模型,保存在寿命预测模块5中;Step S3: Use the model building module 4 to retrieve all the rotary joint index data in the database module 3 and perform training in it to obtain the relationship between the service life and other indicators, so as to obtain the trained rotary joint life prediction model and save it in the life prediction In module 5;
构建旋转接头寿命预测表达式,具体表达式为:Construct the life prediction expression of the rotary joint, the specific expression is:
Ht,i=f{St,i,Nt,gi(d)·Dt,i,gi(d)·Tt,i,Pt,i}H t, i = f{S t, i , N t , g i (d) D t, i , g i (d) T t, i , P t, i }
式中,t为某一时刻;i为第几个子网格,按照行的顺序进行标号;Ht,i为第i个子网格t时刻的使用寿命;Nt为待测旋转接头t时刻的扭矩,所有子网格t时刻的扭矩值一样;f为输入输出指标数据的对应法则;St,i为第i个子网格t时刻的转速;Dt,i为第i个子网格t时刻的声音分贝;Tt,i为第i个子网格t时刻的温度值;Pt,i为第i个子网格t时刻的压力值;gi(d)为随距离改变的相关系数函数,当i为传感器监测点所在的子网格时gi(d)为1,其余子网格均符合g(d)分布,g(d)为指数衰减函数;d为两个子网格之间的距离。In the formula, t is a certain moment; i is the number of sub-grids, which are labeled in the order of rows; H t, i is the service life of the i-th sub-grid at time t; N t is the service life of the rotary joint to be tested at time t Torque, the torque value of all sub-grids at time t is the same; f is the corresponding law of input and output index data; S t, i is the speed of the i-th sub-grid at time t; D t, i is the i-th sub-grid at time t T t, i is the temperature value of the i-th sub-grid at time t; P t, i is the pressure value of the i-th sub-grid at time t; g i (d) is the correlation coefficient function that changes with distance, When i is the sub-grid where the sensor monitoring point is located, g i (d) is 1, and the rest of the sub-grids conform to g(d) distribution, and g(d) is an exponential decay function; d is the distance between two sub-grids distance.
两个子网格的距离d采用欧式距离公式得到,计算公式具体为:The distance d between two sub-grids is obtained using the Euclidean distance formula, and the specific calculation formula is:
式中,p和q表示3维空间中任意两个子网格;pj和qj分别表示p和q在三维空间中的对应分量。In the formula, p and q represent any two sub-grids in 3-dimensional space; p j and q j represent the corresponding components of p and q in 3-dimensional space, respectively.
由对应法则f和每个子网格的输入指标数据预测出每个子网格的使用寿命Ht,i。输入输出指标数据的对应法则f由以下多贝叶斯回归预测器产生,该预测器可以根据输入指标数据,预测出所需指标数据,预测结果为实际的物理量,多贝叶斯回归预测器具体表达式为:The service life H t,i of each sub-grid is predicted by the corresponding law f and the input index data of each sub-grid. The corresponding rule f of the input and output index data is generated by the following multi-Bayesian regression predictor. The predictor can predict the required index data according to the input index data. The predicted result is the actual physical quantity. The multi-Bayesian regression predictor is specific The expression is:
式中,x1,...,x5为旋转接头输入指标数据;y为旋转接头输出待预测指标数据;为旋转接头输出预测指标数据;P(y|x1,...,x5)为在x1,...,x5均发生的情况下y发生的概率;P(y)为y发生的概率。In the formula, x 1 ,..., x 5 are the input index data of the rotary joint; y is the output index data to be predicted of the rotary joint; Output predictor data for rotary joints; P(y|x 1 ,...,x 5 ) is the probability of y occurrence when x 1 ,...,x 5 all occur; P(y) is y occurrence The probability.
步骤S4:将待预测的旋转接头指标数据输入寿命预测模块5,可以得到每个旋转接头子网格的使用寿命,结果分析模块6排序所有子网格的使用寿命,筛选出所有子网格中使用寿命最短的作为旋转接头整体使用寿命,具体表达式为:Step S4: Input the index data of the rotary joint to be predicted into the life prediction module 5, and the service life of each rotary joint sub-grid can be obtained, and the result analysis module 6 sorts the service life of all sub-grids, and screens out all sub-grids The shortest service life is regarded as the overall service life of the rotary joint, and the specific expression is:
式中,Ht,i为第i个子网格t时刻的预测使用寿命;Ht min为t时刻其中的最小使用寿命。In the formula, H t,i is the predicted service life of the i-th sub-grid at time t; H t min is the minimum service life of it at time t.
本发明与传统旋转接头利用传感器进行整体监测的方式不同的是,该方法将旋转接头主体部分在空间中进行三维网格划分,划分成若干子网格,对每个子网格的状态进行评估预测。采集的指标数据包括了5种不同传感器获取的指标,能从多个角度反应旋转接头的实际情况。通过多传感器采集旋转接头的指标数据并建立寿命预测模型,并考虑到不同子网格的指标随距离的变化情况,给出了基于多贝叶斯估计预测回归器,用于预测各个子网格的使用寿命。本发明能及时预测旋转接头的使用寿命,更好的了解旋转接头的情况,让企业做出合理的生产计划。The difference between the present invention and the traditional rotary joint using sensors for overall monitoring is that the method divides the main part of the rotary joint into a three-dimensional grid in space, divides it into several sub-grids, and evaluates and predicts the state of each sub-grid . The collected indicator data includes indicators acquired by five different sensors, which can reflect the actual situation of the rotary joint from multiple angles. The index data of the rotary joint is collected by multiple sensors and the life prediction model is established. Taking into account the change of the index of different sub-grids with the distance, a predictive regressor based on multi-Bayesian estimation is given to predict each sub-grid service life. The invention can timely predict the service life of the rotary joint, better understand the situation of the rotary joint, and allow enterprises to make reasonable production plans.
上述具体实施方式只是本发明的一个优选实施例,并不是用来限制本发明的实施与权利要求范围的,凡依据本发明申请专利保护范围内容做出的等效变化和修饰,均应包括于本发明专利申请范围内。The specific implementation described above is only a preferred embodiment of the present invention, and is not used to limit the implementation of the present invention and the scope of the claims. All equivalent changes and modifications made according to the content of the patent protection scope of the present invention should be included in the Within the scope of the patent application of the present invention.
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