CN109992872A - A kind of mechanical equipment method for predicting residual useful life based on stacking separation convolution module - Google Patents
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Abstract
A kind of mechanical equipment method for predicting residual useful life based on stacking separation convolution module, first obtains the original vibration signal of mechanical equipment under different operating conditions, is pre-processed, and establishes the mechanical equipment predicting residual useful life model based on stacking separation convolution module；Using stacking separation convolution module extract original vibration signal in and the maximally related high-level characteristic feature of mechanical equipment health status；Then high-level characteristic feature is input to fully-connected network, obtains the predicting residual useful life value of mechanical equipment；The mean square error objective function for constructing predicting residual useful life model obtains optimal predicting residual useful life model by Adam optimization algorithm iteration update prediction model to training parameter；Pretreated mechanical equipment vibration signal is recently entered, mechanical equipment is completed and obtains predicting residual useful life；The present invention has the advantages that precision of prediction is higher, stability is more preferable, robustness is stronger.
Description
Technical Field
The invention belongs to the technical field of residual life prediction of mechanical equipment, and particularly relates to a residual life prediction method of the mechanical equipment based on a stacked separation convolution module.
Background
With the increase of informatization level and industrial automation level, modern mechanical equipment is developing towards high performance, high precision and high efficiency. Because the equipment often works under more complicated working conditions, the failure occurrence rate of mechanical equipment is higher, and the equipment is difficult to operate safely and reliably. Therefore, the residual life of the mechanical equipment needs to be predicted, so that an effective early maintenance scheme is formulated, and the normal operation of the equipment is ensured. Due to the reasons of large number of monitoring points of the equipment, high sampling frequency, long data collection duration and the like, the mechanical health monitoring enters a big data era, and a serious challenge is brought to the residual life prediction of mechanical equipment. Therefore, a new method for predicting the residual life of the data-driven mechanical equipment needs to be invented to ensure the safe service of the mechanical equipment.
By introducing a deep learning theory, the method for predicting the residual service life of the mechanical equipment based on data driving can effectively mine degradation information of the mechanical equipment from monitoring data, and a good prediction result is obtained. However, the current method for predicting the remaining life of mechanical equipment based on data driving needs to manually extract features from vibration signals, and the process not only needs professional signal processing knowledge and rich expert experience, but also needs a lot of labor cost. Meanwhile, the monitoring data of the mechanical equipment is acquired through a plurality of sensors, and the data of different sensors contains the degradation information of the mechanical equipment with different degrees, so that the interaction mechanism among different component faults is reflected. However, the current method ignores the correlation between different sensor data, so that the most relevant information to the health condition of the mechanical equipment cannot be effectively extracted, and the accuracy of the residual life prediction result of the mechanical equipment under the background of big mechanical data is seriously influenced.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for predicting the residual life of mechanical equipment based on a stacked separation convolution module, which directly excavates information most relevant to the health condition of the mechanical equipment from a vibration signal of the mechanical equipment, automatically extracts high-level typical characteristics and predicts the residual life of the mechanical equipment by using the high-level typical characteristics; the method has the advantages of higher prediction precision, better stability and stronger robustness.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for predicting the residual life of mechanical equipment based on a stacked separation convolution module comprises the following steps:
1) obtaining original vibration signals of mechanical equipment under different working conditionsWherein,m is the number of signal samples, N is the number of data points contained in each vibration signal sample, and C is the number of vibration sensors; meanwhile, constructing a prediction model of a laminated separation convolution module with a deep structure;
2) for original vibration signalCarrying out pretreatment: firstly, to the vibration signal xiPerforming Z-score normalization processing, and then embedding x by a time window embedding strategyiEmbedding previous validity time information data into xiThe method comprises the following specific steps:
2.1) the vibration signal x is corrected by Z-scoreiCarrying out normalization processing to obtain monitoring data
The Z-score normalized operation expression is shown below:
in the formula, x is original data;is the mean of the original data; sigma is the standard deviation of the original data; x is the number of*Is Z-scoreNormalized data;
2.2) setting the time window size S, integrating data by time window embedding strategyAnd S-1 vibration signal samples before the vibration signal sample to obtain dataNamely, it is
3) Stacking T separate convolution modules from the preprocessed original vibration signalThe method comprises the following steps of extracting high-level typical characteristics most relevant to the health condition of mechanical equipment:
3.1) input samplesRespectively using channel convolution kernel kcwAnd a point convolution kernel kpwAnd input samplesPerforming convolution to obtain characteristics
3.2) features of the pairsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
3.3) characterization ofInputting the data into T stacked separate convolution modules with residual connection, and extracting high-level typical features most relevant to the health condition of mechanical equipment, wherein each separate convolution module is specifically realized in the following way:
(a) firstly, processing the input of a separation convolution module by using a pre-activation strategy, and sequentially passing through a batch normalization layer and a linear rectification function (ReLU) layer;
the batch normalization layer operation expression is as follows:
in the formula, xl-1Is the input of the separation convolution module; y isl-1The output is the output after batch normalization; mu.sB、Are respectively input xl-1γ, β are reconstruction parameters that the normalization layer can learn;
(b) inputting the data subjected to the pre-activation operation in the step (a) into a separation convolution layer, and extracting deep expression characteristics of the data
The separation convolution includes convolution in the channel direction and convolution in the point direction, and the calculation expression is as follows:
in the formula,the data after the pre-activation operation is obtained;is the output of the convolution in the channel direction;is the output of the convolution in the direction of the point; k is a radical ofcw、Represents the convolution kernel and bias in the channel direction; k is a radical ofpw、Represents the convolution kernel and bias in the direction of the point; c represents the input channel of the c convolution operation; n represents the output channel of the nth convolution operation;
(c) performing the steps (a) and (b) again, and further extracting deep expression characteristics of the dataLearning correlations between different sensor data;
(d) for the extracted deep expression characteristicsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
(e) Will pool intoLatter featureInputting the data into a characteristic calibration layer, and performing characteristic response recalibration through compression operation and self-calibration operation to obtain characteristics most relevant to the health state of the mechanical equipment;
the compression operation refers to global average pooling, and the computational expression is as follows:
in the formula, H represents the global length of the input of the compressed excitation layer;an input representing a compressed excitation layer;
the self-calibration operation refers to estimating the information quantity of each channel by using an adaptive door mechanism, generating the weight of the corresponding channel, and calculating the expression as follows:
wherein, sigma (·) and delta (·) are respectively Sigmoid and ReLU activation functions; wherein r is the ratio of dimensionality reduction, and C is the number of channels;
(f) finally, the separation convolution module adopts residual connection, and the output calculation expression is as follows:
xl＝xl-1+F(xl-1,Wl)
in the formula, xl-1Is the input of the l-th layer, xlIs the output of the l-th layer; f (-) is a residual function, and the expression is as follows:
F(xl-1):＝H(xl-1)-xl-1
in the formula, H (x)l-1) Is a desired mapping;
4) inputting the extracted high-level typical features into a full-connection network to obtain a service life predicted value of the mechanical equipment, wherein the specific steps are as follows;
4.1) performing global average pooling on the extracted typical features to ensure that the output feature maps only contain one element, and obtaining the pooled features
4.2) post-pooling featureLay flat into one-dimensional vectorComputing a remaining life of a mechanical device preRUL using a fully connected networki；
5) Based on Adam optimization algorithm, repeating steps 3) and 4), setting iteration times N, and iteratively updating parameters of the stacked separation convolution module and the fully-connected network to obtain an optimal residual life prediction model, namely a minimum mean square error objective function:
in the formula, yiThe actual residual life value of the mechanical equipment;
6) the preprocessed mechanical equipment vibration signal is transmittedAnd inputting the residual life into an optimal residual life prediction model to predict the residual life of the mechanical equipment.
The invention has the beneficial effects that:
the method can directly extract high-level typical characteristics from the original vibration signals, and considers the correlation of different sensor data in typical characteristic learning, so that the degradation information most relevant to the health condition of mechanical equipment can be accurately mined. The method overcomes the defects that the traditional method excessively depends on expert experience and domain knowledge and can not effectively extract the information most relevant to the health condition of the mechanical equipment, and realizes accurate prediction of the residual life of the mechanical equipment under the condition of large mechanical data.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram showing the basic components of the separation convolution module.
FIG. 3 is a diagram illustrating the predicted residual life of the rolling bearing under three operating conditions of the embodiment.
FIG. 4 is a graph showing a comparison of the predicted residual life of the rolling bearing of the embodiment.
Detailed Description
The invention is explained in further detail below with reference to the drawings.
Referring to fig. 1, a method for predicting the remaining life of mechanical equipment based on a stacked separation convolution module includes the following steps:
1) obtaining original vibration signals of mechanical equipment under different working conditionsWherein,m is the number of signal samples, N is the number of data points contained in each vibration signal sample, and C is the number of vibration sensors; meanwhile, constructing a prediction model of a laminated separation convolution module with a deep structure;
2) for original vibration signalCarrying out pretreatment: firstly, to the vibration signal xiPerforming Z-score normalization processing, and then embedding x by a time window embedding strategyiEmbedding previous validity time information data into xiThe method comprises the following specific steps:
2.1) the vibration signal x is corrected by Z-scoreiCarrying out normalization processing to obtain monitoring data
The Z-score normalized operation expression is shown below:
in the formula, x is original data;is the mean of the original data; sigma is the standard deviation of the original data; x is the number of*Data normalized for Z-score;
2.2) setting the time window size S, integrating data by time window embedding strategyAnd S-1 vibration signal samples before the vibration signal sample to obtain dataNamely, it is
3) Stacking T separate convolution modules as shown in FIG. 2 from the preprocessed raw vibration signalThe method comprises the following steps of extracting high-level typical characteristics most relevant to the health condition of mechanical equipment:
3.1) input samplesRespectively using channel convolution kernel kcwAnd a point convolution kernel kpwAnd input samplesPerforming convolution to obtain characteristics
The convolution operation includes convolution in the channel direction and convolution in the point direction, featuresCan be calculated from the following convolution expression:
yc＝kcw*xc+bc
in the formula, xcThe vibration signal data is preprocessed; y iscIs the output of the convolution in the channel direction; z is a radical ofnIs the output of the convolution in the direction of the point; k is a radical ofcw、bcRepresents the convolution kernel and bias in the channel direction; k is a radical ofpw、bnRepresents the convolution kernel and bias in the direction of the point; c denotes the c-th convolution operationAn input channel is made; n represents the output channel of the nth convolution operation;
3.2) features of the pairsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
The pooled features may be calculated by the following expression:
xAP＝pool(xcp,p,s)
in the formula, xcpRepresenting the features after convolution; x is the number ofAPRepresenting pooled features; pool (·) represents a downsampling function; p, s are respectively the size of the pooling and the sliding step length;
3.3) characterization ofInputting the data into T stacked separate convolution modules with residual connection, and extracting high-level typical features most relevant to the health condition of mechanical equipment, wherein each separate convolution module is specifically realized in the following way:
(a) firstly, processing the input of a separation convolution module by using a pre-activation strategy, and sequentially passing through a batch normalization layer and a linear rectification function (ReLU) layer;
the batch normalization layer operation expression is as follows:
in the formula, xl-1Is the input of the separation convolution module; y isl-1The output is the output after batch normalization; mu.sB、Are respectively input xl-1γ, β are reconstruction parameters that the normalization layer can learn;
(b) inputting the data subjected to the pre-activation operation in the step (a) into a separation convolution layer, and extracting deep expression characteristics of the data
The separation convolution includes convolution in the channel direction and convolution in the point direction, and the calculation expression is as follows:
in the formula,the data after the pre-activation operation is obtained;is the output of the convolution in the channel direction;is the output of the convolution in the direction of the point; k is a radical ofcw、Represents the convolution kernel and bias in the channel direction; k is a radical ofpw、Represents the convolution kernel and bias in the direction of the point; c represents the input channel of the c convolution operation; n represents the output channel of the nth convolution operation;
(c) performing the steps (a) and (b) again, and further extracting deep expression characteristics of the dataLearning correlations between different sensor data;
(d) for the extracted deep expression characteristicsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
(e) Characteristics after poolingInputting the data into a characteristic calibration layer, and performing characteristic response recalibration through compression operation and self-calibration operation to obtain characteristics most relevant to the health state of the mechanical equipment;
the compression operation refers to global average pooling, and the computational expression is as follows:
in the formula, H represents the global length of the input of the compressed excitation layer;an input representing a compressed excitation layer;
the self-calibration operation refers to estimating the information quantity of each channel by using an adaptive door mechanism, generating the weight of the corresponding channel, and calculating the expression as follows:
wherein, sigma (·) and delta (·) are respectively Sigmoid and ReLU activation functions; wherein r is the ratio of dimensionality reduction, and C is the number of channels;
(f) finally, the separation convolution module adopts residual connection, and the output calculation expression is as follows:
xl＝xl-1+F(xl-1,Wl)
in the formula, xl-1Is the input of the l-th layer, xlIs the output of the l-th layer; f (-) is a residual function, and the expression is as follows:
F(xl-1):＝H(xl-1)-xl-1
in the formula, H (x)l-1) Is a desired mapping;
4) inputting the extracted high-level typical features into a full-connection network to obtain a service life predicted value of the mechanical equipment, wherein the specific steps are as follows;
4.1) performing global average pooling on the extracted typical features to ensure that the output feature maps only contain one element, and obtaining the pooled features
4.2) post-pooling featureLay flat into one-dimensional vectorComputing a remaining life of a mechanical device preRUL using a fully connected networki；
5) Based on Adam optimization algorithm, repeating steps 3) and 4), setting iteration times N, and iteratively updating parameters of the stacked separation convolution module and the fully-connected network to obtain an optimal residual life prediction model, namely a minimum mean square error objective function:
in the formula, yiThe actual residual life value of the mechanical equipment;
6) the preprocessed mechanical equipment vibration signal is transmittedAnd inputting the residual life into an optimal residual life prediction model to predict the residual life of the mechanical equipment.
Example (b): the effectiveness of the method is verified by taking a rolling bearing in mechanical equipment as a case and based on the experimental data of the accelerated life of the rolling bearing. The data set of the acceleration life experiment of the rolling bearing adopted in the embodiment comprises 3 subsets which respectively correspond to three different working conditions, namely 12kN/2100rpm, 11kN/2250rpm and 10kN/2400 rpm. Wherein, every operating mode contains 5 antifriction bearing's full life cycle vibration signal. As shown in table 1, when the method of the present invention is used to predict the remaining life of a rolling bearing, the first 4 rolling bearing data under each working condition are used as a training data set, and the last 1 rolling bearing data are used as a test data set.
The parameter settings of the prediction model of the stacked separation convolution module are as follows: the number T of the separation convolution modules is 3; the time window S is 5; the size of the convolution kernel in the channel direction is 8, and the number of the convolution kernels is 16; the size and the step length of the pooling area are set to be 4; the dimensionality reduction ratio is set to 16; 3 layers of separation convolution modules are adopted for stacking; the number of small batch trains is 128; the number of iterations is chosen to be 100. The method is used for predicting the residual life of the rolling bearing test data set under 3 working conditions, the prediction result is shown in figure 3, and as can be seen from figure 3, although the deviation between the real life and the predicted life of the rolling bearing at the early stage is large, the predicted life of the rolling bearing gradually approaches to the real life along with the time, which shows that the method can effectively predict the residual life of the rolling bearing. In order to further verify the superiority of the method, the method is compared with a residual life prediction method (DBN) based on a deep confidence network and a residual life prediction Method (MCNN) based on a multi-scale convolutional neural network, evaluation is carried out on the three methods by using a scoring function and a root mean square error prediction performance index, and the result is shown in FIG. 4. As can be seen from FIG. 4, in the residual life prediction of three test bearings, two prediction performance index values of the method are smaller than those of the other two prediction methods, which shows that the residual life prediction precision of the method is higher, the stability is better and the robustness is stronger.
TABLE 1
According to the method, the laminated separation convolution module is utilized, high-level typical characteristics can be directly extracted from an original vibration signal, the correlation of different sensor data in typical characteristic learning is considered, then the degradation information most relevant to the health condition of mechanical equipment can be accurately mined, the precision of predicting the residual life of the mechanical equipment is effectively improved, and more excellent prediction performance is obtained.
Claims (1)
1. A method for predicting the residual life of mechanical equipment based on a stacked separation convolution module is characterized by comprising the following steps:
1) obtaining original vibration signals of mechanical equipment under different working conditionsWherein,m is the number of signal samples and N is perThe number of data points contained in each vibration signal sample, wherein C is the number of vibration sensors; meanwhile, constructing a prediction model of a laminated separation convolution module with a deep structure;
2) for original vibration signalCarrying out pretreatment: firstly, to the vibration signal xiPerforming Z-score normalization processing, and then embedding x by a time window embedding strategyiEmbedding previous validity time information data into xiThe method comprises the following specific steps:
2.1) the vibration signal x is corrected by Z-scoreiCarrying out normalization processing to obtain monitoring data
The Z-score normalized operation expression is shown below:
in the formula, x is original data;is the mean of the original data; sigma is the standard deviation of the original data; x is the number of*Data normalized for Z-score;
2.2) setting the time window size S, integrating data by time window embedding strategyAnd S-1 vibration signal samples before the vibration signal sample to obtain dataNamely, it is
3) Stacked T discrete convolution modulesFrom the preprocessed original vibration signalThe method comprises the following steps of extracting high-level typical characteristics most relevant to the health condition of mechanical equipment:
3.1) input samplesRespectively using channel convolution kernel kcwAnd a point convolution kernel kpwAnd input samplesPerforming convolution to obtain characteristics
3.2) features of the pairsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
3.3) characterization ofInputting the data into T stacked separate convolution modules with residual connection, and extracting high-level typical features most relevant to the health condition of mechanical equipment, wherein each separate convolution module is specifically realized in the following way:
(a) firstly, processing the input of a separation convolution module by using a pre-activation strategy, and sequentially passing through a batch normalization layer and a linear rectification function (ReLU) layer;
the batch normalization layer operation expression is as follows:
in the formula, xl-1Is the input of the separation convolution module; y isl-1The output is the output after batch normalization; mu.sB、Are respectively input xl-1γ, β are reconstruction parameters that the normalization layer can learn;
(b) inputting the data subjected to the pre-activation operation in the step (a) into a separation convolution layer, and extracting deep expression characteristics of the data
The separation convolution includes convolution in the channel direction and convolution in the point direction, and the calculation expression is as follows:
in the formula,the data after the pre-activation operation is obtained;is the output of the convolution in the channel direction;is the output of the convolution in the direction of the point; k is a radical ofcw、Represents the convolution kernel and bias in the channel direction; k is a radical ofpw、Represents the convolution kernel and bias in the direction of the point; c represents the input channel of the c convolution operation; n represents the output channel of the nth convolution operation;
(c) performing the steps (a) and (b) again, and further extracting deep expression characteristics of the dataLearning correlations between different sensor data;
(d) for the extracted deep expression characteristicsPerforming average pooling to obtain average value of elements in non-overlapping pooling region, and obtaining pooled features
(e) Characteristics after poolingInputting the data into a characteristic calibration layer, and performing characteristic response recalibration through compression operation and self-calibration operation to obtain characteristics most relevant to the health state of the mechanical equipment;
the compression operation refers to global average pooling, and the computational expression is as follows:
in the formula, H represents the global length of the input of the compressed excitation layer;an input representing a compressed excitation layer;
the self-calibration operation refers to estimating the information quantity of each channel by using an adaptive door mechanism, generating the weight of the corresponding channel, and calculating the expression as follows:
wherein, sigma (·) and delta (·) are respectively Sigmoid and ReLU activation functions; wherein r is the ratio of dimensionality reduction, and C is the number of channels;
(f) finally, the separation convolution module adopts residual connection, and the output calculation expression is as follows:
xl＝xl-1+F(xl-1,Wl)
in the formula, xl-1Is the input of the l-th layer, xlIs the output of the l-th layer; f (-) is a residual function, and the expression is as follows:
F(xl-1):＝H(xl-1)-xl-1
in the formula, H (x)l-1) Is a desired mapping;
4) inputting the extracted high-level typical features into a full-connection network to obtain a service life predicted value of the mechanical equipment, wherein the specific steps are as follows;
4.1) performing global average pooling on the extracted typical features to ensure that the output feature maps only contain one element, and obtaining the pooled features
4.2) post-pooling featureLay flat into one-dimensional vectorComputing a remaining life of a mechanical device preRUL using a fully connected networki；
5) Based on Adam optimization algorithm, repeating steps 3) and 4), setting iteration times N, and iteratively updating parameters of the stacked separation convolution module and the fully-connected network to obtain an optimal residual life prediction model, namely a minimum mean square error objective function:
in the formula, yiThe actual residual life value of the mechanical equipment;
6) the preprocessed mechanical equipment vibration signal is transmittedAnd inputting the residual life into an optimal residual life prediction model to predict the residual life of the mechanical equipment.
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| CN112131760A (en) * | 2019-11-04 | 2020-12-25 | 中国人民解放军国防科技大学 | CBAM model-based prediction method for residual life of aircraft engine |
| CN112881518A (en) * | 2021-01-08 | 2021-06-01 | 东冶及策河北能源技术有限公司 | Method for predicting residual life of dynamic filter compensator |
| CN113686577A (en) * | 2021-08-17 | 2021-11-23 | 山东科技大学 | Bearing fault diagnosis method based on rapid nonlinear sparse spectrum |
| CN115048873A (en) * | 2022-08-12 | 2022-09-13 | 太原科技大学 | Residual service life prediction system for aircraft engine |
| CN116579505A (en) * | 2023-07-12 | 2023-08-11 | 中国科学院空间应用工程与技术中心 | Electromechanical equipment cross-domain residual life prediction method and system without full life cycle sample |
| CN117010116A (en) * | 2023-09-06 | 2023-11-07 | 长兴昇阳科技有限公司 | Bearing residual service life assessment method based on elastic neuron colloid |
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