CN117213856A - Bearing residual life prediction method based on Yun Bian double-data-source fusion - Google Patents
Bearing residual life prediction method based on Yun Bian double-data-source fusion Download PDFInfo
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Abstract
The invention discloses a cloud edge double-data-source fusion-based bearing residual life prediction method, belongs to the field of equipment health management and maintenance, and is realized by a Yun Bian double-data-source fusion-based bearing residual life prediction system. The method comprises the following steps: step one: performing wavelet denoising treatment on the historical degradation data of the bearing; step two: extracting, analyzing and selecting time domain and frequency domain degradation characteristics of the denoising signal; step three: the horizontal and vertical degradation characteristics are constructed into horizontal and vertical training sets; step four: normalizing operation; training a transducer model; step six: vibration signals collected in real time; step seven: constructing a degradation characteristic test set; step eight: and fusing according to the double-error weighted-DS evidence to obtain the residual life predicted value of the bearing. The method can effectively solve the problems that the existing bearing has low residual service life prediction accuracy and does not consider the real-time prediction, and ensures the real-time state monitoring and service life prediction of the bearing.
Description
Technical Field
The invention discloses a method for predicting the residual life of a bearing based on Yun Bian double-data-source fusion, belongs to the field of equipment health management and maintenance, and is particularly suitable for predicting the residual life of the bearing based on Yun Bian double-data-source fusion.
Background
With the rapid development of science and technology and the continuous innovation of engineering technology, more and more rotating mechanical devices currently play roles in life production. The bearing is used as an important component in rotary machinery and is widely applied to various fields of aerospace, rail transit and the like, and if the bearing is degraded or damaged, real-time condition monitoring and treatment are not carried out, more serious equipment damage is often caused. In addition, with the rapid development of industrial internet of things technology, data generated by rotating machinery is particularly prominent, and data adopted for monitoring the state of a bearing accounts for 40%. The huge data is uploaded to the cloud server in real time, so that the network can face huge flow pressure, and meanwhile, the server can hardly guarantee timeliness of bearing state monitoring when processing massive data. How to perform health status monitoring and residual life prediction (Remaining Useful Life, RUL) on important bearings in rotating machinery in real time is a problem to be solved in the research field.
The traditional bearing RUL method mainly predicts from the angles of failure mechanism and statistical probability, and has the defects of difficult modeling and missing training data, so that the bearing RUL method has great difficulty. With the development of artificial intelligence technology, a new information technology method based on data driving is widely applied to a bearing RUL. According to the method, the characteristic value representing the degradation state of the bearing is extracted to serve as the predicted covariant, so that the residual life of the bearing is accurately predicted. The current bearing RUL method based on data driving mainly has two forms: (1) only taking an original signal as input, and utilizing models such as a time convolution network, a deep belief network and the like to mine deep feature learning degradation modes; (2) and extracting characteristics representing degradation trend from the original signal, and inputting the characteristics into a deep learning model for prediction.
The above method (1) requires a large amount of data for training and adjustment, and it is difficult to sufficiently extract degradation features from the original data. For the method of extracting degradation features from the original signal, followed by bearing RUL, a recurrent neural network (Recursive Neural Network, RNN) is applied to the RUL with its significant timing signal processing capability. However, when time series data is processed, the RNN serial operation mode seriously reduces the operation speed, and the capturing capability of the RNN on the long time series dependency relationship is weak, so that gradient disappearance and gradient explosion are easy to generate. The transducer model realizes parallel computation and long-distance feature capture which cannot be achieved by RNN through the position coding and multi-head self-attention mechanism, and reduces operation time while improving prediction accuracy.
In summary, aiming at the problems that the prediction accuracy of the residual service life of the current rolling bearing is not high and the real-time prediction is not considered, how to guarantee real-time state monitoring and service life prediction of the bearing is the key point and difficulty of the research in the field.
Disclosure of Invention
The invention aims to provide a Cloud-edge (Cloud-Edge Collaborative Computing, CECC) double-data-source fusion (Data Sources Fusion, DSF) based bearing residual life prediction method, which aims to utilize real-time performance of Cloud and edge calculation linkage, reliability of a two-dimensional sensor and accuracy of a transducer model so as to improve prediction accuracy and reduce operation time, and solve the problems that the existing bearing residual service life prediction accuracy is not high and prediction real-time performance is not considered.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the function of the bearing residual life prediction method based on Yun Bian double-data-source fusion is realized by a bearing residual life prediction system based on Yun Bian double-data-source fusion. The cloud edge double-data source fusion-based bearing residual life prediction system comprises a cloud server, a horizontal direction edge computing device, a vertical direction edge computing device, a horizontal vibration sensor and a vertical vibration sensor, and is characterized in that the horizontal vibration sensor and the vertical vibration sensor are respectively arranged in the horizontal direction and the vertical direction of a bearing and used for detecting the vibration condition of the bearing; the horizontal direction edge computing device and the vertical direction edge computing device are respectively connected with the horizontal vibration sensor and the vertical vibration sensor and are used for acquiring vibration signals uploaded by the horizontal vibration sensor and the vertical vibration sensor in real time and extracting characteristics; the horizontal direction edge computing device and the vertical direction edge computing device are provided with network communication modules, and can be connected with a cloud server network.
The horizontal direction edge computing device and the vertical direction edge computing device are computers which are based on a CPU or an FPGA and are provided with memories.
The method for predicting the residual life of the bearing based on Yun Bian double-data-source fusion is characterized by comprising the following steps of:
step one: the cloud server performs wavelet denoising processing on the bearing history degradation data;
step two: the cloud server extracts time domain and frequency domain degradation characteristics of the denoising signals and performs characteristic analysis and selection;
step three: the cloud server respectively constructs the screened horizontal and vertical degradation characteristics into a horizontal training set and a vertical training set;
step four: the cloud server normalizes the horizontal training set, the vertical training set and the labels;
step five: the cloud server respectively trains the horizontal transducer model and the vertical transducer model by utilizing the training set and the labels;
step six: vibration signals acquired in real time by the horizontal vibration sensor and the vertical vibration sensor are respectively transmitted to the horizontal direction edge computing device and the vertical direction edge computing device;
step seven: the horizontal direction edge computing device and the vertical direction edge computing device respectively conduct denoising processing and degradation characteristic extraction on the horizontal vibration signal and the vertical vibration signal, construct a degradation characteristic test set and upload the degradation characteristic test set to the cloud server in real time;
step eight: and the cloud server respectively inputs the horizontal and vertical degradation characteristic test sets into a trained horizontal and vertical transducer model to predict the residual life of the horizontal and vertical signal bearings, and fuses the horizontal and vertical signal bearings according to double-error weighted-DS evidence to obtain the residual life prediction value of the bearings.
Further, the steps one to five may be processed in an off-line manner; the sixth to eighth steps are on-line processing modes, and real-time performance can be improved by parallel computing.
Further, the wavelet denoising processing in the step one is to denoise the historical degradation data by using a wavelet threshold denoising method, specifically: and 5 layers of discrete wavelet decomposition taking a plurality of Bei Xisi-order wavelets as mother wavelets is carried out on the original noise-containing signal, and the detail coefficients are subjected to threshold processing and wavelet reconstruction so as to remove noise and retain information capable of representing degradation characteristics.
Further, the degradation feature extraction and selection in the second step specifically includes: 27 characteristic parameters of a time domain and a frequency domain are extracted from the denoised signal, then the characteristics of good monotonicity, trending and robustness of good degradation characteristics are obtained according to expert priori knowledge, the characteristics are subjected to monotonicity, trending and robustness analysis, and the proper degradation characteristic parameters are selected to comprise ten degradation characteristics of standard deviation, variance, peak-to-peak value, root mean square, maximum value, absolute average value, waveform factor, margin factor, frequency root mean square and frequency average value.
Further, the training set normalization in the fourth step adopts maximum and minimum normalization; the label normalizes to be the ratio of remaining life to full life, satisfies the one-time function relation with bearing operating time.
Furthermore, the horizontal transducer model and the vertical transducer model are composed of modules such as position coding, a multi-head attention mechanism and the like, the mean square error is used as a loss function, an Adam optimizer is selected for training and optimizing the models, and the learning rate is set to be 0.0001.
Further, the merging of the double-error weighted-DS evidence in the step eight is specifically as follows:
(1) Calculating a root mean square error (Root Mean Square Error, RMSE) and an average absolute error (Mean Absolute Error, MAE) of the horizontal signal remaining life prediction result and the vertical signal remaining life prediction result, respectively;
(2) Based on the root mean square error, calculating the initial weight of the root mean square error Wherein Rmse x Rmse, root mean square error, as a result of the residual life prediction of the horizontal signal y Root mean square error as a result of the vertical signal residual life prediction;
(3) Based on the average absolute error, calculating the initial weight of the root mean square error Based on the average absolute error, mae of x Average absolute error of residual life prediction result for horizontal signal Mae y Average absolute error of the vertical signal residual life prediction result;
(4) Calculating horizontal and vertical fusion weights using dual error weighted-DS evidence
(5) Obtaining the residual life prediction value pred of the bearing by fusing evidence i ＝pred x ·Rul xi +pred y ·Rul yi The method comprises the steps of carrying out a first treatment on the surface of the Therein Rul xi 、Rul yi Pred, the prediction of the remaining life of the horizontal and vertical signals, respectively i Is a residual life prediction value of the bearing.
The invention has the beneficial effects that: the invention provides a bearing residual life prediction method based on Yun Bian double-data source fusion, which combines the characteristics of high real-time performance of edge calculation, high data processing capacity of an artificial intelligence technology, high reliability of a multi-sensor technology and the like to realize RUL prediction, can effectively solve the problems of low accuracy of predicting the residual service life of the existing bearing, and no consideration of the real-time performance of prediction, and ensures real-time state monitoring and life prediction of the bearing.
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In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a frame diagram of a bearing residual life prediction system based on cloud edge double data source fusion, which is provided by the invention;
FIG. 2 is a flow chart of a method for predicting the residual life of a bearing based on cloud edge double data source fusion;
FIG. 3 shows the predicted LSTM results of example 1 of the present invention;
fig. 4 shows the prediction effect of the method for predicting the residual life of the bearing based on Yun Bian dual data source fusion in example 1 of the present invention.
Detailed Description
The following experimental embodiments of the present invention are described by specific examples, and those skilled in the art can easily understand the advantages of the present invention from the contents of the present specification. The invention is capable of other and different embodiments, and its several details are capable of modification and variation in various respects, all without departing from the spirit of the present invention.
Example 1: the institute of IEEE reliability Association and FEMTO-ST has organized an IEEE PHM2012 data challenge race. The challenge is to estimate the remaining life of the bearing, which is a critical issue, as most failures of rotating machines and these components greatly impact the availability, safety and cost-effective systems and equipment of the machine's industries such as electricity and traffic.
Aiming at PHM2012 data sets, the invention provides a 'Yun Bian double-data-source-fusion-based bearing residual life prediction method', which is realized by a cloud-edge double-data-source-fusion-based bearing residual life prediction system, and in combination with fig. 1, the cloud-edge double-data-source-fusion-based bearing residual life prediction system comprises: the cloud server comprises a cloud server (1), a horizontal direction edge computing device (2), a vertical direction edge computing device (3), a horizontal vibration sensor (4) and a vertical vibration sensor (5), and is characterized in that the horizontal vibration sensor (4) and the vertical vibration sensor (5) are composed of two micro accelerometers which are mutually positioned to be 90 degrees, the micro accelerometers are respectively arranged on a vertical shaft and a horizontal shaft of a bearing outer ring along the radial direction, the signal sampling frequency is set to be 25.6kHz, the sampling interval is 10s, the sampling time of each time is 0.1s, and 2560 vibration data are acquired by one sampling. The horizontal direction edge computing device (2) and the vertical direction edge computing device (3) are respectively connected with the horizontal vibration sensor (4) and the vertical vibration sensor (5) and are used for acquiring vibration signals uploaded by the horizontal vibration sensor (4) and the vertical vibration sensor (5) in real time and extracting characteristics; the horizontal edge computing device (2) and the vertical edge computing device (3) are provided with network communication modules, and can be connected with a cloud server (1) in a network mode. The horizontal direction edge computing device (2) and the vertical direction edge computing device (3) use AMD Ryzen 5 3600 6-Core Processor (main frequency 3.6 GHz) 64-bit operating system, and the memory is 8GB; the cloud server (1) is a super computer, and the internal memory is 6TB, and is an Intra Xeon Gold 6330 28core (main frequency 2.0 GHz) 56 thread.
The embodiment is realized under a Tensorflow deep learning framework.
In connection with fig. 2, the method comprises the steps of:
s1: the cloud server (1) performs wavelet denoising processing on the bearing history degradation data;
s2: the cloud server (1) extracts time domain and frequency domain degradation characteristics of the denoising signals and performs characteristic analysis and selection;
s3: the cloud server (1) respectively constructs the screened horizontal and vertical degradation characteristics into a horizontal training set and a vertical training set;
s4: the cloud server (1) normalizes the horizontal training set, the vertical training set and the labels;
s5: the cloud server (1) respectively trains the horizontal transducer model and the vertical transducer model by utilizing the training set and the labels;
s6: vibration signals acquired in real time by the horizontal vibration sensor (4) and the vertical vibration sensor (5) are respectively transmitted to the horizontal edge computing device (2) and the vertical edge computing device (3);
s7: the horizontal direction edge computing device (2) and the vertical direction edge computing device (3) respectively conduct denoising processing and degradation characteristic extraction on the horizontal vibration signal and the vertical vibration signal, construct a degradation characteristic test set and upload the degradation characteristic test set to the cloud server (1) in real time;
s8: the cloud server (1) respectively inputs the horizontal degradation characteristic test set and the vertical degradation characteristic test set into a trained horizontal transducer model and a trained vertical transducer model to predict the residual life of the horizontal signal bearing and the residual life of the vertical signal bearing, and fuses the horizontal signal bearing and the vertical signal bearing according to double-error weighting-DS evidence to obtain a residual life prediction value of the bearing.
In step S2:
the denoising signal is to denoise a fourth group of full life degradation data set with radial force of 4000N,180 r/min, the data set has 1428 samples in total, each sample has a horizontal vibration signal and a vertical vibration signal, and the horizontal acceleration vibration signal and the vertical acceleration vibration signal contain effective information representing degradation characteristics.
In step S3:
judging the bearing fault generating point to be at 1085 group according to prior expert knowledge, and training the model by taking 60% of the data of the complete degradation period generated by the fault as a training set of the model, namely taking 1085 to 1287 groups of data as the training set.
In step S4:
taking the actual residual life as a training and testing label y, setting the label to be 0 to 1, wherein the label 1 represents that the bearing is intact and unused, and the label 0 represents that the bearing is completely invalid. The data set is composed of 1428 groups of data, when the sample is the 1300 th group of data, the label of the residual life of the bearing is 1-1300/1428= 0.0896, and the like, so as to construct the label of the residual life of the rolling bearing.
In step S5:
setting the time step of a transducer model as 1, optimizing loss of a training process by using an Adam optimizer in an experimental process, and setting the learning rate as 0.0001.
In step S8:
the double-error weighting-DS evidence fusion specifically comprises the following steps:
(1) Calculating a root mean square error (Root Mean Square Error, RMSE) and an average absolute error (Mean Absolute Error, MAE) of the horizontal signal remaining life prediction result and the vertical signal remaining life prediction result, respectively;
(2) Based on the root mean square error, calculating the initial weight of the root mean square error Wherein Rmse x Rmse, root mean square error, as a result of the residual life prediction of the horizontal signal y Root mean square error as a result of the vertical signal residual life prediction;
(3) Based on the average absolute error, calculating the initial weight of the root mean square error Based on the average absolute error, mae of x Average absolute error of residual life prediction result for horizontal signal Mae y Average absolute error of the vertical signal residual life prediction result;
(4) Calculating horizontal and vertical fusion weights using dual error weighted-DS evidence
(5) Obtaining the residual life prediction value pred of the bearing by fusing evidence i ＝pred x ·Rul xi +pred y ·Rul yi The method comprises the steps of carrying out a first treatment on the surface of the Therein Rul xi 、Rul yi Predictive junctions for remaining life of horizontal and vertical signals, respectivelyFruit, pred i Is a residual life prediction value of the bearing.
Table 1 comparative performance of inventive example 1
In order to better demonstrate the advantages of the method, the LSTM model of the single-dimension signal is adopted to predict the residual life of the bearing in the PHM2012 data set as a control group, the prediction result is shown in figure 3, and meanwhile, the prediction result of the bearing residual life prediction method based on Yun Bian double-data source fusion is shown in figure 4. Further, the results shown in table 1 were obtained by comparing the root mean square error, the average absolute error and the calculation time.
Therefore, the method for predicting the residual life of the bearing based on cloud-edge double-data source fusion is far ahead in accuracy and efficiency.
Example 2: in order to better demonstrate the advantages of double data source fusion, on the basis of the embodiment 1, data of a single horizontal signal, a single vertical signal and double data sources are respectively processed, and the method for predicting the residual life of the bearing based on Yun Bian double data source fusion is realized by a system for predicting the residual life of the bearing based on cloud edge double data source fusion.
It is particularly emphasized that the steps and system descriptions in this embodiment are the same as those in embodiment 1, and further description is omitted here.
In contrast, in step S3:
dividing a data set into a training set and a testing set by an experiment, judging that the bearing fault generating point is at 1085 group according to priori expert knowledge, and training the model by taking 60% of the data of the complete degradation period generated by the fault as the training set of the model, namely, taking 1085 group to 1287 group of data of the data as the training set; the rest 40% data, namely 141 groups of data, are used as a test set of the cloud edge cooperative computing model for prediction.
In step S4:
taking the actual residual life as a training and testing label y, setting the label to be 0 to 1, wherein the label 1 represents that the bearing is intact and unused, and the label 0 represents that the bearing is completely invalid.
In step S8:
and respectively outputting prediction results of the single horizontal signal, the single vertical signal and the data of the double data sources, wherein the fusion of the double data sources is respectively performed by fusing the single horizontal signal and the single vertical signal with the horizontal weight and the vertical weight of 0.5, and the fusion of double-error weighted-DS evidence is performed. The results are shown in Table 2, and it can be seen that the predicted results of the dual data sources are better than those of the single data, and the fusion of dual error weighted-DS evidence is better than the fixed weight fusion.
Table 2 comparison of fusion experiment results in example 2
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (7)
1. The method for predicting the residual life of the bearing based on Yun Bian double-data-source fusion is realized by a Yun Bian double-data-source fusion-based bearing residual life prediction system, and the cloud edge double-data-source fusion-based bearing residual life prediction system comprises the following steps: cloud server (1), horizontal direction edge computing device (2), vertical direction edge computing device (3), horizontal vibration sensor (4), vertical vibration sensor (5), its characterized in that: the horizontal vibration sensor (4) and the vertical vibration sensor (5) are respectively arranged in the horizontal direction and the vertical direction of the bearing and are used for detecting the vibration condition of the bearing; the horizontal direction edge computing device (2) and the vertical direction edge computing device (3) are respectively connected with the horizontal vibration sensor (4) and the vertical vibration sensor (5) and are used for acquiring vibration signals uploaded by the horizontal vibration sensor (4) and the vertical vibration sensor (5) in real time and extracting characteristics; the horizontal edge computing device (2) and the vertical edge computing device (3) are provided with network communication modules, and can be connected with a cloud server (1) in a network manner; the method comprises the following steps:
step one: the cloud server (1) performs wavelet denoising processing on the bearing history degradation data;
step two: the cloud server (1) extracts time domain and frequency domain degradation characteristics of the denoising signals and performs characteristic analysis and selection;
step three: the cloud server (1) respectively constructs the screened horizontal and vertical degradation characteristics into a horizontal training set and a vertical training set;
step four: the cloud server (1) normalizes the horizontal training set, the vertical training set and the labels;
step five: the cloud server (1) respectively trains the horizontal transducer model and the vertical transducer model by utilizing the training set and the labels;
step six: vibration signals acquired in real time by the horizontal vibration sensor (4) and the vertical vibration sensor (5) are respectively transmitted to the horizontal edge computing device (2) and the vertical edge computing device (3);
step seven: the horizontal direction edge computing device (2) and the vertical direction edge computing device (3) respectively conduct denoising processing and degradation characteristic extraction on the horizontal vibration signal and the vertical vibration signal, construct a degradation characteristic test set and upload the degradation characteristic test set to the cloud server (1) in real time;
step eight: the cloud server (1) respectively inputs the horizontal degradation characteristic test set and the vertical degradation characteristic test set into a trained horizontal transducer model and a trained vertical transducer model to predict the residual life of the horizontal signal bearing and the residual life of the vertical signal bearing, and fuses the horizontal signal bearing and the vertical signal bearing according to double-error weighting-DS evidence to obtain a residual life prediction value of the bearing.
2. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the first to fifth steps are processed in an off-line mode; and the sixth to eighth steps are on-line processing modes, and real-time performance is improved by parallel calculation.
3. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the step one of wavelet denoising is to denoise historical degradation data by adopting a wavelet threshold denoising method, and specifically comprises the following steps: and 5 layers of discrete wavelet decomposition taking a plurality of Bei Xisi-order wavelets as mother wavelets is carried out on the original noise-containing signal, and the detail coefficients are subjected to threshold processing and wavelet reconstruction so as to remove noise and retain information capable of representing degradation characteristics.
4. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the degradation characteristic extraction and selection specifically comprises the following steps: 27 characteristic parameters of a time domain and a frequency domain are extracted from the denoised signal, then the characteristics of good monotonicity, trending and robustness of good degradation characteristics are obtained according to expert priori knowledge, the characteristics are subjected to monotonicity, trending and robustness analysis, and the proper degradation characteristic parameters are selected to comprise ten degradation characteristics of standard deviation, variance, peak-to-peak value, root mean square, maximum value, absolute average value, waveform factor, margin factor, frequency root mean square and frequency average value.
5. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the training set normalization adopts maximum and minimum normalization; the label normalizes to be the ratio of remaining life to full life, satisfies the one-time function relation with bearing operating time.
6. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the horizontal transducer model and the vertical transducer model use mean square error as a loss function, and an Adam optimizer is selected to train and optimize the models.
7. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the merging of the double-error weighted-DS evidence in the step eight is specifically as follows:
(1) Calculating a root mean square error (Root Mean Square Error, RMSE) and an average absolute error (Mean Absolute Error, MAE) of the horizontal signal remaining life prediction result and the vertical signal remaining life prediction result, respectively;
(2) Based on the root mean square error, calculating the initial weight of the root mean square error Wherein Rmse x Rmse, root mean square error, as a result of the residual life prediction of the horizontal signal y Root mean square error as a result of the vertical signal residual life prediction;
(3) Based on the average absolute error, calculating the initial weight of the root mean square error Based on the average absolute error, mae of x Average absolute error of residual life prediction result for horizontal signal Mae y Average absolute error of the vertical signal residual life prediction result;
(4) Calculating horizontal and vertical fusion weights using dual error weighted-DS evidence
(5) Obtaining the residual life prediction value pred of the bearing by fusing evidence i ＝pred x ·Rul xi +pred y ·Rul yi The method comprises the steps of carrying out a first treatment on the surface of the Therein Rul xi 、Rul yi Pred, the prediction of the remaining life of the horizontal and vertical signals, respectively i Is a residual life prediction value of the bearing.
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| CN117556261B (en) * | 2024-01-08 | 2024-05-14 | 浙江大学 | MCNN-based diaphragm pump check valve service life prediction method and system |
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