RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling
Abstract
:1. Introduction
- In view of the problem that it is difficult to accurately characterize the running state with a single running feature, multi-information entropy feature extraction is carried out for vibration signals, and multi-entropy indicators are used to improve the characterization ability and provide a basis for the subsequent construction of HI.
- At present, the extracted feature indicators in some studies have too much information, which belongs to high-dimensional feature information, excessive redundant information, and difficult screening. In traditional feature use, it is easy to cause the omission of important feature information or the calculation time is too long, and it is difficult to ensure the accuracy and overall feature extraction. In this paper, DAE was used for multi-information entropy fusion and dimensionality reduction, HI was constructed to evaluate the running state of bearings, and significant root mean square (RMS) value jump time was found as the failure threshold of HI to evaluate the running state of bearings.
- Aiming at the problem of large amounts of calculation and time required when LSTM deals with long sequence problems, the powerful feature extraction ability of CAE is used to extract the unsupervised features of vibration signals, and the hidden features are input into BiLSTM for RUL prediction.
2. Theoretical Backgrounds
2.1. Denoising Autoencoder
2.2. Convolutional Autoencoder
2.3. Bidirectional Long Short Term Memory Network
2.4. Multi-Information Selection Indicator
2.5. Health Index Construction Method
- (1)
- Bearing lifetime vibration acceleration signal is obtained through bearing data set.
- (2)
- Take the first 2048 frequency points of each data set as the training samples of the model.
- (3)
- The characteristic indexes of ReEn, ApEn, PeEn, FuEn, DisEn, and SDEn are calculated for the intercepted training samples.
- (4)
- Adam was selected as the optimizer and the DAE was trained using the training set. The loss function was as follows:
- (5)
- When the loss value stops falling, it can be judged that the training is complete, and an encoder capable of effectively extracting features and dimensionality reduction can be obtained.
- (1)
- The data at the initial moment of the data set are stacked after multi-entropy feature index calculation, and input into the trained DAE to obtain the bearing characteristics in the health state.
- (2)
- Calculate the multi-entropy feature indicators of the data at the current time, and also input these indicators into the trained DAE to obtain the degraded state characteristics of the bearing under the current state.
- (3)
- Calculate the Bray–Curtis distance between the health state feature and the degraded state feature [28], and take these index data as the running state HI of the bearing.
2.6. Failure Threshold Construction Method
- (1)
- Obtain the HI value of the current moment and then insert it into the HI sequence in chronological order.
- (2)
- In order to obtain a smoother HI sequence, the moving average filter is used to filter the current HI sequence.
- (3)
- Calculate the RMS value of the HI sequence at the current moment, and compare the HI value at the current moment with the failure threshold (4); if the failure threshold is reached, the bearing has reached the degradation stage. Set the HI value at this time as the failure threshold of the bearing. The RMS calculation formula is as follows:
2.7. RUL Model Based on CAE-BiLSTM
- (1)
- Firstly, the data are preprocessed, and after the data are normalized, the time domain data are converted into envelope spectrum data by Hilbert yellow transformation for model training.
- (2)
- Initialize the parameters of the CAE-BiLSTM network, process the data, adjust the input data according to the characteristics of the network model, and prepare to train the network. According to the test data, the remaining four data sets under the same working conditions are used as the training set.
- (3)
- Conduct network training, construct loss function, calculate loss function value, and optimize the network through optimization algorithm.
- (4)
- Determine whether the maximum number of iterations is reached. If the maximum number of iterations is reached, the training is completed; otherwise, return to step (3).
- (5)
- Save the trained model parameters, input the test data into the trained CAE-BiLSTM network, and obtain the prediction results.
3. Results
3.1. Multi-Entropy Extraction Process
3.2. Construction of Health Indicator and Failure Threshold
3.3. Results of RUL Prediction
- (1)
- First, the order of the predicted data is divided into stationary trend terms and random covectors .
- (2)
- The EWMA is calculated as follows:
4. Discussion
- (1)
- Calculate the residual Er between the real value of the data and the predicted value:
- (2)
- Divide the obtained residual data Er into the first half Era and Erb, and the data length of each part is half of the length of the whole residual data.
- (3)
- Score A is calculated by traversing the two parts of the split residual data, and the calculated score is added to the respective arrays grade_a and grade_b. The calculation rules are as follows:
- (4)
- Calculate the mean and of the two score arrays, and obtain the total score Score by weighting.
5. Conclusions
- (1)
- In this paper, the running state monitoring of rolling bearings is taken as the research goal, and the multi-entropy fusion method is adopted to make up for the poor information representation in the single entropy feature. The multi-entropy feature index of the XJTU-SY data set was extracted, and the multi-entropy feature was fused and dimensionality reduced by CAE.
- (2)
- After dimensionality reduction, the health data of the fusion index in the early stage are regarded as the normal operation data, and the operation data at the current time are extracted. After that, the Bray–Curtis distance method is used to construct HI, and the RMS jump time of HI is set as the failure threshold. Comparative analysis of the test results shows that the proposed method can accurately evaluate the running state of the rolling bearing and accurately locate the failure time. Finally, the frequency spectrum of the original data is analyzed by fast Fourier transform, and the failure time is again located in accordance with the monitoring method in this paper, which verifies the effectiveness and superiority of the proposed method.
- (3)
- Aiming at the time-consuming problem of label selection in the RUL process, a CAE-BiLSTM unsupervised model is proposed to convert XJTU-SY data into envelope spectrum data for model training and testing. The RUL curve of the model was smooched by EWMA to reduce the fluctuation of sample points. The analysis of the test results showed that the RUL of most data sets could be realized except for the data set whose life information of the health stage was stable at a high level. Finally, the performance evaluation index is introduced to compare the proposed model, and the results show that the proposed method has good prediction accuracy and robustness.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Working Condition | Data Set | Sample Count | Basic Rated Life | Actual Life | Fault Type |
---|---|---|---|---|---|
1 | Bearing1_1 | 123 | 5.600–9.677 h | 2 h 3 min | Outer ring |
Bearing1_2 | 161 | 2 h 41 min | Outer ring | ||
Bearing1_3 | 158 | 2 h 32 min | Outer ring | ||
Bearing1_4 | 122 | 2 h 2 min | Cage | ||
Bearing1_5 | 52 | 52 min | Outer ring, Inner ring | ||
2 | Bearing2_1 | 491 | 6.786–11.726 h | 8 h 14 min | Inner ring |
Bearing2_2 | 161 | 2 h 41 min | Outer ring | ||
Bearing2_3 | 533 | 8 h 38 min | Cage | ||
Bearing2_4 | 339 | 5 h 39 min | Outer ring | ||
Bearing2_5 | 42 | 42 min | Outer ring | ||
3 | Bearing3_1 | 2538 | 8.468–14.632 h | 42 h 18 min | Outer ring |
Bearing3_2 | 2496 | 41 h 36 min | Inner ring, Outer ring, Cage, rolling body | ||
Bearing3_3 | 371 | 6 h 11 min | Inner ring | ||
Bearing3_4 | 1515 | 25 h 15 min | Inner ring | ||
Bearing3_5 | 114 | 1 h 54 min | Outer ring |
CAE-BiLSTM | CAE | BiLSTM | |||||||
---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | Score | RMSE | MAE | Score | RMSE | MAE | Score | |
Bearing1_1 | 0.1286 | 0.1061 | 0.9978 | 0.3189 | 0.2845 | 0.9901 | 0.2674 | 0.1664 | 0.9871 |
Bearing1_2 | 0.1227 | 0.0979 | 0.9967 | 0.4861 | 0.4257 | 0.9878 | 0.2784 | 0.1983 | 0.9886 |
Bearing1_3 | 0.2303 | 0.1865 | 0.9977 | 0.4057 | 0.3745 | 0.9831 | 0.2458 | 0.2238 | 0.9926 |
Bearing1_4 | 0.5411 | 0.4601 | 0.9931 | 0.7851 | 0.7032 | 0.9786 | 0.5987 | 0.5542 | 0.9884 |
Bearing1_5 | 0.2198 | 0.1734 | 0.9970 | 0.3571 | 0.3020 | 0.9889 | 0.2978 | 0.2049 | 0.9912 |
Bearing2_1 | 0.4114 | 0.3362 | 0.9947 | 0.5479 | 0.4204 | 0.9801 | 0.4687 | 0.4297 | 0.9857 |
Bearing2_2 | 0.1988 | 0.1668 | 0.9972 | 0.2354 | 0.1876 | 0.9902 | 0.2025 | 0.1691 | 0.9924 |
Bearing2_3 | 0.1745 | 0.1552 | 0.9967 | 0.3470 | 0.3057 | 0.9898 | 0.2671 | 0.2125 | 0.9935 |
Bearing2_4 | 0.2088 | 0.1726 | 0.9912 | 0.4251 | 0.3584 | 0.9851 | 0.4114 | 0.3259 | 0.9854 |
Bearing2_5 | 0.1796 | 0.1412 | 0.9964 | 0.2249 | 0.1754 | 0.9924 | 0.2003 | 0.1155 | 0.9945 |
Bearing3_1 | 0.2864 | 0.2466 | 0.9939 | 0.3932 | 0.3230 | 0.9912 | 0.2871 | 0.2410 | 0.9910 |
Bearing3_2 | 0.3997 | 0.3340 | 0.9900 | 0.5048 | 0.4125 | 0.9856 | 0.4125 | 0.3699 | 0.9849 |
Bearing3_3 | 0.1540 | 0.1138 | 0.9977 | 0.3574 | 0.3061 | 0.9934 | 0.2579 | 0.2197 | 0.9918 |
Bearing3_4 | 0.2907 | 0.2491 | 0.9906 | 0.3334 | 0.3091 | 0.9845 | 0.3082 | 0.2596 | 0.9890 |
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Guan, P.; Zhang, T.; Zhou, L. RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling. Processes 2024, 12, 1831. https://doi.org/10.3390/pr12091831
Guan P, Zhang T, Zhou L. RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling. Processes. 2024; 12(9):1831. https://doi.org/10.3390/pr12091831
Chicago/Turabian StyleGuan, Peng, Tianrui Zhang, and Lianhong Zhou. 2024. "RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling" Processes 12, no. 9: 1831. https://doi.org/10.3390/pr12091831