Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
Abstract
:1. Introduction
- The proposed bearing RUL prediction method is an end-to-end prediction method that does not rely on manual experience and sets the degradation threshold. The original data are input into the designed model, and the results of the bearing’s RUL are obtained directly.
- A multi-branch convolutional neural network and a BiLSTM network are built for the extraction of bearing degradation features without traditional feature extraction.
- The advantages of the proposed method over other methods are verified on a publicly available bearing degradation dataset.
2. Theoretical Background
2.1. CNN Network
2.2. LSTM Network
2.3. Attention Mechanism
3. Proposed Method
- Step 1. The fast Fourier transform is performed on the bearings’ originally vibrating signal to acquire the frequency domain signal, dividing the dataset into training set and test set based on the original signal and frequency domain signal. With the aim of improving the speed of training convergence of the network, the original vibration signal and the frequency domain signal are normalized. The data are preprocessed with the Z-Score normalization method. After normalization, the data exhibit a distribution with a mean of 1 and a standard deviation of 0. The equation is shown as follows:
- Step 2. According to the RUL “end-to-end” prediction method, the bearing RUL labels are normalized and mapped to the range of 0–1, as follows:
- Step 3. The data are divided according to the length of the set time window, and the data in each time window have a time order.
- Step 4. Model training. According to the MBCNN-BiLSTM model structure, the training hyperparameters are configured. The optimization algorithm of back propagation is used in the training period to optimize the network weights according to the loss function taking the minimum value, i.e., the minimum error of RUL between the true and the predicted.
- Step 5. Model evaluation. The data from the test set are input to the trained model to acquire the prediction results. The RUL prediction evaluation metrics are calculated according to the predicted and true values to get the RUL prediction performance of the model.
4. Experimental Study
4.1. Experimental Data Presentation
4.2. Experimental Parameter Setting
- The CPU is [email protected] GHz with 32 GB of memory;
- The GPU is NVIDIA Quadro P2200 with 5 GB of video memory;
- The operating system is Windows 10;
- The programming language is Python 3.10, which is implemented on the basis of the pytorch2.0 deep learning framework, and the experiments use the GPU to accelerate the speed of training.
4.3. Performance Evaluation Metrics
4.4. Experimental Comparison Analysis
4.4.1. Experiments and Analysis of Different Models
4.4.2. Experiments and Analysis with Different Existing Methods
4.5. Noise Resistance Test
4.6. Visualization Analysis
5. Conclusions
- The proposed method is an end-to-end bearing RUL prediction method, in which the original data are directly input into the network for RUL prediction without manual extraction of degradation features;
- The spatial feature extraction of the bearing input data is realized by MBCNN, and then BiLSTM further mines the data for temporal features. The GAM attentional mechanism is more conducive to focusing on the key information in the features, which improves the prediction accuracy of the bearings’ RUL;
- It has been shown that the average absolute error and root mean square error of prediction are reduced by “22.2%” to “50.0%” and “26.1%” to “52.8%”, respectively, compared with some existing prediction methods, which proves the effectiveness and feasibility of the proposed method. It is proved that the method proposed in this paper can reduce the prediction error in bearing RUL and has certain advantages over other methods.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Conditions 1 | Conditions 2 | Conditions 3 | |
---|---|---|---|
Radial force/N | 4000 | 4200 | 5000 |
Rotational Speed/ | 1800 | 1650 | 1500 |
Bearing dataset | B1_1 | B2_1 | B3_1 |
B1_2 | B2_2 | B3_2 | |
B1_3 | B2_3 | B3_3 | |
B1_4 | B2_4 | - | |
B1_5 | B2_5 | - | |
B1_6 | B2_6 | - | |
B1_7 | B2_7 | - |
Structure Name | Parameter | Activation Function |
---|---|---|
Convolutional layer 1 | (16, 64, 16) (16, 64, 8) | ReLu |
Convolutional layer 2 | (16, 3, 1) (16, 3, 1) | ReLu |
Convolutional layer 3 | (32, 3, 1) (32, 3, 1) | ReLu |
Convolutional layer 4 | (64, 3, 1) (64, 3, 1) | ReLu |
Convolutional layer 5 | (64, 3, 1) (64, 3, 1) | ReLu |
Fully connected layer 1 | 128 | ReLu |
Connect | - | - |
BiLSTM layer | Hidden size 16 | tanh |
GAM layer | - | - |
Fully connected layer 2 | 100 | ReLu |
Output layer | 1 | - |
Model | MAE | RMSE | Score |
---|---|---|---|
CNN | 0.09 | 0.10 | 0.43 |
BiLSTM | 0.12 | 0.16 | 0.50 |
CNN-BiLSTM | 0.07 | 0.08 | 0.51 |
MBCNN-BiLSTM | 0.06 | 0.07 | 0.63 |
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Li, J.; Huang, F.; Qin, H.; Pan, J. Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM. Appl. Sci. 2023, 13, 7706. https://doi.org/10.3390/app13137706
Li J, Huang F, Qin H, Pan J. Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM. Applied Sciences. 2023; 13(13):7706. https://doi.org/10.3390/app13137706
Chicago/Turabian StyleLi, Jian, Faguo Huang, Haihua Qin, and Jiafang Pan. 2023. "Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM" Applied Sciences 13, no. 13: 7706. https://doi.org/10.3390/app13137706