A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network
Abstract
:1. Introduction
2. Methods
2.1. VGG Convolutional Neural Network (CNN)
2.2. Data Segmentation
2.3. Spectral Analysis of Short-Time Fourier Transform (STFT)
2.4. Diagnostic Methods
- Sensors installed in different locations of the equipment collect vibration signals. In this study, we used collected vibration datasets rather than real-time vibration signals.
- The collected vibration signals are processed, the appropriate length is selected as a sample and 1D data are processed by the STFT method. The processed 1D data are stored as 2D images by Matplotlib. When the dataset is multidimensional, a multichannel dataset is generated by data fusion to improve the recognition accuracy.
- The spectrum map dataset is divided into a training set and a validation set according to a certain proportion.
- Appropriate neural networks are selected for training. Finally, a VGG convolutional neural network is used to train the model on the training set to obtain the neural network prediction model of bearing faults.
- The trained model is deployed to mechanical equipment for fault detection.
3. Dataset
3.1. Dataset 1: Case Western Reserve University (CWRU) Dataset
3.2. Dataset 2: Society for Machinery Failure Prevention Technology (MFPT) Dataset
4. Experiment and Analysis
4.1. Evaluation Index and Method
4.1.1. Loss Function and Accuracy
4.1.2. Confusion Matrix
4.1.3. Clustering Analysis
4.2. CWRU Experiment Results
4.2.1. DE Single-Channel Data
4.2.2. Dual-Channel Data of DE and FE
4.2.3. Evaluation under Different Load Conditions
4.3. MFPT Experiment Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Class | Nor | IR0.007 | B0.007 | OR0.007 | IR0.014 | B0.014 | OR0.014 | IR0.021 | B0.021 | OR0.021 |
---|---|---|---|---|---|---|---|---|---|---|
Train | 2196 | 1091 | 1102 | 1097 | 1096 | 1096 | 1095 | 1098 | 1098 | 1102 |
Val | 243 | 121 | 122 | 121 | 121 | 121 | 121 | 121 | 121 | 122 |
Label | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|---|
Normal | IR0.007 | B0.007 | OR0.007 | IR0.014 | B0.014 | OR0.014 | IR0.021 | B0.021 | OR0.021 | |
Draw directly | 244 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 | 123 |
27 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
GADF | 244 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 | 123 |
27 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
MTF | 244 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 | 123 |
27 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
STFT | 244 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 | 123 |
27 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 |
Method | Train Loss | Val Loss | Train Acc | Val Acc |
---|---|---|---|---|
Draw directly | 0.171 | 0.195 | 0.936 | 0.938 |
GADF | 0.501 | 0.566 | 0.802 | 0.781 |
MTF | 0.385 | 0.51 | 0.835 | 0.797 |
STFT | 0.0 | 0.0 | 1.0 | 1.0 |
Label | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|---|
Normal | IR0.007 | B0.007 | OR0.007 | IR0.014 | B0.014 | OR0.014 | IR0.021 | B0.021 | OR0.021 | |
HP 0 | 244 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 | 123 |
27 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
HP 1 | 484 | 122 | 121 | 123 | 122 | 122 | 122 | 122 | 122 | 122 |
53 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
HP 2 | 484 | 122 | 122 | 121 | 122 | 122 | 122 | 122 | 122 | 122 |
53 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | |
HP 3 | 485 | 123 | 122 | 123 | 122 | 122 | 122 | 122 | 122 | 122 |
53 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 |
HP | Train Loss | Val Loss | Train Acc | Val Acc |
---|---|---|---|---|
HP 0 | 0.0 | 0.0 | 1.0 | 1.0 |
HP 1 | 0.002 | 0.057 | 0.999 | 0.994 |
HP 2 | 0.0 | 0.0 | 1.0 | 1.0 |
HP 3 | 0.0 | 0.0 | 1.0 | 1.0 |
Label | 0 | 1 | 2 | 3 |
---|---|---|---|---|
Baseline | Outer-Race Fault | More Outer-Race Faults | Inner-Race Fault | |
MFPT | 1756 | 1757 | 1021 | 1021 |
195 | 195 | 113 | 113 |
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Wang, B.; Feng, G.; Huo, D.; Kang, Y. A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network. Processes 2022, 10, 1426. https://doi.org/10.3390/pr10071426
Wang B, Feng G, Huo D, Kang Y. A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network. Processes. 2022; 10(7):1426. https://doi.org/10.3390/pr10071426
Chicago/Turabian StyleWang, Baiyang, Guifang Feng, Dongyue Huo, and Yuyun Kang. 2022. "A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network" Processes 10, no. 7: 1426. https://doi.org/10.3390/pr10071426