Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment
Abstract
:1. Introduction
- More formative vibration signals, used for training the fault prediction model, are collected from multiple sensors to improve the accuracy of the prediction method;
- Deep features of varies degradation periods and fault types can be extracted by CNN and LSTM automatically without relying on manual intervention and professional knowledge;
- The degradation period and fault type can be predicted simultaneously in advance with high accuracy.
2. Problem Formulation and Main Fault Prediction Framework
- In the feature extraction stage, the original vibration signals collected by multiple sensors are sent to the CNN-LSTM network for the extraction of spatiotemporal features, which contain operating status information;
- In the prediction stage, the attention-Bi-LSTM is trained to predict the trend of the features;
- In the classification stage, based on the spatiotemporal features and their trends, the SVC model is formulated to identify the degradation period and the future fault type.
3. Deep Learning Network-Based Three-Stage Fault Prediction
3.1. Feature Extraction Stage
3.2. Prediction Stage
3.3. Classification Stage
3.4. Implementing the Proposed Fault Prediction Strategy
Algorithm 1 Fault prediction algorithm |
|
4. Validating the Proposed Method
4.1. The Description of the Dataset
4.2. Feature Extraction
4.2.1. Training Process
4.2.2. Verifying the Validity of the Feature Extraction Model
4.3. Trend Prediction
4.3.1. Training Process
4.3.2. Testing Results
4.3.3. Comparison with Other Models
4.4. Classification
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Bearing | Fault Type | Sensor Number |
---|---|---|---|
1-1 | - | 2 | |
dataset1 | 1-2 | - | 2 |
1-3 | inner race defect | 2 | |
1-4 | roller element defect | 2 |
File Numbers | Period | Norm | Slight | Severe | Failure |
---|---|---|---|---|---|
Bearing | |||||
1-3 H | 1–1850 | 1851–2119 | 2120–2151 | 2152–2156 | |
1-3 V | 1–1850 | 1851–2119 | 2120–2151 | 2152–2156 | |
1-4 H | 1–1600 | 1601–2128 | 2129–2151 | 2152–2156 | |
1-4 V | 1–1600 | 1601–2128 | 2129–2151 | 2152–2156 |
Layer | Type | Kernel Size/Stride/Numbers | Activation Function | Padding | BN |
---|---|---|---|---|---|
1-1 | Sensor H | - | - | - | N |
1-2 | Sensor V | - | - | - | N |
2-1 | 1D Convolution | 64/16/16 | RELU | same | Y |
2-2 | 1D Convolution | 64/16/16 | RELU | same | Y |
3-1 | 1D Maxpooling | 2/2 | - | valid | N |
3-2 | 1D Maxpooling | 2/2 | - | valid | N |
4-1 | 1D Convolution | 32/8/32 | RELU | same | Y |
4-2 | 1D Convolution | 32/8/32 | RELU | same | Y |
5-1 | LSTM | 100 | Tanh/Sigmoid | - | N |
5-2 | LSTM | 100 | Tanh/Sigmoid | - | N |
6-1 | LSTM | 40 | Tanh/Sigmoid | - | N |
6-2 | LSTM | 40 | Tanh/Sigmoid | - | N |
7 | Concatenate | - | - | - | N |
8 | Dense1 | 128 | - | - | N |
9 | Dense2 | 10(feature) | - | - | N |
10 | Softmax | - | - | - | N |
11 | Cross-entropy | - | - | - | N |
Sensor Type | Accuracy |
---|---|
Sensor H | 0.892 |
Sensor V | 0.832 |
Sensor H and Sensor V | 0.928 |
Layer | Units | Activation Function |
---|---|---|
Input | - | - |
Bi-LSTM | 100 | Tanh/Sigmoid |
Attention | - | - |
Dense | 75 | RELU |
Dense | 1 | - |
Input | RMSE |
---|---|
Three inputs | 2.221 |
Six inputs | 1.818 |
Nine inputs | 1.906 |
Algorithm | RMSE |
---|---|
LSTM | 1.875 |
Bi-LSTM | 1.828 |
Attention-LSTM | 1.838 |
Attention-Bi-LSTM | 1.818 |
Window Size × the Number of Windows | The Accuracy of Fault Mode Prediction | Sampling Time (s) | Fault Prediction Time (s) |
---|---|---|---|
4096 × 5 | 0.8 | 0.2 | 0.244 |
2048 × 10 | 0.86 | 0.1 | 0.248 |
256 × 80 | 0.944 | 0.0125 | 0.255 |
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Peng, H.; Li, H.; Zhang, Y.; Wang, S.; Gu, K.; Ren, M. Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment. Entropy 2022, 24, 164. https://doi.org/10.3390/e24020164
Peng H, Li H, Zhang Y, Wang S, Gu K, Ren M. Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment. Entropy. 2022; 24(2):164. https://doi.org/10.3390/e24020164
Chicago/Turabian StylePeng, Huaqing, Heng Li, Yu Zhang, Siyuan Wang, Kai Gu, and Mifeng Ren. 2022. "Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment" Entropy 24, no. 2: 164. https://doi.org/10.3390/e24020164
APA StylePeng, H., Li, H., Zhang, Y., Wang, S., Gu, K., & Ren, M. (2022). Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment. Entropy, 24(2), 164. https://doi.org/10.3390/e24020164