Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU
Abstract
:1. Introduce
2. Fault Feature Extraction Based on SDAE
2.1. SDAE Outlook
2.2. SDAE Model
2.3. The Process of Feature Extraction in the SDAE Method
3. Fault Prediction Model Based on AGWO-GRU
3.1. GRU Model
3.2. AGWO Algorithm
3.3. Signal Conditioning Module Fault Prediction Based on AGWO-GRU Model
- (1)
- Data acquisition: The signal conditioning module circuit is simulated and modeled using PSpice 17.4 simulation software, which allows for the identification of key components. Moreover, fault datasets are obtained during the simulation process for further analysis.
- (2)
- Data preprocessing: Normalize the collected fault data of critical components using min–max normalization.
- (3)
- Feature extraction using SDAE network: Train the normalized fault data using the SDAE network, and the output one-dimensional feature is the degradation feature of the component.
- (4)
- Constructing the fault dataset: Use the sliding window method to construct the extracted degradation features into training and testing sets that comply with the input format of the GRU model.
- (5)
- AGWO-optimized GRU model: Initialize the grey wolf population, take the learning rate of the GRU model, the number of hidden layers, and the number of hidden layer nodes as the coordinates of the position parameters of the wolves, select the training sample set to train the GRU, and obtain the position vector of the optimal α wolf which is the optimized GRU parameters.
- (6)
- Construct the GRU prediction model based on the optimized parameters, and input the testing set for prediction.
4. Simulation Modeling and Fault Prediction Verification of the Signal Conditioning Module’s Output Circuit
4.1. Simulation Modeling and Fault Data Acquisition
4.2. Fault Feature Extraction of Signal Output Circuit
4.3. Simulation Experiment Validation of AGWO-GRU Fault Prediction Model
5. Conclusions
- The SDAE-based feature extraction method proposed in this paper achieves smoother, better trending, and more reflective fault features of component degradation without relying on expert experience and complex signal processing technology, surpassing the PCA method and traditional signal feature extraction methods.
- The AGWO-GRU model proposed in this paper has a higher prediction accuracy compared to the RNN, LSTM, PF, SVM, and MKRVM models. It accurately predicts the future trend of circuit fault features and demonstrates improved stability. The AGWO algorithm optimization enhances the dynamic adaptability of the prediction model.
- The proposed model accurately predicts faults in the safety-class signal conditioning module even with limited monitoring data. It exhibits a good long-term prediction performance, providing valuable insights for the monitoring and operation and maintenance of electronic equipment in complex environments of nuclear power plants.
- The universality of SDAE-based fault feature extraction needs further verification as there may be uncertainties when using degradation data from the safety-class signal conditioning module to evaluate the performance degradation of other electronic equipment in nuclear power plants.
- Future research should consider the diversity and stage characteristics of degradation modes and further study the structure of prediction models. The focus should be on multi-component degradation faults as this study only investigated single-component degradation faults.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Load/Ω | CH1 /mA | CH2 /mA | CH3 /mA | CH4 /mA | AVG (CH1 to CH4) | Measurement Error | Accuracy | |
---|---|---|---|---|---|---|---|---|
4 mA actual value | 100 | 3.9974 | 3.9989 | 3.9984 | 3.9969 | 3.9979 | 0.2% | |
300 | 3.9981 | 3.9989 | 3.9985 | 3.9969 | 3.9981 | 0.2% | ||
600 | 3.9980 | 3.999 | 3.9985 | 3.9969 | 3.9981 | 0.2% | ||
4 mA simulation value | 100 | 3.9963 | 0.040% | 0.2% | ||||
300 | 3.9962 | 0.047% | 0.2% | |||||
600 | 3.9961 | 0.051% | 0.2% | |||||
8 mA actual value | 100 | 7.9969 | 7.9983 | 7.9976 | 7.9938 | 7.9966 | 0.2% | |
300 | 7.9974 | 7.9983 | 7.9977 | 7.9939 | 7.9968 | 0.2% | ||
600 | 7.9973 | 7.9985 | 7.9977 | 7.9938 | 7.9968 | 0.2% | ||
8 mA simulation value | 100 | 7.9925 | 0.052% | 0.2% | ||||
300 | 7.9923 | 0.057% | 0.2% | |||||
600 | 7.9920 | 0.060% | 0.2% | |||||
12 mA actual value | 100 | 11.9964 | 11.9979 | 11.9972 | 11.9908 | 11.9955 | 0.2% | |
300 | 11.997 | 11.998 | 11.9971 | 11.9907 | 11.9957 | 0.2% | ||
600 | 11.9969 | 11.9984 | 11.9971 | 11.9908 | 11.9958 | 0.2% | ||
12 mA simulation value | 100 | 11.9887 | 0.057% | 0.2% | ||||
300 | 11.9884 | 0.061% | 0.2% | |||||
600 | 11.9879 | 0.065% | 0.2% | |||||
16 mA actual value | 100 | 15.9968 | 15.998 | 15.9974 | 15.9885 | 15.9951 | 0.2% | |
300 | 15.9974 | 15.9983 | 15.9973 | 15.9885 | 15.9953 | 0.2% | ||
600 | 15.9971 | 15.9987 | 15.9973 | 15.9887 | 15.9954 | 0.2% | ||
16 mA simulation value | 100 | 15.9851 | 0.063% | 0.2% | ||||
300 | 15.9848 | 0.065% | 0.2% | |||||
600 | 15.9845 | 0.069% | 0.2% | |||||
20 mA actual value | 100 | 19.9981 | 19.999 | 19.9984 | 19.9869 | 19.9956 | 0.2% | |
300 | 19.9984 | 19.9993 | 19.9984 | 19.9871 | 19.9958 | 0.2% | ||
600 | 19.9979 | 19.9993 | 19.9985 | 19.987 | 19.9956 | 0.2% | ||
20 mA simulation value | 100 | 19.9813 | 0.071% | 0.2% | ||||
300 | 19.9811 | 0.073% | 0.2% | |||||
600 | 19.9807 | 0.075% | 0.2% |
Component | Nominal Value | Tolerance | Threshold |
---|---|---|---|
R4 | 10 kΩ | 0.01% | 10.02 kΩ |
R7 | 348 Ω | 0.01% | 348.7 Ω |
C1 | 1 nF | 5% | 0.816 nF |
C3 | 470 pF | 5% | 253.5 pF |
Component | Feature Extraction Method | Time Correlation | Monotonicity |
---|---|---|---|
R4 | SDAE | 0.9993 | 1 |
PCA | 0.9949 | 0.92 | |
PPMCC | 0.9271 | 0.83 | |
R7 | SDAE | 1 | 1 |
PCA | 0.9920 | 0.99 | |
PPMCC | 0.9745 | 0.98 | |
C1 | SDAE | 1 | 1 |
PCA | 0.9920 | 0.99 | |
PPMCC | 0.9745 | 0.98 | |
C3 | SDAE | 0.9994 | 1 |
PCA | 0.9985 | 0.99 | |
PPMCC | 0.9617 | 0.97 |
Input Layer Nodes | Number of Hidden Layer | Hidden Layer Nodes | Learning Rate | Output Layer Nodes |
---|---|---|---|---|
5 | 1 | 103 | 2.45 × 10−3 | 1 |
Component | Method | RMSE | MAE | MAPE | EA | Accuracy |
---|---|---|---|---|---|---|
R4 | PF | 1.22 × 10−4 | 0.0098 | 1.2432 | 0.007951 | 99.20% |
SVM | 1.12 × 10−4 | 0.0096 | 1.1447 | 0.014541 | 98.55% | |
MKRVM | 1.78 × 10−5 | 0.0032 | 0.4054 | 0.000606 | 99.94% | |
RNN | 8.59× 10−4 | 0.0155 | 2.4552 | 0.004006 | 99.60% | |
LSTM | 4.52 × 10−5 | 0.0133 | 1.5292 | 0.003213 | 99.68% | |
AGWO-GRU | 1.47 × 10−5 | 0.0029 | 0.3905 | 0.000109 | 99.99% | |
R7 | PF | 5.17 × 10−4 | 0.0196 | 2.6708 | 0.023256 | 97.67% |
SVM | 1.92 × 10−4 | 0.0118 | 2.5066 | 0.019266 | 98.07% | |
MKRVM | 1.06 × 10−4 | 0.0094 | 1.2007 | 0.000798 | 99.92% | |
RNN | 8.62 × 10−4 | 0.0190 | 2.9768 | 0.000717 | 99.93% | |
LSTM | 1.88 × 10−5 | 0.0106 | 1.0986 | 0.000438 | 99.96% | |
AGWO-GRU | 7.21 × 10−5 | 0.0065 | 0.6321 | 5.33 × 10−5 | 99.99% | |
C1 | PF | 1.39 × 10−4 | 0.0054 | 0.8772 | 0.007598 | 99.24% |
SVM | 8.67 × 10−5 | 0.0046 | 0.6417 | 0.002098 | 99.79% | |
MKRVM | 2.43 × 10−5 | 0.0022 | 0.2791 | 0.000279 | 99.97% | |
RNN | 2.59 × 10−4 | 0.0063 | 1.0417 | 0.000540 | 99.95% | |
LSTM | 3.03 × 10−5 | 0.0050 | 0.7265 | 0.000231 | 99.98% | |
AGWO-GRU | 1.83 × 10−6 | 0.0011 | 0.1418 | 0.000102 | 99.99% | |
C3 | PF | 8.82 × 10−5 | 0.0056 | 0.8643 | 0.002718 | 99.73% |
SVM | 4.02 × 10−5 | 0.0054 | 0.7512 | 0.000754 | 99.92% | |
MKRVM | 5.56 × 10−5 | 0.0048 | 0.6327 | 0.000234 | 99.97% | |
RNN | 1.25 × 10−4 | 0.0061 | 1.3213 | 0.000592 | 99.94% | |
LSTM | 3.29 × 10−5 | 0.0051 | 0.7274 | 7.72×10-5 | 99.99% | |
AGWO-GRU | 1.49 × 10−5 | 0.0035 | 0.4936 | 3.65×10-5 | 99.99% |
Component | Method | Training Time/s | Testing Time/s |
---|---|---|---|
R4 | LSTM | 36.83992 | 0.0080020 |
AGWO-GRU | 2.70715 | 0.0060019 | |
R7 | LSTM | 35.57344 | 0.0070014 |
AGWO-GRU | 2.20703 | 0.0050010 | |
C1 | LSTM | 14.72103 | 0.010002 |
AGWO-GRU | 0.56013 | 0.0060009 | |
C3 | LSTM | 12.04802 | 0.0080018 |
AGWO-GRU | 0.46063 | 0.0070018 |
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Chen, Z.; Dai, M.; Liu, J.; Jiang, W. Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU. Energies 2024, 17, 4063. https://doi.org/10.3390/en17164063
Chen Z, Dai M, Liu J, Jiang W. Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU. Energies. 2024; 17(16):4063. https://doi.org/10.3390/en17164063
Chicago/Turabian StyleChen, Zhi, Miaoxin Dai, Jie Liu, and Wei Jiang. 2024. "Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU" Energies 17, no. 16: 4063. https://doi.org/10.3390/en17164063