Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection
Abstract
:1. Introduction
- The simulation model of the hydraulic multi-way valve is established, and the fault data of the multi-way valve is obtained through simulation as an extension of the experimental data.
- Based on the shallow statistical features, the spatial and temporal multi-dimensional fault features are extracted, and the fault features are weighted adaptively combined with the attention mechanism.
- A fault diagnosis method for hydraulic multi-way valves based on CNN, SE, and GRU is proposed, which effectively realizes fault diagnosis for hydraulic multi-way valves even under variable operating conditions.
2. Methods
- (1)
- Using a data acquisition system, the healthy signal and fault signals of the hydraulic valve under different operating conditions are obtained.
- (2)
- The window sliding is used to enhance the fault sample data of the fault signal, and then the shallow statistical features (i.e., time-domain, frequency-domain, and time-frequency domain features) are extracted from the data containing fault information.
- (3)
- Conduct an mRMR feature selection algorithm analysis on the shallow statistical features of the hydraulic valve obtained under different operating conditions to assess the correlation with faults. This analysis identifies the optimal features among these features for the subsequent processing.
- (4)
- In this step, the CNN is carried out on the optimized fault features, and the weighted spatial dimension features are obtained by adding an SE block, which is then input into the GRU network for timing feature processing. The fault features of hydraulic multi-way valves are extracted in the time dimension. Consequently, the final fault diagnosis results are obtained.
2.1. Fault Sample Data Enhancement
2.2. Data Feature Extraction
2.3. Max-Relevance and Min-Redundancy for Feature Selection
2.4. CNN Based on Attention Mechanism
2.5. A Brief Introduction to GRU
3. Obtained Fault Data
3.1. Case1: Simulation and Experiment of the Hydraulic Multi-Way Valve
3.1.1. Simulation Model
3.1.2. Fault Models
3.1.3. Simulated Fault Data
3.2. Case2: Experiment of the Hydraulic Directional Valve
3.2.1. Working Principle
3.2.2. Fault Models
3.2.3. Fault Data
4. Fault Diagnosis
4.1. Case1: Fault Diagnosis of the Hydraulic Multi-Way Valve
4.1.1. Data Processing
4.1.2. Feature Prioritization
4.1.3. Results of Different Methods
4.1.4. Confusion Matrix
4.1.5. Influence of the Anti-Noise Performance
4.2. Case2: Hydraulic Directional Valve Fault Diagnosis Experiment
4.2.1. Results of Different Methods
4.2.2. Confusion Matrix
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Time-Domain Feature | Frequency-Domain | Time-Frequency Domain | |||
---|---|---|---|---|---|
Max | Min | Mean value | Peak value | Gravity center frequency | Wavelet energy entropy |
Peak-peak value | Average amplitude | Root amplitude | Variance | Root mean square frequency | Wavelet singular entropy |
Standard deviation | Root mean square | Skewness | Kurtosis | Average frequency | |
Waveform factor | Peak factor | Pulse factor | Margin factor | ||
Clearance factor |
Parameter | Value | Parameter | Value |
---|---|---|---|
Motor speed/(r/min) | 1500 | Spool displacement/(mm) | 6 |
Pump displacement/(cc/rev) | 25 | Spring stiffness/(N/m) | 0.01 |
Relief valve cracking pressure/(bar) | 120 | Preload/(N) | 10 |
Spool diameter/(mm) | 12 | Density/(kg/) | 850 |
Rod diameter/(mm) | 8 | Temperature/(°C) | 40 |
Spool mass/(kg) | 0.15 | Bulk modulus/(MPa) | 1700 |
Radial clearance/(mm) | 0.001 | Flow coefficient | 0.7 |
Class | Fault Model | Fault Phenomenon | Fault Reason |
---|---|---|---|
1 | Normal | None | None |
2 | Cavitation | Noise, Vibration, Efficiency reduction | Bubbles in the oil |
3 | Moderate failure of valve spring | Pressure regulation, pressure retention, and response function out of control | Fatigue, heat, and degradation |
4 | Seriously failure of valve spring |
Class | Fault Model | Fault Phenomenon | Fault Reason |
---|---|---|---|
1 | Normal | None | None |
2 | Light wear (0.015–0.035 mm) | Leakage | Oil particle pollution |
3 | Moderate wear (0.035–0.060 mm) | ||
4 | Severe wear (>0.060 mm) | ||
5 | Mild failure of return spring | Pressure regulation, pressure retention, and response function out of control | Fatigue, heat, and degradation |
6 | Severe failure of return spring |
CNN | BiLSTM | GRU | CNN-BiLSTM | CNN-GRU | SECNN-GRU |
---|---|---|---|---|---|
Conv_1, 8, 3 × 1, 1 | Unit, 10 | Unit, 10 | Conv_1, 8, 3 × 1, 1 | Conv_1, 8, 3 × 1, 1 | Conv_1, 16, 3 × 1, 1 |
Maxpool, 2 × 1, 2 | Fc, 4 | Fc, 4 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 |
Conv_2, 16, 3 × 1, 1 | Conv_2, 16, 3 × 1, 1 | Conv_2, 16, 3 × 1, 1 | Conv_2, 64, 3 × 1, 1 | ||
Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | ||
Conv_3, 32, 3 × 1, 1 | Conv_3, 32, 3 × 1, 1 | Conv_3, 32, 3 × 1, 1 | Global average pool | ||
Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Fc, 16 | ||
Conv_4, 32, 3 × 1, 1 | Conv_4, 32, 3 × 1, 1 | Conv_4, 32, 3 × 1, 1 | Fc, 64 | ||
Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Maxpool, 2 × 1, 2 | Flatten | ||
Fc, 4 | Unit, 10 | Unit, 10 | Unit, 10 | ||
Fc, 4 | Fc, 4 | Fc, 4 |
Methods | Average Accuracy(%) | Standard Deviation(%) |
---|---|---|
CNN | 94.66 | 1.03 |
BiLSTM | 86.46 | 1.14 |
GRU | 84.69 | 0.94 |
CNN-BiLSTM | 95.55 | 0.63 |
CNN-GRU | 97.43 | 0.94 |
The proposed method | 98.94 | 0.40 |
Methods | Average Accuracy(%) | Standard Deviation(%) | ||||||
---|---|---|---|---|---|---|---|---|
A1 | A2 | P1 | P2 | A1 | A2 | P1 | P2 | |
CNN | 85.43 | 87.02 | 93.76 | 88.77 | 0.31 | 0.68 | 0.71 | 0.85 |
BiLSTM | 71.10 | 73.59 | 88.65 | 77.86 | 0.33 | 0.89 | 0.62 | 0.72 |
GRU | 71.30 | 77.31 | 89.94 | 79.52 | 0.46 | 0.66 | 0.74 | 0.92 |
CNN-BiLSTM | 89.10 | 90.38 | 95.83 | 90.47 | 0.34 | 0.73 | 0.80 | 0.79 |
CNN-GRU | 90.39 | 90.54 | 95.98 | 90.93 | 1.07 | 0.51 | 0.59 | 0.32 |
The proposed method | 92.10 | 92.12 | 97.07 | 93.37 | 0.60 | 0.73 | 0.70 | 0.83 |
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Share and Cite
Guan, H.; Yan, R.; Tang, H.; Xiang, J. Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection. Sensors 2023, 23, 9371. https://doi.org/10.3390/s23239371
Guan H, Yan R, Tang H, Xiang J. Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection. Sensors. 2023; 23(23):9371. https://doi.org/10.3390/s23239371
Chicago/Turabian StyleGuan, Hanlin, Ren Yan, Hesheng Tang, and Jiawei Xiang. 2023. "Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection" Sensors 23, no. 23: 9371. https://doi.org/10.3390/s23239371