Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
Abstract
:1. Introduction
2. Proposed Hierarchical Lempel-Ziv Complexity
2.1. Lempel-Ziv Complexity
2.2. Multi-Scale Lempel-Ziv Complexity
2.3. Hierarchical Lempel-Ziv Complexity
2.4. Simulated Impulsive Signal
3. Proposed Fault Diagnosis Framework
3.1. Support Vector Machine
3.2. Proposed Method
- (1)
- Measure the vibration data for various conditions of rotating machinery;
- (2)
- Partition the measured vibration data into training datasets and testing datasets;
- (3)
- Utilize HLZC to extract fault features from the vibration signals. Note that the hierarchical decomposition layers of HLZC is set as , and thus 31 features will be obtained;
- (4)
- Train SVM classifier using the training features;
- (5)
- Test the trained SVM classifier, wherein the output of SVM can be used to recognize the different fault types of rotating machinery.
4. Experimental Validations
4.1. Experiment 1
4.2. Experiment 2
5. Conclusions
- (1)
- LZC was extended to hierarchical decomposition analysis, namely, HLZC;
- (2)
- HLZC considered the fault information hidden in both low-frequency and high-frequency components through conducting the averaging and differencing operations;
- (3)
- A novel fault diagnosis scheme was proposed by combining HLZC and SVM;
- (4)
- The proposed method was verified using both simulated and experimental signals.
Author Contributions
Funding
Conflicts of Interest
References
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Fault Class | Class Label | Damage Diameter (mm) | Number of Training Samples | Number of Testing Samples |
---|---|---|---|---|
Normal | 1 | 0 | 50 | 50 |
Ball fault | 2 | 0.01 | 50 | 50 |
Inner race fault | 3 | 0.01 | 50 | 50 |
Outer race fault | 4 | 0.01 | 50 | 50 |
Grooving in the inner race | 5 | 0.2 | 50 | 50 |
Grooving in the outer race | 6 | 0.2 | 50 | 50 |
Experiments | HLZC | MLZC | LZC | ||||||
---|---|---|---|---|---|---|---|---|---|
Accuracy (%) | Accuracy (%) | Accuracy (%) | |||||||
Max | Min | Mean | Max | Min | Mean | Max | Min | Mean | |
1 | 95.67 | 91.33 | 94.30 | 93.67 | 89.33 | 91.72 | 66.33 | 61.67 | 63.47 |
2 | 97.20 | 92 | 94.72 | 90 | 84.80 | 87.82 | 45.20 | 36 | 41.22 |
Fault Class | Class Label | Damage Diameter (mm) | Number of Training Samples | Number of Testing Samples |
---|---|---|---|---|
Normal | 1 | 0 | 50 | 50 |
BI | 2 | 0.01 | 50 | 50 |
MI | 3 | 0.01 | 50 | 50 |
NI | 4 | 0.01 | 50 | 50 |
PI | 5 | 0.01 | 50 | 50 |
Scale Factor τ | 1 | 2 | 5 | 10 | 15 | 17 | 18 | 19 | 20 | 21 |
---|---|---|---|---|---|---|---|---|---|---|
Max (%) | 45.2 | 52 | 60 | 75 | 85 | 88 | 89 | 90 | 90 | 90 |
Min (%) | 36 | 43 | 52 | 70 | 78 | 82 | 84 | 83 | 85 | 85 |
Mean (%) | 41.22 | 45 | 57 | 73 | 82 | 85 | 86.4 | 87.2 | 87.82 | 87.2 |
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Han, B.; Wang, S.; Zhu, Q.; Yang, X.; Li, Y. Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity. Appl. Sci. 2020, 10, 4221. https://doi.org/10.3390/app10124221
Han B, Wang S, Zhu Q, Yang X, Li Y. Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity. Applied Sciences. 2020; 10(12):4221. https://doi.org/10.3390/app10124221
Chicago/Turabian StyleHan, Bing, Shun Wang, Qingqi Zhu, Xiaohui Yang, and Yongbo Li. 2020. "Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity" Applied Sciences 10, no. 12: 4221. https://doi.org/10.3390/app10124221
APA StyleHan, B., Wang, S., Zhu, Q., Yang, X., & Li, Y. (2020). Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity. Applied Sciences, 10(12), 4221. https://doi.org/10.3390/app10124221