Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation
Abstract
:1. Introduction
2. Feature Extraction and Multimeasurement Hybrid-Feature Weighting Scheme
2.1. Multiple-Type Feature Extraction from Multiple Domains
2.2. Hybrid Feature-Weighting Scheme
2.2.1. Four Basic Measure Schemes
2.2.2. Weight Calculation of Hybrid-Feature Evaluation
3. Method and Process of Fault Diagnosis Based on Multimeasurement Hybrid-Feature Evaluation
3.1. Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation
3.1.1. Fault-Diagnosis Model Based on KPCA
3.1.2. Fault-Diagnosis Model Based on SVM
3.2. Fault-Diagnosis Process Based on Multimeasurement Hybrid-Feature Evaluation
4. Experiment Analysis
4.1. Experiment Depictions
4.2. Validation and Comparisons of New Fault-Feature Set
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Randall, R.B.; Antoni, J. Rolling element bearing diagnostics—A tutorial. Mech. Syst. Signal Process. 2011, 25, 485–520. [Google Scholar] [CrossRef]
- Li, Z.; Outbib, R.; Giurgea, S.; Hissel, D. Diagnosis for PEMFC systems: A data-driven approach with the capabilities of online adaptation and novel fault detection. IEEE Trans. Ind. Electron. 2015, 62, 5164–5174. [Google Scholar] [CrossRef]
- Ju, B.; Zhang, H.; Liu, Y.; Liu, F.; Lu, S.; Dai, Z. A feature extraction method using improved multi-scale entropy for rolling bearing fault diagnosis. Entropy 2018, 20, 212. [Google Scholar] [CrossRef]
- Xiao, Y.; Wang, Y.; Mu, H.; Kang, N. Research on misalignment fault isolation of wind turbines based on the mixed-domain features. Algorithms 2017, 10, 67. [Google Scholar] [CrossRef]
- Guo, X.; Shen, C.; Chen, L. Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery. Appl. Sci. 2017, 7, 41. [Google Scholar] [CrossRef]
- Alías, F.; Socoró, J.C.; Sevillano, X. A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Appl. Sci. 2016, 6, 143. [Google Scholar] [CrossRef]
- Uddin, M.; Lee, J.; Rizvi, S.; Hamada, S. Proposing enhanced feature engineering and a selection model for machine learning processes. Appl. Sci. 2018, 8, 646. [Google Scholar] [CrossRef]
- Vakharia, V.; Gupta, V.; Kankar, P. A comparison of feature ranking techniques for fault diagnosis of ball bearing. Soft Comput. 2016, 20, 1601–1619. [Google Scholar] [CrossRef]
- Islam, M.R.; Islam, M.M.; Kim, J.-M. Feature Selection Techniques for Increasing Reliability of Fault Diagnosis of Bearings. In Proceedings of the 2016 9th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 20–22 December 2016; pp. 396–399. [Google Scholar]
- Chen, X.; Zhang, X.; Zhou, J.; Zhou, K. Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets. Appl. Sci. 2019, 9, 1161. [Google Scholar] [CrossRef]
- He, C.; Niu, P.; Yang, R.; Wang, C.; Li, Z.; Li, H. Incipient rolling element bearing weak fault feature extraction based on adaptive second-order stochastic resonance incorporated by mode decomposition. Measurement 2019, 145, 687–701. [Google Scholar] [CrossRef]
- Zhu, K.; Chen, L.; Hu, X. A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis. J. Nondestruct. Eval. 2019, 38, 90. [Google Scholar] [CrossRef]
- Li, J.; Wang, H.; Song, L.; Cui, L. A novel feature extraction method for roller bearing using sparse decomposition based on self-Adaptive complete dictionary. Measurement 2019, 148, 106934. [Google Scholar] [CrossRef]
- Jiang, Q.; Yan, X.; Huang, B. Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference. IEEE Trans. Ind. Electron. 2015, 63, 377–386. [Google Scholar] [CrossRef]
- Lu, F.; Jiang, J.; Huang, J.; Qiu, X. An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis. Energies 2018, 11, 1807. [Google Scholar] [CrossRef]
- Lei, Y.; He, Z.; Zi, Y. Fault diagnosis based on novel hybrid intelligent model. Chin. J. Mech. Eng. 2008, 44, 112–117. [Google Scholar] [CrossRef]
- Guyon, I.; Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3, 1157–1182. [Google Scholar]
- Li, J.; Cheng, K.; Wang, S.; Morstatter, F.; Trevino, R.P.; Tang, J.; Liu, H. Feature selection: A data perspective. ACM Comput. Surv. (CSUR) 2018, 50, 94. [Google Scholar] [CrossRef]
- Zheng, K.; Wang, X. Feature selection method with joint maximal information entropy between features and class. Pattern Recognit. 2018, 77, 20–29. [Google Scholar] [CrossRef]
- Gu, Q.; Li, Z.; Han, J. Generalized Fisher Score for Feature Selection. arXiv 2012, arXiv:1202.3725. [Google Scholar]
- He, X.; Cai, D.; Niyogi, P. Laplacian Score for Feature Selection. In Advances in Nerual Information Processing Systems; MIT Press: Cambridge, MA, USA, 2005. [Google Scholar]
- Si, L.; Wang, Z.; Tan, C.; Liu, X. Vibration-based signal analysis for shearer cutting status recognition based on local mean decomposition and fuzzy C-means clustering. Appl. Sci. 2017, 7, 164. [Google Scholar] [CrossRef]
- Widodo, A.; Yang, B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 2007, 21, 2560–2574. [Google Scholar] [CrossRef]
- Chen, F.; Tang, B.; Song, T.; Li, L. Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement 2014, 47, 576–590. [Google Scholar] [CrossRef]
- Bordoloi, D.; Tiwari, R. Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data. Measurement 2014, 55, 1–14. [Google Scholar] [CrossRef]
- Liu, Z.; Cao, H.; Chen, X.; He, Z.; Shen, Z. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing 2013, 99, 399–410. [Google Scholar] [CrossRef]
- Jiang, Y.; Cheng, G.; Kan, J.; Xuan, Z.; Ma, J.; Zhang, Z. Rolling bearing fault diagnosis based on NGA optimized SVM. Chin. J. Sci. Instrum. 2013, 34, 2684–2689. [Google Scholar]
- Liu, R.; Yang, B.; Zio, E.; Chen, X. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 2018, 108, 33–47. [Google Scholar] [CrossRef]
- Zeng, L.; Long, W.; Li, Y. A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T2 and SPE. Processes 2019, 7, 124. [Google Scholar] [CrossRef]
- Liu, Y.-H.; Wang, S.-H.; Hu, M.-R. A self-paced P300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA + SVM-based detector. Appl. Sci. 2016, 6, 142. [Google Scholar] [CrossRef]
- Loparo, K. Case Western Reserve University Bearing Data Center. Available online: https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website (accessed on 15 September 2019).
- Sawalhi, N.; Randall, R. Simulating gear and bearing interactions in the presence of faults: Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults. Mech. Syst. Signal Process. 2008, 22, 1924–1951. [Google Scholar] [CrossRef]
Notation | Equation |
---|---|
Motor Speed (rpm) | Motor Load (HP) | Fault location | Fault Diameter (inch) | Name of Setting |
---|---|---|---|---|
1750 | 2 | normal | 0 | normal |
1750 | 2 | Rolling | 0.007 | B/0.007 |
1750 | 2 | Rolling | 0.014 | B/0.014 |
1750 | 2 | Rolling | 0.021 | B/0.021 |
1750 | 2 | Inner-race | 0.007 | IR/0.07 |
1750 | 2 | Inner-race | 0.014 | IR/0.014 |
1750 | 2 | Inner-race | 0.021 | IR/0.021 |
1750 | 2 | Outer-race | 0.007 | OR/0.007 |
1750 | 2 | Outer-race | 0.014 | OR/0.014 |
1750 | 2 | Outer-race | 0.021 | OR/0.021 |
Motor Speed(rpm) | Fault Location | Fault Diameter (mm) | Name of Setting |
---|---|---|---|
600 | normal | 0 | Normal state |
600 | Inner-race | 0.8 | Inner-race failure |
600 | Outer-race | 0.3 | Outer-race failure |
600 | Ball | 0.5 | Ball failure |
Feature Sets | First Main Element | Second Main Element | Mean Value | ||||||
---|---|---|---|---|---|---|---|---|---|
1.26 × 105 | 3.78 × 108 | 3.01 × 103 | 5.82 × 103 | 8.44 × 106 | 1.45 × 103 | 6.59 × 104 | 1.93 × 108 | 2.23 × 103 | |
1.25 × 104 | 7.18 × 106 | 575.19 | 3.26 × 103 | 1.29 × 106 | 396.76 | 7.87 × 103 | 4.24 × 106 | 485.98 | |
8.59 × 103 | 2.16 × 107 | 2.51 × 103 | 3.25 × 103 | 2.01 × 106 | 616.76 | 5.92 × 103 | 1.18 × 107 | 1.56 × 103 | |
8.53 × 103 | 6.43 × 106 | 753.15 | 2.87 × 103 | 1.17 × 106 | 407.11 | 5.70 × 103 | 3.79 × 106 | 580.13 | |
1.24 × 104 | 1.91 × 107 | 1.54 × 103 | 2.75 × 103 | 1.71 × 106 | 622.50 | 7.57 × 103 | 1.04 × 107 | 1.08 × 103 | |
474.22 | 3.57 × 105 | 753.15 | 159.75 | 6.50 × 104 | 407.11 | 316.98 | 2.11 × 105 | 580.13 |
Feature Sets | Cumulative Number of Misclassified Samples | Normal State | Inner Race Failure | Outer Race Failure | Ball Failure | Classification Accuracy (%) |
---|---|---|---|---|---|---|
Misclassified Sample Number | Misclassified Sample Number | Misclassified Sample Number | Misclassified Sample Number | |||
4 | 2 | 2 | 0 | 0 | 95.0 | |
8 | 0 | 3 | 0 | 5 | 90.0 | |
10 | 0 | 10 | 0 | 0 | 87.5 | |
15 | 3 | 10 | 2 | 0 | 81.25 | |
10 | 2 | 5 | 0 | 3 | 87.5 | |
17 | 2 | 5 | 3 | 7 | 78.75 |
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Ge, J.; Yin, G.; Wang, Y.; Xu, D.; Wei, F. Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation. Information 2019, 10, 359. https://doi.org/10.3390/info10110359
Ge J, Yin G, Wang Y, Xu D, Wei F. Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation. Information. 2019; 10(11):359. https://doi.org/10.3390/info10110359
Chicago/Turabian StyleGe, Jianghua, Guibin Yin, Yaping Wang, Di Xu, and Fen Wei. 2019. "Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation" Information 10, no. 11: 359. https://doi.org/10.3390/info10110359
APA StyleGe, J., Yin, G., Wang, Y., Xu, D., & Wei, F. (2019). Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation. Information, 10(11), 359. https://doi.org/10.3390/info10110359