Abstract
For the management of rotating machines, machine learning (ML) has been researched with the use of feature parameters that have physical and statistical meanings of vibration signals. Genetic algorithm (GA) and principal component analysis (PCA) are the algorithms used for the selection or extraction process of the features; equipment condition. This study proposes a new method to maximize the advantages of the extraction and selection algorithms, thereby improving the fault classification performance. The proposed method is estimated in a variety of equipment conditions by selecting and extracting the effective features for status classification. To evaluate the performance of the fault classification through feature selection and extraction of the ML, a comparative analysis with the proposed method and the original method is also performed. With Lab-scale gearbox, several types of fault tests are conducted, and seven different fault types of equipment conditions, including the normal status, are simulated. The results of the experiments show that, the performance of classification of GA for feature selection is 85%, while PCA for feature extraction is 53%. The performance result of the proposed method for fault classification is 95%, meaning that the performance of fault diagnosis is more efficient in terms of discriminative learning than the original method. Therefore, the proposed method with feature extraction and selection algorithm can improve the fault classification performance by 10% and more for fault diagnosis through ML.
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Abbreviations
- \(N\) :
-
Total number of samples
- \(n\) :
-
Sample number
- \(x\left( n \right)\) :
-
Time-domain signal
- \(\overline{x}\) :
-
Mean of time-domain signal
- \(s\left( n \right)\) :
-
Frequency-domain signal (fast Fourier transform from time-domain signal)
- \(\overline{s}\) :
-
Mean of time-domain signal
- \(c\left( n \right)\) :
-
Cepstrum-domain signal (inverse fast Fourier transform from frequency-domain signal)
- \(\overline{c}\) :
-
Mean of Cepstrum-domain signal
- \(\sigma\) :
-
Standard deviation of time-domain signal
- \(\tau\) :
-
Standard deviation of frequency-domain signal
- \(\varphi\) :
-
Standard deviation of Cepstrum-domain signal
- \(P\left( n \right)\) :
-
Random variable of time-domain signal
- \(Z\left( n \right)\) :
-
Random variable of frequency-domain signal
- T\(\left( n \right)\) :
-
Random variable of Cepstrum-domain signal
References
Preuveneers, D., & Ilie-Zudor, E. (2017). The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0. Journal of Ambient Intelligence and Smart Environments,9, 287–298.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP,16, 3–8.
Jeong, H. D., & Lee, S. C. (2017). Industrial artificial intelligence. Transaction of Korean Society for Noise and Vibration Engineering,27(6), 3–7.
Kim, D. H., Kim, T. J. Y., Wang, X., Kim, M. C., Quan, Y.-J., Oh, J. W., et al. (2018). Smart machine process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology,5(4), 555–568.
Kim, J. S., Lee, C. S., Kim, S. M., & Lee, S. W. (2018). Development of data-driven in situ monitoring and diagnosis system of fused deposition modeling (FDM) process based on support vector machine algorithm. International Journal of Precision Engineering and Manufacturing-Green Technology,5(4), 479–486.
Yang, H., Mathew, J., & Ma, L. (2003). Vibration feature extraction techniques for fault diagnosis of rotating machinery: A literature survey. In Asia-pacific vibration conference.
Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processings,18, 625–644.
Samanta, B., & AL-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing,17, 317–328.
Jack, L. B., & Nandi, A. K. (2000). Genetic algorithms for feature selection in machine condition monitoring with vibration signals. IEE Proceedings of Vision and Image Signal Processing.,147, 205–212.
Rekimoto, J., & Green, M., (1993). The information cube: Using transparency in 3D information visualization. In Proceedings of the third annual workshop information technologies and systems.
Shin, I. S., Lee, J. M., Lee, Y. J., Jung, K. S., Kwon, D. I., Youn, B. D., et al. (2018). A framework for prognostics and health management applications toward smart manufacturing systems. International Journal of Precision Engineering and Manufacturing-Green Technology,5(4), 535–554.
Cheong, D. Y., Ahn, B. H., Park, D. H., & Choi, B. K. (2019). Feature-based trend monitoring of vibration signals according to severity of gear tooth breakage. Transaction of Korean Society for Noise and Vibration Engineering,29(2), 199–205.
Kim, H. J., Ahn, B. H., Park, D. H., & Choi, B. K. (2017). Vibration signal analysis of gearbox fault according to feature. Transactions of Korean Society for Noise and Vibration Engineering,27(4), 419–424.
Ha, J. M., Kim, H. J., Shin, Y. S., & Choi, B. K. (2018). Degradation trend estimation and prognostics for low speed gear lifetime. International Journal of Precision Engineering and Manufacturing,19(8), 1099–1105.
Ahn, B. H., Yu, H. T., & Choi, B. K. (2018). Feature-based analysis for fault diagnosis of gas turbine using machine learning and genetic algorithms. Journal of the Korean Society for Precision Engineering,35(2), 163–167.
Kim, J. M., Ahn, B. H., Lee, J. M., Yu, H. T., & Choi, B. K. (2017). Feature analysis of vibration and acoustic emission according to pipe cracking and valve opening/closing. Transaction of the Korean Society of Mechanical Engineers,27(7), 857–862.
Kim, H. J., Ahn, B. H., Park, D. H., & Choi, B. K. (2017). Feature analysis for fault diagnosis according to gearbox failure. Transaction of Korean Society for Noise and Vibration Engineering,27(3), 312–317.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithm and machine learning. Machine Learning,3(95), 95–99.
De Jong, K. (1988). Learning with genetic algorithms: An overview. Machine Learning,3(95), 121–138.
Vafaie, H., & De Jong, K. (1992). Genetic algorithms as a tool for feature selection in machine learning. In Proceeding of the 4th international conference on tools with artificial intelligence.
Leardi, R., Boggia, R., & Terrile, M. (1992). Genetic algorithms as strategy for feature selection. Journal of Chemometric,6, 267–281.
Lindasay, I. S. (2002). A tutorial on principal components analysis. http://reflect.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf. Accessed 26 Feb, 2002.
Widodo, A., & Yang, B. S. (2007). Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motor. Expert System with Application,33(1), 241–250.
Trendafilova, I., Cartmell, M. P., & Ostachowicz, W. (2008). Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition. Journal of Sound and Vibration,313, 560–566.
Shao, R., Wentao, H., Wang, Y., & Qi, X. (2014). The fault extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement,54, 118–132.
Randall, R. B. (2016). A history of Cepstrum analysis its application to mechanical problems. Mechanical Systems and Signal Processing,97, 3–19.
Nacib, L., Pekpe, K. M., & Sakhara, S. (2013). Detecting gear tooth cracks using cepstral analysis in gearbox of helicopters. International Journal of Advances in Engineering and Technology.,5, 139–145.
Saitta, L. (1995). Support-vector networks. Machine Learning,20, 273–297.
Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing,21, 2560–2574.
Parker, J. R. (2001). Rank and response combination from confusion matrix data. Information Fusion,2, 113–120.
Acknowledgements
This research was supported by the grant entitled “Development of Automatic Predictive Diagnosis Technology (Korea Hydro & Nuclear Power Central Research Institute, L18S065000).
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Lee, WK., Cheong, DY., Park, DH. et al. Performance Improvement of Feature-Based Fault Classification for Rotor System. Int. J. Precis. Eng. Manuf. 21, 1065–1074 (2020). https://doi.org/10.1007/s12541-020-00324-w
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DOI: https://doi.org/10.1007/s12541-020-00324-w