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Performance Improvement of Feature-Based Fault Classification for Rotor System

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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

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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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Authors and Affiliations

  1. Prognostic Engineering Lab, Korea Hydro Nuclear Power, 70, Yuseong-daero 1312 Beon-gil, Yuseong-gu, Daejeon, 341-01, Republic of Korea

    Won-Kyu Lee

  2. Department of Energy and Mechanical Engineering, Gyeongsang National University, 38, Cheondaegukchi-gil, Tongyeong-si, Gyeongsangnam-do, 530-64, Republic of Korea

    Deok-Yeong Cheong & Dong-Hee Park

  3. Department of Energy and Mechanical Engineering, Institute of Marine Industry, Gyeongsang National University, 38, Cheondaegukchi-gil, Tongyeong-si, Gyeongsangnam-do, 530-64, Republic of Korea

    Byeong-Keun Choi

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  1. Won-Kyu Lee
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Correspondence to Byeong-Keun Choi.

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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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