Chun-Chin Hsu1*, Chun-Yuan Cheng1, Fang-Chih Tien2 1 Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung, Taiwan, R.O.C.
2 Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, R.O.C.
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As the growth of Industry 4.0, online fault detection plays a crucial role in ensuring the manufacturing quality. Generally, the fault detection methods can be classified into model-based and data-driven methods. There are advantages/disadvantages between two methods. In this study, we integrated both methods in order to develop an efficient fault detection method for non-Gaussian industrial processes. The data-driven method, independent component analysis (ICA) is used to extract non-Gaussian information and dimensionality reduction. Meanwhile, the model-based method, generalized likelihood ratio (GLR) test is adopted as the charting statistic. The proposed ICA-GLR method has advantages of 1) detecting a wide range of process changes, 2) estimating the change points and 3) needless prior parameters to be specified by practitioner. The efficiency of the proposed ICA-GLR fault detection method will be verified via implementing one simulated non-Gaussian process and two real manufacturing processes: Tennessee Eastman process and semiconductor manufacturing process. Results demonstrate that the proposed ICA-GLR method has superior fault detectability when compared to traditional methods, such as principal component analysis and ICA.ABSTRACT
Keywords:
Fault detection, Principal component analysis, Independent component analysis, Generalized likelihood ratio test, Non-Gaussian.
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Received:
2020-12-17
Revised:
2021-02-23
Accepted:
2021-03-23
Available Online:
2021-06-01
Hsu, C.-C., Cheng, C.-Y., Tien, F.-C. 2021. Fault detection based on ICA-GLR for non-Gaussian industrial processes, International Journal of Applied Science and Engineering, 18, 2020332. https://doi.org/10.6703/IJASE.202106_18(2).015
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