International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

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.


Download Citation: |
Download PDF


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.

Keywords: Fault detection, Principal component analysis, Independent component analysis, Generalized likelihood ratio test, Non-Gaussian.

Share this article with your colleagues



  1. Chen, Q., Kruger, U., Leung, A.T.Y. 2004. Regularised kernel density estimation for clustered process data. Control Engineering Practice, 12, 267–274.

  2. Chen, Q., Wynne, R.J., Goulding, P., Sandoz, D. 2000. The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 8, 531–543.

  3. Ge, Z., Song, Z. 2007. Process monitoring based on independent component Analysis-Principal Component analysis (ICA-PCA) and similarity factors. Industrial and Engineering Chemistry Research, 46, 2054–2063.

  4. González, I., Sánchez, S. 2008. Principal alarms in multivariate statistical process control using independent component analysis. International Journal of Production Research, 46, 6345–6366.

  5. Horn, J. 1965. A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.

  6. Hyvärinen, A. 1999. Fast and robust Fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10, 626–634.

  7. Kano, M. Tanaka, S., Hasebe, S., Hashimoto, I., Ohno, H. 2003. Monitoring independent components for fault detection. AIChE Journal, 49, 969–976.

  8. Krzanowski, W.J., Kline, P. 1995. Cross-Validation for choosing the number of important components in principal component analysis. Multivariate Behavioral Research, 30, 149–165.

  9. Ku, W., Storer, R., Georgakis, C. 1995. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30, 179–196.

  10. Lee, J.M., Qin, S.J., Lee I.B. 2006. Fault detection and diagnosis based on modified independent component analysis. AIChE Journal, 52, 3501–3514.

  11. Lee, J.M., Qin, S.J., Lee, I.B. 2007. Fault detection of Non-linear process using kernel independent component analysis. The Canadian Journal of Chemical Engineering, 85, 526–536.

  12. Lee, J.M., Yoo, C.K., Lee, I.B. 2004. Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chemical Engineering Science, 59, 2995–3006.

  13. Li, W., Yue, H.H., Valle-Cervanttes, S., Qin, S.J. 2000. Recursive PCA for adaptive process monitoring. Journal of Process Control, 10, 471–486.

  14. Lu, C.J., Wu, C.M., Keng, C.J., Chiu, C.C. 2008. Integrated application of SPC/EPC/ICA and neural networks. International Journal of Production Research, 46, 873–893.

  15. Martin, E.B., Morris, A.J. 1996. Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control, 6, 349–358.

  16. Montgomery, D.C. 2012. Introduction to statistical quality control, 7th edition, New York, NY: Wiley.

  17. Munirathinam, S., Ramadoss, B. 2016. Predictive models for equipment fault detection in the semiconductor manufacturing process. IACSIT International Journal of Engineering and Technology. 8, 273–285.

  18. Page, E.S. 1954. Continuous inspection schemes. biometrics, 41, 100–115.

  19. Rato, T.J., Reis, M.S. 2013. Fault detection in the Tennessee Eastman benchmark process using principal component analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems, 125, 101–108.

  20. Reynolds, M.R., Jr., Lou, J. 2010. An evaluation of a GLR control chart for monitoring the process mean. Journal of Quality Technology, 42, 287–310.

  21. Reynolds, M.R., Jr., Stumbos, Z.G. 2004a. Control charts and the efficient allocation of sampling resources. Technometrics, 46, 200–214.

  22. Reynolds, M.R., Jr., Stumbos, Z.G. 2004b. Should observations be grouped for effective process monitoring? Journal of Quality Technology. 36, 343–366.

  23. Robert, S.W. 1959. Control chart tests based on geometric moving averages. Technometrics 1, 239–250.

  24. Silverman, B.W., 1986. Density estimation for statistics and data analysis, UK: Chapman & Hall.

  25. Valle, S., Li. W., Qi, S.J. 1999. Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods. Industrial and Engineering Chemistry Research, 38, 4379–4401.

  26. Wang, X., Kruger, U., Irwin, G.W. 2005. Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research, 44, 5691–5702.

  27. Yoo, C.K., Lee, J.M., Vanrolleghem, P.A., Lee, I.B. 2004. On-line monitoring of batch processes using multiway independent component analysis. Chemometrics and Intelligent Laboratory Systems, 71, 151–163.

  28. Zhang, C., Chen, N., Zou, C. 2016. Robust multivariate control chart based on Goodness-of-Fit test. Journal of Quality Technology. 48, 139–161.

  29. Zhou, Z., Wen, C., Yang, C. 2016. Fault isolation based on k-Nearest neighbor rule for industrial processes. IEEE Transactions on Industrial Electronics, 63, 2578–2586.

  30. Zhu, K., Hong, G.S., Wong, Y.S., Wang, W. 2008. Cutting force denoising in Micro-milling tool condition monitoring. International Journal of Production Research, 46, 4391–4408.


Received: 2020-12-17
Revised: 2021-02-23
Accepted: 2021-03-23
Available Online: 2021-06-01

Cite this article:

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.

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

We use cookies on this website to improve your user experience. By using this site you agree to its use of cookies.