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Real-time fault detection and process control based on multi-channel sensor data fusion


Sensor signals acquired in industrial equipment contain rich information which can be analyzed to facilitate effective monitoring of equipment, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. In many mechatronic systems, multiple signals are acquired by different sensor channels (i.e., multi-channel data) which can be represented by high-order arrays (tensorial data). The multi-channel data has a high-dimensional and complex cross-correlation structure. It is crucial to develop a method that considers the interrelationships between different sensor channels. This paper proposes a new equipment monitoring approach based on uncorrelated multilinear discriminant analysis that can effectively model the multi-channel data to achieve a superior monitoring and fault diagnosis performance compared to other competing methods. The proposed method is applied directly to the high-dimensional tensorial data. Features are extracted and combined with multivariate control charts to achieve real-time fault detection of equipment. The effectiveness of the proposed method in quick detection of equipment faults is demonstrated with both the simulation and a real-world case study.

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  1. 1.

    Hannan E (1961) The general theory of canonical correlation and its relation to functional analysis. J Aust Math Soc 2:229–242

    MathSciNet  Article  Google Scholar 

  2. 2.

    Leurgans SE, Moyeed RA, and Silverman BW (1993) “Canonical correlation analysis when the data are curves,” Journal of the Royal Statistical Society. Series B (Methodological),725–740

  3. 3.

    Dubin JA, Müller H-G (2005) Dynamical correlation for multivariate longitudinal data. J Am Stat Assoc 100:872–881

    MathSciNet  Article  Google Scholar 

  4. 4.

    Yang W, Müller H-G, Stadtmüller U (2011) Functional singular component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73:303–324

    MathSciNet  Article  Google Scholar 

  5. 5.

    Grasso M, Colosimo BM, Pacella M (2014) Profile monitoring via sensor fusion: the use of PCA methods for multi-channel data. Int J Prod Res 52(20):6110–6135.

    Article  Google Scholar 

  6. 6.

    Di C-Z, Crainiceanu CM, Caffo BS, Punjabi NM (2009) Multilevel functional principal component analysis. Ann Appl Stat 3:458–488

    MathSciNet  Article  Google Scholar 

  7. 7.

    Chiou J-M, Chen Y-T, Yang Y-F (2014) Multivariate functional principal component analysis: a normalization approach. Stat Sin 1571–1596

  8. 8.

    Paynabar K, Zou C, Qiu P (2016) A change-point approach for phase-I analysis in multivariate profile monitoring and diagnosis. Technometrics 58:191–204

    MathSciNet  Article  Google Scholar 

  9. 9.

    Chiou J-M, Müller H-G (2014) Linear manifold modelling of multivariate functional data. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76:605–626

    MathSciNet  Article  Google Scholar 

  10. 10.

    Zhang C, Yan H, Lee S, Shi J (2018) Multiple profiles sensor-based monitoring and anomaly detection. J Qual Technol 50(4):344–362

    Article  Google Scholar 

  11. 11.

    Qiao X, James G, and Lv J (2015) “Functional graphical models,” Tech Rep, Technical report, University of Southern California

  12. 12.

    Paynabar K, Jin J, Pacella M (2013) Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Trans 45(11):1235–1247.

    Article  Google Scholar 

  13. 13.

    Zhu H, Strawn N, Dunson DB (2016) Bayesian graphical models for multivariate functional data. J Mach Learn Res 17:1–27

    MathSciNet  MATH  Google Scholar 

  14. 14.

    Wei Q, Huang W, Jiang W, Zhao W (2016) Real-time process monitoring using kernel distances. Int J Prod Res 54(21):6563–6578.

    Article  Google Scholar 

  15. 15.

    Amdouni A, Castagliola P, Taleb H, Celano G (2017) A variable sampling interval Shewhart control chart for monitoring the coefficient of variation in short production runs. Int J Prod Res 55(19):5521–5536.

    Article  Google Scholar 

  16. 16.

    Ye F, Zhang ZS, Xia Z, Zhou YF, Zhang H (2019) Monitoring and diagnosis of multi-channel profile data based on uncorrelated multilinear discriminant analysis. Int J Adv Manuf Technol 103(9–12):4659–4669

    Article  Google Scholar 

  17. 17.

    Paynabar K, Jin J, Agapiou J, Deeds P (2012) Robust leak tests for transmission systems using nonlinear mixed-effect models. J Qual Technol 44(3):265–278

    Article  Google Scholar 

  18. 18.

    Grasso M, Colosimo BM, Tsung F (2017) A phase I multi-modelling approach for profile monitoring of signal data. Int J Prod Res 55(15):4354–4377.

    Article  Google Scholar 

  19. 19.

    Lei Y, Zhang Z, Jin J (2010) Automatic tonnage monitoring for missing part detection in multi-operation forging processes. Journal of Manufacturing Science and Engineering-Transactions of the Asme 132(5).

  20. 20.

    Bhattacharyya P, Sengupta D (2009) Estimation of tool wear based on adaptive sensor fusion of force and power in face milling. Int J Prod Res 47(3):817–833.

    Article  MATH  Google Scholar 

  21. 21.

    Yang W-A, Zhou Q, Tsui K-L (2016) Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation. Int J Prod Res 54(15):4703–4721.

    Article  Google Scholar 

  22. 22.

    Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455.

    MathSciNet  Article  MATH  Google Scholar 

  23. 23.

    Fan Z, Cai M, Wang H (2012) An improved denoising algorithm based on wavelet transform modulus maxima for non-intrusive measurement signals. Meas Sci Technol 23(4):045007.

    Article  Google Scholar 

  24. 24.

    Guo W, Jin J, Hu SJ, Asme (2016) Profile monitoring and fault diagnosis via sensor fusion for ultrasonic welding. Proceedings of the Asme 11th International Manufacturing Science and Engineering Conference, 2016, Vol 2

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This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No.51775108.

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Correspondence to Zhijie Xia.

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Xia, Z., Ye, F., Dai, M. et al. Real-time fault detection and process control based on multi-channel sensor data fusion. Int J Adv Manuf Technol 115, 795–806 (2021).

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  • Feature extraction
  • Process monitoring and control
  • Sensor fusion
  • Fault detection and diagnosis
  • Tensor decomposition