Detection of structural changes from an operational process is a major goal in machine condition monitoring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combining with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-ofthe-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.
1. Fokianos Konstantinos, E Gombay, A Hussein. Retrospective change detection for binary time series models. Journal of Statistic Planning and Inference, 2014, 145(2). 102-112.
2. N Peach. Detection of Abrupt Changes:Theory and Applications. M Basseville; I V Nikiforov, ed. Journal of the Royal Statistical Society, 2015, 15(158):326-327.
3. A G Tartakovsky, A S Polunchenko. Quickest change point detection in distributed multisensor systems under unknown parameters. International Conference on Information Fusion, IEEE, Istanbul, Turkey, July 9-12, 2013:1-8.
4. Hafiz Zafar Nazir, Muhammad Riaz, Ronald J M M Does, et al. Robust CUSUM control charting. Quality Engineering, 2013, 25(3):211-224.
5. H Cho. Change-point detection in panel data via double CUSUM statistic. Electronic Journal of Statistics, 2016, 10(2):2000-2038.
6. A Reñones, L J D Miguel, J R Perán. Experimental analysis of change detection algorithms for multitooth machine tool fault detection. Mechanical Systems and Signal Processing, 2009, 23(7):2320-2335.
7. K Atashgar. Identification of the change point:an overview. International Journal of Advanced Manufacturing Technology, 2013, 64(9-12):1663-1683.
8. M A Graham, S Chakraborti, S W Human. A nonparametric exponentially weighted moving average signed-rank chart for monitoring location. Computational Statistics and Data Analysis, 2013, 55(8):2490-2503.
9. A Willersrud, M Blanke, L Lmsland. Incident detection and isolation in drilling using analytical redundancy relations. Control Engineering Practice, 2015, 41:1-12.
10. A S Willsky, H L Joned. A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems. Automatic Control IEEE Transactions on, 1976, 21(1):108-112.
11. A Willersrud, M Blanke, L Imsland, et al. Drillstring Washout Diagnosis Using Friction Estimation and Statistical Change Detection. IEEE Transactions on Control Systems Technology, 2015, 23(5):1886-1900.
12. A Willersrud, M Blanke, L Imsland, et al. Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection. Journal of Process Control, 2015, 30:90-103.
13. C Aldrich, L Auret. Unsupervised process monitoring and fault diagnosis with machine learning methods. London:Springer Publishing Company, Incorporated, 2013:1-15.
14. B Yildiz. Development of a hybrid intelligent system for on-line real-time monitoring of nuclear power plant operations. Boston:Massachusetts Institute of Technology, 2003.
15. A J Tickle. Morphological scene change detection for night time security. Proceedings of SPIE-The International Society for Optical Engineering, 2012, 8540(24):2288-2291.
16. H Wang, C Zhang, T Shi, et al. Real-time EEG-based detection of fatigue driving danger for accident prediction. International Journal of Neural Systems, 2015, 25(2):1550002.
17. K C Huang, T Y Huang, C H Chuang, et al. An EEG-based fatigue detection and mitigation system. International Journal of Neural Systems, 2016, 26(4):1650018.
18. M Karapirli, E Kandemir, S Akyol, et al. Failure mode and effect analysis in asset maintenance:a multiple case study in the process industry. International Journal of Production Research, 2013, 51(4):1055-1071.
19. J Lee, F Wu, W Zhao, M Ghaffari, et al. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mechanical Systems and Signal Processing, 2014, 42(1-2):314-334.
20. Y Urabe, K Yamanishi, R Tomioka, et al. Real-time change-point detection using sequentially discounting normalized maximum likelihood coding. Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Shenzhen, China, Springer-Verlag, May 24-27, 2011:185-197.
21. W Yang, P J Tavner, W Tian. Wind turbine condition monitoring based on an improved spline-kernelled Chirplet transform. IEEE Transactions on Industrial Electronics, 2015, 62(10):6565-6574.
22. C T Lin, C H Chuang, HC S Huang, et al. Wireless and wearable EEG system for evaluating driver vigilance. IEEE Transactions on Biomedical Circuits and Systems, 2014, 8(2):165-176.
23. A Pouliezos, G S Stavrakakis. Real time fault monitoring of industrial processes. Netherlands:Kluwer Academic Publishers, 2013:463-519.
24. G L Lu, Y Q Zhou, C H Lu, et al. A novel framework of changepoint detection for machine monitoring. Mechanical Systems and Signal Processing, 2017, 83:533-548.
25. J L Doob. Boundary properties of functions with finite Dirichlet integrals. Annales-Institut institute Fourier, 1962, 12:573-621.
26. V Vovk, I Nouretdinov, A Gammerman. Testing exchangeability on-line. Machine Learning, Proceedings of the Tweritieth International Conference DBLP, Washington, August 21-24, 2003:768-775.
27. E Tsamoura, A Gounaris, Y Manolopoulos. Incorporating change detection in the monitoring phase of adaptive query processing. Journal of Internet Services and Applications, 2016, 7(1):1-18.
28. A Smola, A Gretton, L Song, et al. A Hilbert space embedding for distributions. International Conference on Algorithmic Learning Theory, Sendai, Japan, Springer Berlin Heidelberg, October 1-4, 2007:13-31.
29. P L Bartlett, S Mendelson. Rademacher and Gaussian Complexities:Risk Bounds and Structural Results. Journal of Machine Learning Research, 2002, 3:463-482.
30. P C Mahalanobis. On the generalized distance in statistics. Proceedings of the National Institute of Sciences, 1936, 2:49-55.
