Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

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Keywords Graph model, Machine monitoring, Martingale test, Structural change detection
Persistent URL dx.doi.org/10.1016/j.ymssp.2017.06.003
Journal Mechanical Systems and Signal Processing
Citation
Lu, G. (Guoliang), Liu, J, & Yan, P. (Peng). (2018). Graph-based structural change detection for rotating machinery monitoring. Mechanical Systems and Signal Processing, 99, 73–82. doi:10.1016/j.ymssp.2017.06.003