Detection of structural changes in machine state attracts increasing attention for the monitoring and prognosis of rotating machinery. Very recently, the graph model has been introduced and adopted for modeling of normal machine state so as to investigate the likelihood of potential changes by the use of Martingale-test. This paper expands the potential of the graph model toward correlation-analysis-based monitoring applications, where the problem of measuring graph similarity is the main challenge due to domain specificity. We first investigated six typical schemes taken from other areas for this purpose, and found that they show discriminative capacities when dealing with changes with different types in the machine condition monitoring scenarios. Subsequently, based on a procedure called “method of ranks,” we have chosen four schemes among them which are potentially promising for our usage. Meanwhile, based on these metrics, two machine learning based similarity metrics are proposed to further improve their practical values by combining them. At last, comprehensive theoretical interpretations and comparisons of presented methods are made both in simulated scenarios and real-engineering monitoring applications.

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Keywords Analytical models, Change detection, Condition monitoring, condition monitoring, graph modeling, health management, Measurement, Monitoring, Prognostics and health management, Reliability, Rotating machines, rotating machines, similarity metric
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Journal IEEE Transactions on Reliability
Wang, T. (Teng), Lu, G. (Guoliang), Liu, J, & Yan, P. (Peng). (2018). Graph-Based Change Detection for Condition Monitoring of Rotating Machines: Techniques for Graph Similarity. IEEE Transactions on Reliability. doi:10.1109/TR.2018.2866152