Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis

Abstract Multi-sensors configuration has been popular in the field of condition monitoring of rotary machines. This paper proposes a novel multi-sensors based monitoring strategy that can be used to detect changes of machine running status during continuous operations. The base of the method is automatic change detection which is implemented via combining the multidimensional time-series analysis (MultiDTSA) with an extended autoregressive-integrated-moving-average (ARIMA) regression process. The ARIMA regression process is to quantify temporal anomalies for each deployed sensor, such that decision fusion can be allowed from all sensors under the architecture of MultiDTSA. In particular, a new fusion strategy is developed to consider differentiating contributes among multi-sensors for decision making. The final result is obtained by testing a null hypothesis. The proposed method has been evaluated based on an experimental setup: comparison with five representative techniques shows its promising results.

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