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• W2898172688 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." @default.
• W2898172688 created "2018-11-02" @default.
• W2898172688 creator A5019685555 @default.
• W2898172688 creator A5035955418 @default.
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• W2898172688 date "2019-02-01" @default.
• W2898172688 modified "2023-10-09" @default.
• W2898172688 title "Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis" @default.
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• W2898172688 doi "https://doi.org/10.1016/j.measurement.2018.10.089" @default.
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