Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor

Ahmed, M., Gu, Fengshou and Ball, Andrew (2012) Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor. In: 18th International Conference On Automation And Computing (ICAC), 2012. IEEE, Cardiff, UK. (In Press)

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Abstract

This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor. Principal Component Analysis (PCA) is based to develop a detection and diagnosis framework in that the effective diagnostic features are selected from the PCA of 14 potential features and a PCA model based detection method using and statistics is subsequently developed to detect various faults including valve leakage, suction valve leakage, inter-cooler leakage, loose drive belt, discharge valve leakage combined with suction valve leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with intercooler leakage. Moreover a study of Q-contributions has found two original features: Histogram Lower Bound and Normal Negative log- likelihood which allow full classification of different simulated faults.

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