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Fault diagnosis in reciprocating compressor bearings: an approach using LAMDA applied on current signals

Abstract:

Condition monitoring is one of the most important activities to implement pbkp_redictive maintenance in industrial processes and perform fault diagnosis. Vibration is the most used signal for this purpose, however current signals arise as a non-intrusive alternative to condition monitoring. On the other hand, data driven approaches becomes as a way to develop fault classifiers by using Machine Learning. This paper proposes the development of a fault classifier for diagnosing failures in the bearings of a reciprocating compressor by using the current signals measured from the induction machine that power the mechanical device. The proposal applies cluster validity assessment for feature selection, and a LAMDA-based model for classification. Results show that this proposal can diagnose three failure modes with a precision over 90%.