Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Abstract

Condition monitoring (CM) for rotating machinery is becoming an essential task that allows detecting faults at early stages, preventing unexpected damage and catastrophic accidents. In machine fault diagnoses, the vibration signal analysis is the most widely used nondestructive technique for extracting relevant information. Recently, data acquisition systems using acoustic signals have also gained demand over other noncontact measurement techniques when the sensor locations on the machine are unavailable or the measurement procedure has a high risk for workers [1]. , acoustic signals are more vulnerable to environmental noise than vibration responses [2], making employing signal preprocessing techniques necessary to reduce the undesirable interferences and improve the lowsignal-to-noise ratio (SNR) [3]. Furthermore, the vast majority of reported acousticbased CMare focused on visual inspections, eluding to incorporate the signal preprocessing into automatic diagnoses systems [4].