Motor and bearing multi-fault diagnosis with sound quality metrics

Abstract

The occurrence of multi-faults is more critical as compared to single faults, and it creates unfavourable working conditions. The number of contact-based sensors and their placement plays a crucial role in diagnosing the multi-faults. The use of non-contact-based acoustic sensors could be a cost-effective potential solution to this problem. In this work, two rotating components, a motor and a separate bearing of a system situated away from the motor, were considered for forming system-level multi-fault conditions. The acoustic signature of various multi-fault conditions was acquired at different rotational speeds. Thereafter, a moving window was employed on the acquired raw acoustic signals to extract statistical and sound quality features. A feature fusion approach was implemented on the extracted features, and a machine learning model was trained with optimised hyperparameters. A comparative study was performed to analyse the model's performance, which was trained with the extracted statistical, sound quality and fused features. It was found that the proposed feature fusion-based methodology was helpful for diagnosing multi-faults with acoustic signatures. The importance of psychoacoustical features on model performance was also observed. It mitigated the complexity of mounting the sensors and showed the potential usage of sound quality metrics for multi-fault diagnosis.