Critical evaluation and comparison of psychoacoustics, acoustics and vibration features for gear fault correlation and classification

摘要

Gear fault diagnosis has gained importance in the last few decades with the focus of fault diagnosis function for maintenance purpose. This paper investigates the ability of various vibration, acoustics and psychoacoustic features to correlate and classify faults in the gearbox. This study is motivated by the process of gearbox fault diagnosis at the end of the assembly line inspection of gearbox. It is observed that during the end of assembly line inspection, the gearbox is operated on a test bench and operator takes a decision about the presence of fault by listening to the sound emitted by the gearbox. This decision is based on an operator's judgement and past experience which involves subjectivity. Efforts are made to address this issue of end of the line inspection, by applying a scientific, objective psychoacoustic based technique which works on similar principles of listening. Experiments are performed in a laboratory by simulating four types of gear faults and vibration and acoustic signals are acquired to extract statistical features of vibration and acoustic signal along with psychoacoustic features. It is observed that different features respond to faults in different ways and changes in feature values are also dependent on loading condition. Therefore, correlating these features with faults is not simple and need intelligent techniques for correlating faults to features. This correlation of faults to features is attempted with different techniques like ANN and discriminant classifier. It is found that the psychoacoustic features have better ability to classify faults compared to acoustics and vibration statistical features which are found to be 93.02% using quadratic discriminant classifier and 95.93% using multilayer feed-forward back-propagation neural network. It is shown that the psychoacoustic based fault identification technique can be applied for the end of the assembly line inspection of gearbox. (C) 2020 Elsevier Ltd. All rights reserved.