Fault Diagnosis of Electric Impact Drills Based on Time-Varying Loudness and Logistic Regression

Abstract

As the main component of an electric impact drill, the gearbox is used to decelerate and transmit power; damage and failure to the gears often lead to the transmission system's failure. Therefore, as the core component of power transmission, the fault detection and diagnosis of gearbox devices have attracted increasing attention. This paper presents a psychoacoustic-based fault diagnosis method for gears of electric impact drills. The proposed methods employ acoustic signals and the time-varying loudness theory of psychoacoustic parameters. Two states of electric impact drills were analyzed: an electric impact drill with healthy gears and an electric impact drill with faulty gears. A feature extraction peak-to-average ratio (PAR) method based on the time-varying loudness spectrum was described and implemented to compute the feature vectors. The classification was carried out by applying logistic regression (LR). This paper provides the results of an acoustic analysis of electric impact drills. The results had a good recognition rate and the total accuracy of recognition of EIDs based on the PAR with LR was 97%. This method simulates the human auditory perception to detect the gear components of an electric impact drill, which can replace the traditional artificial listening detection method.