An Early Fault Diagnosis Method for Ball Bearings of Electric Vehicles Based on Integrated Subband Averaging and Enhanced Kurtogram Method

Abstract

Faults of mechanical transmission systems generally occur in the rotating bearing part at high speeds, which causes problems such as performance degradation of transmission, generation of noise or vibration, and additional damage to connected adjacent systems. In this way, faults cause adverse effects to the entire system, such as deterioration and damage. The early detection and correction of bearing problems allows for improved system safety and the reduction of maintenance costs, resulting in efficient system operation. As a result, a variety of methods have been developed by many researchers in order to diagnose bearing mechanical defects, and one of the most representative methods is applying various signal processing techniques to vibration data. Wavelet packet transform (WPT) and kurtogram were used in this study to identify the frequency band that contained the fault component, and the enhanced kurtogram technique was used to analyze the fault. A technique for minimizing the effect of intermittent abnormal peak components caused by noise and external influences has been presented using sub-band averaging to detect early fault frequency component detection and fault development. Using the technique proposed in this study, the state of the bearing based on the degree of fault was evaluated quantitatively, and it was demonstrated experimentally that the bearing fault frequency could be detected at an early stage by the filtered data. In a situation where it is difficult to accept all the detailed design specifications and operating conditions of the complex mechanical systems at industrial sites, determining the degree of fault with simple time-series data and detecting fault components at an early stage is a practical analysis technique for fault diagnosis in the industrial field using various rotating bodies.