An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis

Abstract

Vibration and wear debris analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. In practice, these two techniques are usually conducted independently, and can only diagnose about 30–40% of faults when used separately. However, recent evidence shows that combining these two techniques provides greater and more reliable information, thereby resulting in a more effective maintenance program with large cost benefits to industry. In this paper, the correlation of vibration analysis and wear debris analysis was investigated. An experimental test rig consisting of a worm gearbox driven by an electric motor was set up to examine the correlation of the two techniques under various wear conditions. Three tests were conducted under the following conditions: (a) lack of proper lubrication, (b) normal operation, and (c) with the presence of contaminant particles added to the lubricating oil. Oil samples and vibration data were collected regularly. Wear debris analysis included the study of particle number and size distribution, the examination of particle morphology and types to determine possible wear mechanisms, and the analysis of chemical compositions to assess wear sources. Fault detection in the vibration signature was compared with the particle analysis. The results from this paper have given more understanding on the dependent and independent roles of vibration and wear debris analyses in machine condition monitoring and fault diagnosis.