Fault detection and prognostic of abnormal equipment situations - Yang, Xi (2010)
In the machine tool industry, unexpected failure of rotary equipments can lead to severe part damage and costly machine downtime, affecting the overall production logistic and productivity. R&D activities in this area have increased tremendously in the last few years due to the need to maintain high valued equipments, to improve their reliability and to make them available when needed. This final year project is aimed to investigate the current machine condition monitoring technologies, and to develop a methodology which can be used to diagnose machine fault automatically with high accuracy and consistency. In recent years, wavelet transform has received considerable attention from the research community due to its ability in extracting time-dependent transient features from vibration signals with strong background noise [1]. However, the existing approach has shortcomings in parameter selection criterion and final envelope construction algorithm. In this project, the student aims to overcome the two shortcomings. The student made two improvements to the Morlet wavelet transform, and developed the reinforced Morlet wavelet transform. Three case studies were conducted to compare the reinforced Morlet wavelet transform with the existing approach, and the case studies prove the consistency and early-fault-detection ability of the reinforced Morlet wavelet transform.