Application of Wavelet Transform in Fault Diagnosis

The application of wavelet transform in fault diagnosis represents a critical research domain. This article provides a comprehensive analysis of wavelet transform applications in fault diagnosis with concrete implementation examples and case studies. In the introduction section, we explore the fundamental principles of wavelet transform and its potential advantages in fault detection systems. The core section focuses on wavelet transform implementation for bearing fault diagnosis, covering both outer race fault detection and rolling element fault diagnosis methodologies. Additionally, we examine wavelet transform applications in gear fault diagnosis and investigate the potential of wavelet packet analysis for bearing fault detection. Through detailed discussions and practical cases, we aim to provide readers with deep insights into the significance and practical value of wavelet transform in the field of fault diagnosis. Implementation aspects include wavelet coefficient analysis using MATLAB's wavelet toolbox functions like cwt() for continuous wavelet transform and dwt() for discrete wavelet transform, which enable multi-resolution analysis of vibration signals to detect characteristic fault frequencies.