Application of a new EWT-based denoising technique in bearing fault diagnosis

Abstract The vibration signal analysis is a popular method for extracting sensitive fault features. The vibration signals are usually contaminated by noise, and therefore the extracted features cannot be providing sufficient information about the bearing faults. In this paper, a new technique is introduced for denoising the vibration signals and recognizing the bearing faults based on the empirical wavelet transform (EWT). Firstly, the vibration signals are decomposed by the EWT method into a set of functions called the empirical modes. Then, the noise-dominate modes have been denoised by an improved thresholding function that has been recently presented. Finally, the kurtosis parameter and the envelope spectrum of the denoised signal are used for early fault detection and diagnosing the fault type, respectively. The result of the simulated signal and different experimental datasets illustrate that the presented work is preferable for the empirical mode decomposition based denoising technique in the early fault detection.