Abstract
Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railway axles, and turbines, etc. Therefore, the detection of rolling bearing faults has been a hot research topic in engineering practices. Envelope demodulation represents a fundamental method for extracting effective fault information from measured vibration signals. However, the performance of envelope demodulation depends heavily on the selection of the filter band and central frequencies. The empirical wavelet transform (EWT), a new signal decomposition method, provides a framework for arbitrarily segmenting the Fourier spectrum of an analysed signal. Scale-space representation (SSR) can adaptively detect the boundaries of the EWT; however, it has two shortcomings: slow calculation speeds and invalid boundary detection results. Accordingly, an EWT method based on optimized scale-space representation (OSSR), namely, the EWTOSSR, is proposed. The effectiveness of the EWTOSSR is verified by comparisons between the simulation and the experimental signals. The results show that the EWTOSSR can automatically and effectively segment the EWT spectrum to extract fault information. Compared with three well-known methods (the traditional EWT, ensemble empirical mode decomposition (EEMD), and the fast kurtogram), the EWTOSSR exhibits a much better fault detection performance.
Highlights
Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railways, and turbines [1]. ey are closely associated with the reliability of a mechanical system [2], which is a necessity to monitor their condition
Procedure of the Proposed Method e procedure of the proposed EWTOSSR method is summarized and it is shown in Figure 10. (1) Compute the Fourier spectrum of the analysed signal (2) Calculate the optimized scale-space plane (OSSP) (3) Detect the boundaries using the differences in the lengths of scale-space curves (4) Decompose the analysed signals using the empirical wavelet transform (EWT) (5) Extract the fault information from the squared envelope spectra of the decomposed signals
According the EWTOSSR procedure, the OSSP is shown in Figure 6. e differences in the length of the scale-space curve (SSC) are shown in Figure 8. e peak of the difference appears at an index of 2
Summary
Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railways, and turbines [1]. ey are closely associated with the reliability of a mechanical system [2], which is a necessity to monitor their condition. To resolve the fixed bandwidth problem associated with the spectral kurtosis and fast kurtogram approaches, the adaptive spectral kurtosis (ASK) technique was proposed [12]. SSR-based EWT is more adaptive and can extract bearing fault information It is computationally expensive, and some overly noisy spectrum segments exist that are subject to the presence of strong noise [29]. E differences in the lengths of the SSCs are used to determine the significant boundaries, and a novel EWT (EWTOSSR) fault detection method based on OSSR is proposed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
