A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform

الملخص EN

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.