Psychoacoustics—facts and models

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator

A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefor...

A new fault diagnosis method based on adaptive spectrum mode extraction

A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis

The psychoacoustics annoyance research based on EEG rhythms for passengers in high-speed railway

Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet

With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced by the proposed method.

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A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

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.

/pdf/a-rolling-bearing-fault-diagnosis-optimized-scale-space-2kc9y0mmfd.pdf

Zechao Liu

Papers

A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis

The psychoacoustics annoyance research based on EEG rhythms for passengers in high-speed railway

Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet

A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

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