Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Abstract

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always nonstationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it's flat for nonGaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bispectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

“…However, the EMD method lacks rigorous theoretical basis, and there is a serious phenomenon of modal aliasing in the decomposition process [13,14]. Variational mode decomposition (VMD) is a new adaptive signal decomposition method, which has a solid theoretical foundation and can solve the problem of mode mixing in EMD, but there are parameter optimization problems in VMD [15].…”

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

“…Empirical mode decomposition (EMD), as a formidable and effective time-frequency analysis method, is programed to analyze the nonstationary signals and can be adaptive to decompose 2 Shock and Vibration the confusion signal into intrinsic mode functions (IMFs) by the inherent characteristics of the signals [11][12][13]. Features extraction by EMD is appropriate for distinguishing different mechanical signals [14][15][16]. Wang et al [14] propose a novel feature extraction method by nonnegative EMD manifold in machinery fault diagnosis.…”

“…Wang et al [14] propose a novel feature extraction method by nonnegative EMD manifold in machinery fault diagnosis. Saidi et al [15] proposed an EMD-based fault diagnosis module to detect the incipient bearing faults based on the raw vibration signals. Ali et al [16] use EMD as feature extraction method then select the most important intrinsic mode functions and classify bearings defects by the artificial neural network (ANN).…”

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

“…In recent years, various intelligent fault diagnosis systems based on EMD [11][12][13][14][15], STFT [16][17][18], and WVD [19][20][21] 2 Shock and Vibration have been widely developed for monitoring the condition of bearings in rotating machines with varying degrees of success. However, for these time-frequency methods, some challenges exist in the application.…”