Fault Feature Extraction Of Rotating Machinery Research Articles

Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction

Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction

A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index

Feature extraction from vibration signals plays a vital role in rotating machinery fault diagnosis. The noise contained in the signals will interfere with the fault feature extraction result. Wavelet denoising (WD) is a commonly used method to reduce the noise, but its parameters are generally selected based on subjective experience. With this problem in mind, an adaptive wavelet denoising (AWD) method is proposed in this paper. Using permutation entropy to evaluate the signal noise level and taking its minimum value as the fitness function, the whale optimization algorithm is applied to optimize the WD parameters. Based on the AWD method and a synthetic detection index, a new feature extraction approach is proposed. Results from simulation experiments and engineering applications prove that the signal denoising performance of the AWD method and the fault feature extraction approach are satisfactory.

Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions

Rotating machinery signals are usually dominated by rotating frequency harmonics, and the presence of these frequency components or change in their magnitudes indicate health condition. Order spectrum is widely applied in rotating machinery fault feature extraction, because of its capability in intuitive spectral representation of rotating frequency harmonics in order domain. However, some speed non-synchronous components are often present. They show wide-band features in order spectra, and hinder accurate identification of rotating frequency harmonic orders. To address this issue, a scheme is proposed to identify and remove speed non-synchronous components of either constant or time-varying frequency. To this end, surrogate test is generalized through new candidate construction method and nonstationarity based indicator for test criterion, to identify true nonstationary signal components adaptively and eliminate subjective influences. Vold-Kalman filter is used to separate signal components by exploiting its capability in mono-component decomposition of complex nonstationary signals. The proposed method is validated through analyses of both induction motor stator current signals and hydraulic turbine rotor vibration signal. The results demonstrate its advantages over conventional computed order spectrum analysis.

Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy.

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time–frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method—the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)—is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

Wavelet Algorithm in Rotating Machinery Fault Feature Extraction

A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry. Key words: Rotating Machinery; bearings; Wavelet algorithm; Hilbert demodulation