A novel weak fault signal detection approach for a rolling bearing using variational mode decomposition and phase space parallel factor analysis

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency-shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.

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Delineation of molecular structure modification during coagulation of mixed camel and cow milk by mid‐infrared spectroscopy and parallel factor analysis

Journal of Food Processing and Preservation ◽

10.1111/jfpp.15839 ◽

2021 ◽

Author(s):

O. Boukria ◽

J. Wang ◽

J. Safarov ◽

A. Gharsallaoui ◽

F. Leriche ◽

...

Keyword(s):

Molecular Structure ◽

Factor Analysis ◽

Infrared Spectroscopy ◽

Parallel Factor Analysis ◽

Cow Milk ◽

Structure Modification ◽

Mid Infrared ◽

Mid Infrared Spectroscopy ◽

Parallel Factor

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An optimized variational mode extraction method for rolling bearing fault diagnosis

Structural Health Monitoring ◽

10.1177/14759217211006637 ◽

2021 ◽

pp. 147592172110066

Author(s):

Bin Pang ◽

Mojtaba Nazari ◽

Zhenduo Sun ◽

Jiaying Li ◽

Guiji Tang

Keyword(s):

Extraction Method ◽

Fitness Function ◽

Rolling Bearing ◽

Initial Guess ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Bearing Fault ◽

Mode Decomposition ◽

Two Parameters ◽

Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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