Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Guo, Junchao; Shi, Zhanqun; Li, Haiyang; Zhen, Dong; Gu, Fengshou; Ball, Andrew

doi:10.1016/j.isatra.2021.03.005

Cited by 12 publications

(6 citation statements)

References 48 publications

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“…Considering the existence of multiple AM–FM components around meshing frequency harmonics in the vibration signal, the signal model can be expressed as 33,34 :…”

Section: Lmsb For Multiple Am–fm Modulation Analysismentioning

confidence: 99%

A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis

Guo

Yang

et al. 2023

Structural Health Monitoring

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This paper proposes a novel multiple amplitude modulation and frequency modulation (AM–FM) demodulation method based on local modulation signal bispectrum (LMSB), which can demodulate the fault features of different components from the gearbox signal with multi-mesh frequency bands and multi-modulation components. Firstly, the collected measurement signal is decomposed into a series of sub-band signals, and the generated sub-band signals are demodulated by modulation signal bispectrum (MSB), which realizes the simultaneous demodulation of multi-mesh frequency bands and multi-modulation components. Subsequently, each sub-band signal resulting from MSB demodulation is synchronously averaged to generate the LMSB, which removes random noise and interference frequencies from the sub-band signals. Finally, the periodic modulation intensity (PMI) is utilized to evaluate the ability of the LMSB to demodulate multi-component signals with AM–FM components. The effectiveness of LMSB in processing multi-component signals with AM–FM components is demonstrated by numerical simulation signals and experimental analysis. Analysis results demonstrate the superiority of LMSB in multiple AM–FM demodulation compared with MSB, envelope analysis (EA) and Teager energy operator (TEO). This research provides a novel perspective for gearbox fault detection.

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“…Considering the existence of multiple AM–FM components around meshing frequency harmonics in the vibration signal, the signal model can be expressed as 33,34 :…”

Section: Lmsb For Multiple Am–fm Modulation Analysismentioning

confidence: 99%

A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis

Guo

Yang

et al. 2023

Structural Health Monitoring

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“…A fault defect index was introduced for assessment in order to further contrast the effectiveness of various methods for the extraction of fault features. According to the literature, [ 37 , 38 ] provides the formula for calculating the fault defect index (FDI).

where

is the fault defect index at the corresponding frequency,

is the envelope spectrum amplitude of the relevant frequency, and

denotes the fault frequency.…”

Section: Simulation Experimentsmentioning

confidence: 99%

A New Method of Wheelset Bearing Fault Diagnosis

Sun

Yang

Yao

et al. 2022

Entropy

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During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances. This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Ramanujan subspace decomposition. MOMEDA optimally filters the signal and enhances the shock component corresponding to the defect, after which the signal is automatically decomposed into a sequence of signal components using Ramanujan subspace decomposition. The method’s benefit stems from the flawless integration of the two methods and the addition of the adaptable module. It addresses the issues that the conventional signal decomposition and subspace decomposition methods have with redundant parts and significant inaccuracies in fault feature extraction for the vibration signals under loud noise. Finally, it is evaluated through simulation and experimentation in comparison to the current widely used signal decomposition techniques. According to the findings of the envelope spectrum analysis, the novel technique can precisely extract the composite flaws that are present in the bearing, even when there is significant noise interference. Additionally, the signal-to-noise ratio (SNR) and fault defect index were introduced to quantitatively demonstrate the novel method’s denoising and potent fault extraction capabilities, respectively. The approach works well for identifying bearing faults in train wheelsets.

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“…It is worth noting that the fitness function should be determined before Fast-SC slices selection. Currently, various measurement indicators, such as correlation coefficient [22], sparse measurement [27], kurtosis [23] and entropy [28], have been widely applied to evaluate dynamic parameters in iteration optimization algorithm. Entropy is the measurement criterion of information uncertainty.…”

Section: Optimization Of Fast-sc Slicementioning

confidence: 99%

“…Morphological filter (MF) is a prominent anti-noise algorithm, which aims to modify the geometric characteristics of the signal through structural elements (SE). The merits of MF in removing random noise and narrowband impulses have been recognized in many applications [22][23]. Lately, Li et al [24] divided MF into two categories based on the effect of impulse feature extraction, one for feature extraction and the other for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Fast Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery

Guo

Zhen

et al. 2022

IEEE Trans. Instrum. Meas.

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Periodic impulse components are a typical symptom of rotating machinery failure, which are often masked by heavy background noise. It is of great practical significance to research how to obtain periodic impulse components to achieve fault diagnosis of rotating machinery. Fast spectrum correlation (Fast-SC), as a typical non-stationary and nonlinear signal processing method, has been studied in feature extraction by suppressing background noise to enhance periodic impulse components. However, effectively determining the bandwidth of Fast-SC is still a challenging issue. To address this issue, a novel method called Fast-SC detector is developed, which decomposes the measured signal of rotating machinery into multiple Fast-SC slices with different frequency bands. The whale optimization algorithm (WOA) is utilized to optimize the number of Fast-SC slices with minimum mean entropy as the optimal Fast-SC bandwidth. In addition, an adaptive combination morphological filter (ACMF) is applied to suppress the residual noise and narrowband impulses in the WOA-based Fast-SC slices to enhance the fault features. Finally, the Fast-SC detector is constructed using the average denoising Fast-SC slices to infer the type of rotating machinery. The proposed Fast-SC detector is compared with existing methods by applying simulation signal model and experimental data. The results prove that Fast-SC detector is superior to some excellent periodic impulse extraction algorithms in extracting the symptoms of rotating machinery failure.

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Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Cited by 12 publications

References 48 publications

A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis

A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis

A New Method of Wheelset Bearing Fault Diagnosis

Fast Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery

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