Compound fault diagnosis of rolling bearing using PWK-sparse denoising and periodicity filtering

Meng, Jing; Wang, Hui; Zhao, Liye; Yan, Ruqiang

doi:10.1016/j.measurement.2021.109604

Cited by 19 publications

(11 citation statements)

References 35 publications

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“…As the damage expands, it usually leads to multiple damages and compound failure, such as damage on both blade and bearing. 43 At present, compound failure is also the main challenge of SHM. 44 To test the compound fault recognition ability of the proposed method, the simulation signal with compound faults is constructed, including the fault of blades and bearings.…”

Section: Resultsmentioning

confidence: 99%

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Song

et al. 2022

Structural Health Monitoring

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The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.

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Section: Resultsmentioning

confidence: 99%

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Song

et al. 2022

Structural Health Monitoring

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“…e cubic spline equation should be established according to (6) and (15). e key to solving the cubic spline equation is to solve the value of the coefficient.…”

Section: Cubic Spline (Cs) Fittingmentioning

confidence: 99%

“…Kim et al [ 14 ] outlined a domain adaptive semantic clustering method to diagnose malfunctions in rotating machinery. In addition, because the noise in the signal affects the diagnosis and the separation of composite malfunctions, Meng et al [ 15 ] proposed a method combining periodic weighted kurtosis—sparse denoising and periodic filtering to extract repeated pulses of composite malfunctions. Gonçalves et al [ 16 ] proposed a pure output malfunction diagnosis method based on Markov parameters using random implementation theory.…”

Section: Introductionmentioning

confidence: 99%

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

Yu²

2022

Computational Intelligence and Neuroscience

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Aiming at the quantitative diagnosis of shafting misalignment malfunction, a novel method based on speed signal with singular value decomposition and Hilbert transform (SVD-HT) and cubic spline fitting-Pareto particle swarm optimization-echo state network (CSF-PPSO-ESN) method is proposed. The malfunction diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment malfunction model. Then, the SVD-HT and CSF-PPSO-ESN methods are applied to obtain the relationship between the shaft misalignment malfunction and the amplitude of the time and the rotation frequency (fr) component of the speed signal. The parameters of the CSF-PPSO-ESN method are settled according to the shaft misalignment malfunction and the fr component of the speed signal. The accuracy of the proposed method is verified by using the fr component of the speed signal and the trained CSF-PPSO-ESN to obtain the value of the shaft misalignment malfunction. The repeating experimental results show that the diagnosing error of the shaft misalignment malfunction can reach less than ±10 μm. The method presented in this paper provides a novel way to diagnose shaft misalignment malfunction quantitatively.

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“…Once the failure of the rolling bearing, the operation of the whole mechanical equipment will become di cult, but as the supporting part of the rotating parts, the rolling bearing will inevitably su er from wear and tear. erefore, it is necessary to diagnose the failure status of the rolling bearing in time [1,2]. Although the extraction of frequency signals, sound signals, and other signals of rolling bearings can well re ect the fault, the extraction of these signals has higher requirements for the equipment and professionalism of personnel [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

Wang

2022

Mathematical Problems in Engineering

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Rolling bearings are the key components for the safe operation of mechanical equipment. It plays an irreplaceable role in the normal operation of mechanical equipment. Higher load makes higher failure rate of rolling bearing. Accurate identification of the fault location is an important step in the diagnosis of the rolling bearing fault. In recent years, the entropy features of rolling bearing vibration signals are usually extracted to identify fault. In this paper, a double feature extraction method based on slope entropy (SlE) and fuzzy entropy (FE) is proposed to recognize the fault state of rolling bearing through rolling bearing signals. In the single feature extraction experiment, the recognition rate of these two kinds of entropy is not high. Through the improvement of the single feature extraction experiment, SlE and FE are selected as two feature combinations. After combining approximate entropy (AE), SlE, FE, permutation entropy (PE), and sample entropy (SE). The identification rate of these combinations was calculated using k nearest neighbor (KNN). The result shows that the recognition rate of this combination is 98% and 3.3% higher than other combinations.

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Compound fault diagnosis of rolling bearing using PWK-sparse denoising and periodicity filtering

Cited by 19 publications

References 35 publications

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

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