Adaptive Sparse Representation-Based Minimum Entropy Deconvolution for Bearing Fault Detection

Sun, Yuanhang; Yu, Jianbo

doi:10.1109/tim.2022.3174278

Cited by 17 publications

(11 citation statements)

References 50 publications

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“…Considering the dominating function of the IESCFFOgram is to enhance the weak fault frequency, in order to present its superiority, we also give the ADF values corresponding to EES instead of IESCFFOgram, it can be seen that the values is obviously lower compared with IES, and they are sensitive to strong noise, especially if the standard deviation exceeds 10, they fluctuate within. 10,16 On the contrary, the proposed method demonstrated good resistance to the fluctuation. The above analysis fully illustrates that, in the presence of strong interference, the proposed method is a better choice for revealing the hidden fault information.…”

Section: Anti-interference Performance Evaluationmentioning

confidence: 89%

“…Step4: Inverse transformation. The remarkable periodic transients X L e is obtained in formula (10).…”

Section: Solving Solution Of Second Problem: a Neighcoeff Threshold B...mentioning

confidence: 99%

“…Compared with time-frequency, morphological component analysis (MCA) 7,8 uses parameterized waveform which can self-adaptive conform to actual signal structure characteristic and directly extract fault frequency consequently. Recently, some new approaches, for example, dynamic model theory, 9 based on blind deconvolution method, a series of improvement algorithms, such as minimum entropy deconvolution, 10 maximum correlated kurtosis deconvolution, 11 multipoint optimal minimum entropy deconvolution adjusted 12 also have been developed and applied for detecting fault. By utilizing filter technology, a series of application methods are utilized to enhance the repetitive transients induced by the localized defect in rolling bearing or determine the frequency band containing diagnostic information, such as spectral kurtosis, 13 Autogram, 14 and average Inforgram.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Yan¹,

Du²,

Ding

et al. 2022

Advances in Mechanical Engineering

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Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology.

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Section: Anti-interference Performance Evaluationmentioning

confidence: 89%

“…Step4: Inverse transformation. The remarkable periodic transients X L e is obtained in formula (10).…”

Section: Solving Solution Of Second Problem: a Neighcoeff Threshold B...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Yan¹,

Du²,

Ding

et al. 2022

Advances in Mechanical Engineering

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show abstract

“…Sparse representation is an advanced feature extraction approach. Recently, it has been widely employed for vibration-based bearing fault diagnosis due to its high flexibility of signal expression [21,22]. The concept of sparse representation herein refers to selecting the optimal linear combination of atoms in a complete dictionary D ∈ R N×M (M > N) to represent the whole or most of the fault impulse responses.…”

Section: A Review Of Sparse Representationmentioning

confidence: 99%

Bearing fault-induced feature enhancement via adaptive multi-band denoising model

Zhao¹,

Zhang²,

Zhang³

et al. 2023

Meas. Sci. Technol.

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To accurately extract the bearing fault-induced impulse features from the vibration signals corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band denoising model based on the Morlet wavelet filter and sparse representation is put forward. First, to locate the desired frequency band associated with fault components, the Morlet wavelet filter is employed to band-pass the signal from the perspective of the frequency-domain. Herein, an improved Protrugram-based index, termed as windowed envelope spectral kurtosis, is designed as the objective function to choose the optimal center frequency and the bandwidth of the Morlet wavelet filter. Furthermore, benefitting from the time-domain characteristics of the vibration signal, the in-band noise is eliminated by sparse representation. One of the critical parameters (resonance frequency) of the wavelet atom used in the sparse representation dictionary is directly taken as the center frequency of the Morlet wavelet filter, which makes full use of the information derived from the filter, and thus significantly improves the calculation efficiency. Finally, the recovery signal is demodulated by the Hilbert transform to extract the fault characteristic frequency. The effectiveness and superiority of the proposed method are demonstrated through a complete analysis of the simulated, experimental, and engineering signals, as well as a comparison with such prevalent methods as Kurtogram, individual sparse representation, and Morlet wavelet filter combined with the maximum correlation kurtosis deconvolution.

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“…For example, Wiggins [15] et al proposed the minimum entropy deconvolution method (MED). Sun [16] et al proposed the adaptive sparse representation minimum entropy deconvolution method (AdaSRMED), which improved the shortcomings of MED, and verified the feasibility of the method through experiments. Sun [17] et al proposed the adaptive CYCBD method to realize the fault feature extraction of gearboxes, and McDonald et al [18] proposed the maximum correlation kurtosis deconvolution (MCKD), which is an ideal method for early weak fault feature extraction of low signal-to-noise ratio signals.…”

Section: Introductionmentioning

confidence: 97%

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

2022

Processes

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In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults.

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Adaptive Sparse Representation-Based Minimum Entropy Deconvolution for Bearing Fault Detection

Cited by 17 publications

References 50 publications

Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Bearing fault-induced feature enhancement via adaptive multi-band denoising model

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

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