A review on the application of blind deconvolution in machinery fault diagnosis

Miao, Yongwu; Zhang, Boyao; Lin, Jing; Zhao, Ming; Liu, Hanyang; Liu, Zongyang; Li, Hao

doi:10.1016/j.ymssp.2021.108202

Cited by 141 publications

(56 citation statements)

References 138 publications

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“…16a-d and f. These results indicate that five frequency bands (4,800,3,200), (5,600, 1,600), (4,800, 1,067), (5,067,533) and (5,333,2,133) contain repetitive impulse features caused by bearing outer race fault. Because the frequency bands (5,600, 1,600), (4,800, 1,067) and (5,067, 533) have relatively narrow bandwidths, the SESs of their corresponding filtered signals exhibit better fault diagnosis effects.…”

Section: A Bearing Outer Race Faultmentioning

confidence: 76%

“…Thus, fault diagnosis of rolling bearings in key transportation, energy and chemical equipment is of great significance to ensure operational safety and improve economic benefits. Up to now, many signal processing-based methods have been developed for fault diagnosis of rolling bearings, such as blind deconvolution [2], signal decomposition [3], envelope analysis [4], stochastic resonance [5,6], morphological filtering [7], cyclic spectral analysis [8,9] and modulation signal bispectrum [10]. Among these methods, envelope analysis is one of the most commonly used methods for fault diagnosis of rolling bearings and other rotating mechanical components.…”

Section: Introductionmentioning

confidence: 99%

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IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen

Dong-li

Cheng

et al. 2022

JDMD

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The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

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Section: A Bearing Outer Race Faultmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen

Dong-li

Cheng

et al. 2022

JDMD

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“…The failure of gearboxes may cause unexpected accidents and economic losses. Therefore, accurately identifying and diagnosing faults is the key to ensuring that gearboxes normally operate [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

Mao

Zhang

Qiao

et al. 2022

Entropy

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The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

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“…DOA matches the conditions fitly. Based on acoustic emission, the information of frequency and power obtained by the sensor array holds the potential of locating angles of acoustic emission using RL algorithm [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Miao

Sun

et al. 2022

Sensors

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Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

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A review on the application of blind deconvolution in machinery fault diagnosis

Cited by 141 publications

References 138 publications

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

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