An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox

Xu, Yuanbo; Cai, Zongyan; Cai, Xiaoyan; Ding, Kai

doi:10.1007/s12206-019-0505-9

Cited by 21 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MOMEDA sets the position and weight of the periodic impacts sequence, and introduces multipoint D-Norm deconvolution algorithm to solve optimal filter with a non-iterative process. And MOMEDA does not need the preprocessing stage for non-integer sample period, overcoming limitations of MED and MCKD, it is widely used in signal noise reduction [32][33][34]. Like that, MOMEDA is used as a post-filter to further process RSSD decomposed signal, for making the fault information more prominent.…”

Section: Adaptive Rssd Construction Based On Lion Swarm Algorithmmentioning

confidence: 99%

Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition

Wang

Jiang

et al. 2021

Eng. Res. Express

View full text Add to dashboard Cite

The existence of periodic impacts in collected vibration signal is the representative symptom of rolling bearing localized defect. Due to the complicacy of the working condition, the fault-related impacts are usually submerged in other ingredients. This article proposes an adaptive Resonance-based Sparse Signal Decomposition (RSSD) for extracting the fault features of rolling bearings. Adaptive RSSD is constructed to fetch the impacts from collected vibration signal, by making RSSD decomposed signal kurtosis value maximum using Lion Swarm Algorithm (LSA). Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is further performed to enhance the amplitude and periodicity of impacts contained in RSSD decomposed signal, so that fault feature is highlighted. The superiority and availability of proposed strategy are validated by applying to single fault feature extraction of an experimental dataset and compound faults feature extraction of a locomotive rolling bearing.

show abstract

Section: Adaptive Rssd Construction Based On Lion Swarm Algorithmmentioning

confidence: 99%

Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition

Wang

Jiang

et al. 2021

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Although it can provide a window that can adaptively adjust with the change of signal frequency, it improves time accuracy by sacrificing frequency accuracy. The empirical modal decomposition and the minimum entropy deconvolution 14 suffer from edge effects leading to distortion.…”

Section: Introductionmentioning

confidence: 99%

Signals hierarchical feature enhancement method for CNN-based fault diagnosis

Zhang

Wang

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The high noise and low energy characteristics of the raw signals collected by sensors make the signal features weak and difficult to train. The purpose of this paper is to enhance the fault features of abnormal signals using the hierarchical feature enhancement method (HFE) which contains three layers. In the first layer, the signals are decomposed into multiple modals estimated by a variational optimization problem. The modals we choose are used to reconstruct the signals to form a complex matrix used to extract features in the second layer. In the third layer, the feature signals are converted into two-dimensional space and then are input into the convolutional neural network (CNN) for fault diagnosis after HFE since CNN helps to mine deeper features and compute in parallel on a large scale. The experimental results effectively verify the performance of the HFE for enhancing the weak fault features and preventing noise interference. The signals analyzed by HFE used as input greatly improve the diagnosis ability of CNN. In addition, the ablation and comparison experiments are conducted which still show superiority.

show abstract

“…Many methods have been successfully applied for diagnosing faults of contemporary wind turbine gearbox, such as wavelet transform (WT) [3], ensemble empirical mode decomposition (EEMD) [4], empirical wavelet transform (EWT) [5], variational mode decomposition (VMD) [6], minimum entropy deconvolution (MED) [7] and their improved versions [8]. However, these methods require considerable prior knowledge and experience of fault diagnosis, especially for the skilled calculation of fault characteristic frequencies; thus, they are not suitable for ordinary workers without practical experience.…”

Section: Introductionmentioning

confidence: 99%

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Yan

She

et al. 2021

Entropy

View full text Add to dashboard Cite

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).

show abstract

An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox

Cited by 21 publications

References 27 publications

Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition

Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition

Signals hierarchical feature enhancement method for CNN-based fault diagnosis

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Contact Info

Product

Resources

About