Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

Kumar, Anil; Berrouche, Yaakoub; Zimroz, Radosław; Vashishtha, Govind; Chauhan, Sumika; Gandhi, C.P.; Tang, Hesheng; Xiang, Jiawei

doi:10.1016/j.measurement.2023.112615

Cited by 24 publications

(10 citation statements)

References 36 publications

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“…The improved method is called ensemble empirical mode decomposition (EEMD). To extract weak defect features for the identification of bearing defects, Kumar et al [ 13 ] established a non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) based methodology.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

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Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

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“…Nevertheless, EMD is accompanied by drawbacks such as endpoint effects, modal mixing, and a lack of theoretical foundation [10][11][12]. Ensemble empirical mode decomposition (EEMD) automatically maps signal regions of different scales to an appropriate scale related to white noise by incorporating Gaussian white noise in the original signal [13]. The method resolves the issue of modal mixing present in EMD.…”

Section: Introductionmentioning

confidence: 99%

An adaptive threshold-selected symplectic geometry mode decomposition for application to multi-modulation complex fault signals

Lv,

Li,

Xiong

et al. 2024

Meas. Sci. Technol.

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The signals obtained from complex mechanical systems are characterized by multilevel modulation and strong noise, which can lead to difficulties in fault feature extraction. Symplectic geometry mode decomposition (SGMD) proves to be a valid approach for decomposing signals. However, inaccurate threshold selection in the iterative decomposition process can compromise the quality of fault diagnosis results. To address the shortcomings of SGMD, this paper proposes adaptive symplectic geometry mode decomposition (A-SGMD) with adaptive threshold selection for fault diagnosis. Based on minimum dispersion entropy indicators, correlation coefficient and stopping thresholds are adaptively chosen using the proposed enhanced dung beetle optimizer (eDBO) algorithm. Then the optimal symplectic geometry component is filtered based on the value of the integrated indicators after decomposition. Finally, the optimal symplectic geometry component is analyzed by envelope demodulation to extract gear fault information. Through simulation and experimental analysis, this method surpasses SGMD and other signal decomposition methods in the aspect of fault feature extraction and noise robustness. Additionally, the results indicate an increase in feature energy ratio by 2.14% to 9.85% compared to SGMD. The paper demonstrates that the proposed method extracts the fault feature frequencies of gears more effectively in complex mechanical systems.

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“…Wavelet transform (WT) analyzes signals in both time and frequency domains, offering superior time-frequency resolution and detecting transient fault signals [6]. Ensemble Empirical Mode Decomposition (EEMD) decomposes bearing signals into different time scales to extract relevant information, addressing issues like mode mixing and spectral leakage present in conventional EMD [7]. Since bearings have a high failure rate, Zhu et al [8] proposed a novel feature fusion approach for bearing fault feature extraction and diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

Dave,

Borade,

Agrawal

et al. 2024

Machines

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Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM.

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Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

Cited by 24 publications

References 36 publications

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

An adaptive threshold-selected symplectic geometry mode decomposition for application to multi-modulation complex fault signals

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

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