A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Min

doi:10.1016/j.ymssp.2017.12.008

Cited by 166 publications

(105 citation statements)

References 38 publications

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“…Zhao et al [12] and [13] applied the improved local mean decomposition (LMD) to process the different bearing clearance fault states of a reciprocating compressor. Multiscale morphological filtering (MMF) was put forward by Li et al [14] to pre-process the vibration signals of planetary gearboxes before the fault extraction. Zhang et al [15] and Bi et al [16] utilized the empirical mode decomposition (EMD) and frequency modulated empirical mode decomposition (FM-EMD) to pre-process the vibration signals of a gearbox with different faults in diagnosing and monitoring the conditions of the gearbox.…”

Section: Fault Diagnosis Methods Based On Modified Multiscale Entropy mentioning

confidence: 99%

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Li¹,

Wang²,

Zhao³

et al. 2019

SV-JME

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According to the nonlinearity, non-stationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of a reciprocating compressor valve based on modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. First, the variational mode decomposition (VMD) method with superior anti-interference performance was utilized to analyse the strong non-stationarity vibration signals for all fault states. The modified multiscale entropy (MMSE) method provided for movingaverage procedures by replacing mean-average coarse-grained procedures was developed for the vibration signals after de-noising, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and to select the optimal sensitivity scale feature. Finally, a binary tree of support vector machine (BTSVM) was selected as the classifier to identify the reciprocating compressor valve fault type. By analysing the experimental data, it can be shown that the method can effectively identify the fault type of the reciprocating compressor valve. Keywords: fault diagnosis, reciprocating compressor valve, modified multiscale entropy, global distance evaluation, binary tree of support vector machine Highlights • An integrated fault feature extraction method of a reciprocating compressor valve based on the VMD-MMSE and GDE is proposed. • A consistent number K of band-limited intrinsic mode functions (BLIMFs) were selected based on a novel criterion for all fault states • The MMSE method provided for the moving-average procedure by replacing mean-average coarse-grained procedure was developed for the vibration signals, GDE was introduced to refine the eigenvectors for higher recognition efficiency and accuracy. • The effectiveness of this method is verified by the recognition results of BTSVM in comparison to other feature extraction methods.

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Section: Fault Diagnosis Methods Based On Modified Multiscale Entropy mentioning

confidence: 99%

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Li¹,

Wang²,

Zhao³

et al. 2019

SV-JME

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“…Sensitivity analysis aims to interrogate “how the output uncertainty of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input.” The most straightforward SA methods are the local sensitivity analysis (LSA) methods, which are defined based on partial derivatives or finite differences . Since they are computationally very cheap and easy, the majority of published literatures use LSA methods to interrogate the influences of input factors . However, one should note that the LSA indices depend on the nominal position of the base point, and are only informative around the base point.…”

Section: Introductionmentioning

confidence: 99%

“…3 Since they are computationally very cheap and easy, the majority of published literatures use LSA methods to interrogate the influences of input factors. 4,5 However, one should note that the LSA indices depend on the nominal position of the base point, and are only informative around the base point. Thus, LSA methods are unable to provide a thorough exploration for the influence of the input factors across the whole distribution space, especially in highly nonlinear and nonadditive models.…”

Section: Introductionmentioning

confidence: 99%

Importance measure method for error sources of degradation systems based on the present worth of expected Taguchi quality loss

Sun

Cui

et al. 2020

Quality & Reliability Eng

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Deteriorating of the error sources and the deviation of the quality characteristic (QC) of the product from the target value are ubiquitous in practice. Studying the relative contribution of the error sources to the QC deviation is helpful for improving the product performance effectively. In this study, we propose an error importance measure (EIM) method to quantify the relative contribution of error sources to the Taguchi quality loss that is caused by the QC deviation. The present worth of the expected Taguchi quality loss (ETQL) is used to characterize the deteriorating QC deviation. We use Taylor series expansion to decompose the time‐dependent ETQL and the present worth of ETQL into a series of fractions, representing the individual and the interaction contributions of the error sources. Based on the decomposition, the time‐dependent and the time‐independent EIM indices for one and a group of error sources are defined, respectively. The evaluation procedure for the EIM indices is provided and the properties are discussed. Finally, we provide a realistic application case to an aircraft wheel door mechanism to demonstrate the effectiveness of the proposed EIM method.

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“…Besides, as entropy is effective in detection the dynamic characteristics of time series, some methods use entropy in feature extraction and selection. Modified multi-scale symbolic dynamic entropy (MMSDE) [13] and modified hierarchical permutation entropy (MHPE) [14] were proposed to extract features for gearbox fault diagnosis. Zhao et al [15] combined the EEMD and multi-scale fuzzy entropy to extract more discriminative features.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Qian¹,

Li²,

Wang³

et al. 2018

J. vibroeng.

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Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF. . His current research interests include rotating machinery fault diagnosis and mechanical signal and information processing.

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A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

Cited by 166 publications

References 38 publications

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Importance measure method for error sources of degradation systems based on the present worth of expected Taguchi quality loss

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

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