A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Hu, Yongtao; Zhang, Shuqing; Jiang, Anqi; Zhang, Liguo; Jiang, Wanlu; Li, Junfeng

doi:10.1186/s10033-019-0356-4

Cited by 38 publications

(17 citation statements)

References 28 publications

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“…begin setup: 1) Initialize the FCM clustering parameters: the power index is 3, the maximum number of iterations is 20, the objective function termination tolerance is 1×10 6 .…”

Section: A Steps To Implement the Fuzzy C-means Clustering Based On mentioning

confidence: 99%

“…Due to the fact that mechanical failures occur gradually, there is often uncertainty in relation to which vibration signal characteristics should be extracted, and a fault state may not be identified from the eigenvalues extracted from the vibration signals. Thus, an effective way to identify failure modes is to use fuzzy c-means (FCM) clustering based on the fuzzy theory [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

et al. 2020

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As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery. INDEX TERMS Rotating machinery, Mutual dimensionless indexes, Fuzzy c-means clustering algorithm, Genetic algorithm, Simulated annealing algorithm.

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“…begin setup: 1) Initialize the FCM clustering parameters: the power index is 3, the maximum number of iterations is 20, the objective function termination tolerance is 1×10 6 .…”

Section: A Steps To Implement the Fuzzy C-means Clustering Based On mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

et al. 2020

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“…This research is partially supported by the National Natural Science Foundation of China (52005103, 71801046, 51775112, 51975121), the Guangdong Basic and Applied Basic Research Foundation (2019B1515120095), and the MoST International Cooperation Program (6)(7)(8)(9)(10)(11)(12)(13)(14).…”

Section: Fundingmentioning

confidence: 99%

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Yang¹,

Gjorgjevikj²,

Long³

et al. 2020

Preprint

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Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that it is able to accurately diagnose known types of defects, as well as to detect unknown defects, outperforming other state-of-the-art methods.

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“…e signal decomposition technology, such as local mean decomposition (LMD), wavelet decomposition, and empirical mode decomposition (EMD), can be applied to suppress noises and dig up the sensitive component containing more characteristic signatures. And applications of these methods in the field of fault identification have already got a certain amount of laudable effectiveness in recent decades [4][5][6]. Nevertheless, some inherent weaknesses of these methods impair their performances on fault characteristic extraction.…”

Section: Introductionmentioning

confidence: 99%

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

Wang

Tang

2020

Complexity

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The defect characteristics of rolling bearing are difficult to excavate at the incipient injury phase; in order to effectively solve this issue, an original strategy fusing recursive singular spectrum decomposition (RSSD) with optimized cyclostationary blind deconvolution (OCYCBD) is put forward to achieve fault characteristic enhanced detection. In this diagnosis strategy, the data-driven RSSD method without predetermined component number is proposed. In addition, a new morphological difference operation entropy (MDOE) indicator, which takes advantage of morphological transformation and Shannon entropy, is developed for confirming the influencing parameters of cyclostationary blind deconvolution (CYCBD). During the process of fault detection, RSSD is firstly adopted to preprocess the original signal, and the most sensitive singular spectrum component (SSC) is selected by the envelope spectrum peak (ESP) indicator. Then, the grid search algorithm is adopted to precisely confirm the optimal parameters and OCYCBD is further performed as a postprocessing technology on the most sensitive component to suppress the residual interferences and amplify the fault signatures. Finally, the enhanced fault detection of rolling bearing is able to achieve by analyzing the envelope spectrum of deconvolution signal. The feasibility of the proposed strategy is verified by the simulated and the measured signals, respectively, and its superiority is also demonstrated through several comparison methods. The results manifest this novel strategy has praisable advantages on weak characteristic extraction and intensification.

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A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Cited by 38 publications

References 28 publications

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

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