Bearing performance degradation condition recognition based on a combination of improved pattern spectrum entropy and fuzzy C-means

[1]  Soumaya Yacout,et al.  Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions , 2019, J. Intell. Manuf..

[2]  Keheng Zhu Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance , 2018 .

[3]  Miin-Shen Yang,et al.  A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy , 2018, IEEE Transactions on Fuzzy Systems.

[4]  Zhaohong Deng,et al.  Cascaded Hidden Space Feature Mapping, Fuzzy Clustering, and Nonlinear Switching Regression on Large Datasets , 2018, IEEE Transactions on Fuzzy Systems.

[5]  Miin-Shen Yang,et al.  Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters , 2017, Pattern Recognit..

[6]  Bing Wang,et al.  Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means , 2017 .

[7]  Sanjay H Upadhyay,et al.  Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering , 2017 .

[8]  Sandeep Kumar,et al.  Compound fault prediction of rolling bearing using multimedia data , 2017, Multimedia Tools and Applications.

[9]  Xin Zhang,et al.  Condition multi-classification and evaluation of system degradation process using an improved support vector machine , 2017, Microelectron. Reliab..

[10]  Niancheng Zhou,et al.  Feature extraction and classification method for switchgear faults based on sample entropy and cloud model , 2017 .

[11]  Yuan Xu,et al.  Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction , 2017 .

[12]  Wei Xing Zheng,et al.  Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory , 2017, IEEE Transactions on Cybernetics.

[13]  Baoping Tang,et al.  Bearing performance degradation assessment based on time-frequency code features and SOM network , 2017 .

[14]  F. Namdari,et al.  High-Speed Protection Scheme Based on Initial Current Traveling Wave for Transmission Lines Employing Mathematical Morphology , 2017, IEEE Transactions on Power Delivery.

[15]  Jianbo Yu,et al.  Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring , 2017 .

[16]  Huihui Wang,et al.  Novel feature extraction method for cough detection using NMF , 2017, IET Signal Process..

[17]  Yongbo Li,et al.  Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.

[18]  David,et al.  A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox , 2017 .

[19]  P. S. Venugopala,et al.  Morphological Pattern Spectrum Based Image Manipulation Detection , 2017, 2017 IEEE 7th International Advance Computing Conference (IACC).

[20]  Pedro Alonso,et al.  Fuzzy mathematical morphology for color images defined by fuzzy preference relations , 2016, Pattern Recognit..

[21]  José M. Asua,et al.  A new approach for mathematical modeling of the dynamic development of particle morphology , 2016 .

[22]  Xiaoyang Tong,et al.  Fault diagnosis for power grid based on adaptive improved FCM algorithm , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[23]  David W. Coit,et al.  Reliability analysis and condition-based maintenance of systems with dependent degrading components based on thermodynamic physics-of-failure , 2015, The International Journal of Advanced Manufacturing Technology.

[24]  Hong-ru Li,et al.  Rolling Bearing Degradation State Identification Based on LPP Optimized by GA , 2016 .

[25]  Diego Cabrera,et al.  Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram , 2016 .

[26]  Philippe Andrey,et al.  MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ , 2016, Bioinform..

[27]  Dong Wang,et al.  Assessing Bayesian model averaging uncertainty of groundwater modeling based on information entropy method , 2016 .

[28]  Hong-ru Li,et al.  Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy , 2016, Journal of Failure Analysis and Prevention.

[29]  Sam Z. Sun,et al.  A modified Fuzzy C-Means (FCM) Clustering algorithm and its application on carbonate fluid identification , 2016 .

[30]  Huajiang Ouyang,et al.  A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation , 2016 .

[31]  Chen Lu,et al.  Health assessment for rolling bearing based on local characteristic-scale decomposition — Approximate entropy and manifold distance , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).

[32]  Diego Cabrera,et al.  Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.

[33]  Witold Pedrycz,et al.  Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values , 2016, Knowl. Based Syst..

[34]  Heping Li,et al.  A condition-based maintenance policy for multi-component systems with Lévy copulas dependence , 2016, Reliab. Eng. Syst. Saf..

[35]  Ling-Li Cui,et al.  Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault , 2016 .

[36]  Xianfang Wang,et al.  Online Fault Diagnosis for Biochemical Process Based on FCM and SVM , 2016, Interdisciplinary Sciences: Computational Life Sciences.

[37]  Bing He,et al.  Phase Space Similarity as a Signature for Rolling Bearing Fault Diagnosis and Remaining Useful Life Estimation , 2016 .

[38]  Diego Cabrera,et al.  Observer-biased bearing condition monitoring: From fault detection to multi-fault classification , 2016, Eng. Appl. Artif. Intell..

[39]  Sulaiman AlYahya,et al.  The techno-economic potential of Saudi Arabia׳s solar industry , 2016 .

[40]  Bin Zhang,et al.  Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings , 2016, Qual. Reliab. Eng. Int..

[41]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[42]  Sanja Jeremic,et al.  Mathematical modeling of the neuron morphology using two dimensional images. , 2016, Journal of theoretical biology.

[43]  Jin Chen,et al.  The changes of complexity in the performance degradation process of rolling element bearing , 2016 .

[44]  Minqiang Xu,et al.  A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .

[45]  Luo Yiping,et al.  Bearing Fault Prediction System Design Based on SPC , 2016, Journal of Failure Analysis and Prevention.

[46]  Wen-Jing Wang,et al.  Erratum to: Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault , 2016 .

[47]  Vivek Sharma,et al.  Bearing Fault Evaluation for Structural Health Monitoring, Fault Detection, Failure Prevention and Prognosis , 2016 .

[48]  Hamid Reza Karimi,et al.  A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management , 2016 .

[49]  Diego Cabrera,et al.  Fuzzy determination of informative frequency band for bearing fault detection , 2016, J. Intell. Fuzzy Syst..

[50]  G. J. Hwang,et al.  Aspect Ratio Effect on Convective Heat Transfer of Radially Outward Flow in Rotating Rectangular Ducts , 1994 .