Study on rolling bearing on-line reliability analysis based on vibration information processing

Abstract The failure of rolling bearing is the foremost cause to the failure and breakdown of rotating machines. As the bearing vibration signal is full of periodic and nonlinear characteristics, using common time domain or frequency domain approaches cannot easily make an accurate estimation for the bearing healthy condition. In the paper, an efficient and effective fault diagnostic approach was proposed to accommodate the requirements for both real-time monitoring and accurate estimation of different fault types and their severities. Firstly, a four-dimensional feature extraction algorithm using entropy and Holder coefficient theories was proposed for extracting health status feature vectors from the vibration signals, and secondly a gray relation algorithm was employed for achieving bearing fault pattern recognition intelligently using the extracted feature vectors. The experimental study has illustrated the effectiveness of the proposed approach in comparison with the existing artificial intelligent methods, and can be suitable for on-line health status monitoring.

[1]  Xinpeng Zhang,et al.  A bearing fault diagnosis method based on the low-dimensional compressed vibration signal , 2015 .

[2]  Xiaozhi Hu,et al.  Effects of heterogeneity and load amplitude on fatigue rate prediction of a welded joint , 2016 .

[3]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[4]  Bill McKelvey,et al.  A COMPARISON OF THREE HURST EXPONENT APPROACHES TO PREDICT NASCENT BUBBLES IN S&P500 STOCKS , 2016, 1601.04188.

[5]  Can Wang,et al.  A new combination method for multisensor conflict information , 2016, The Journal of Supercomputing.

[6]  Wei Wang,et al.  The Controller Placement Problem in Software Defined Networking: A Survey , 2017, IEEE Network.

[7]  David He,et al.  Bearing fault diagnosis based on a new acoustic emission sensor technique , 2015 .

[8]  J. Cai.  Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics , 2015 .

[9]  Jin Chen,et al.  Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm , 2015 .

[10]  Zheng Pan,et al.  A NOVEL FAST FRACTAL IMAGE COMPRESSION METHOD BASED ON DISTANCE CLUSTERING IN HIGH DIMENSIONAL SPHERE SURFACE , 2017 .

[11]  Shaojiang Dong,et al.  Rotating Machine Fault Diagnosis Based on Locality Preserving Projection and Back Propagation Neural Network–Support Vector Machine Model , 2015 .

[12]  Yu Yang,et al.  The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis , 2015 .

[13]  Jiantao Zhou,et al.  Distribution of primary additional errors in fractal encoding method , 2014, Multimedia Tools and Applications.

[14]  Shuncong Zhong,et al.  Sine-modulated wavelength-independent full-range complex spectral optical coherence tomography with an ultra-broadband light source , 2015 .

[15]  Jingchao Li,et al.  A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory , 2016, PloS one.

[16]  Chao Wang,et al.  A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks , 2016, Sensors.

[17]  Yunpeng Cao,et al.  Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm , 2016 .

[18]  Jian Guo,et al.  Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis , 2016 .

[19]  Jaana M. Hartikainen,et al.  RAD51B in Familial Breast Cancer , 2016, PloS one.

[20]  Chun-Chieh Wang,et al.  Applications of fault diagnosis in rotating machinery by using time series analysis with neural network , 2010, Expert Syst. Appl..

[21]  Qihui Wu,et al.  Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space , 2015, IEEE Journal on Selected Areas in Communications.

[22]  Long Tang,et al.  Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines , 2015, Sensors.

[23]  Weizhong Guo,et al.  A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction , 2010 .

[24]  Keheng Zhu,et al.  A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm , 2014 .

[25]  Haolin Li,et al.  A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine , 2016 .

[26]  A. K. Wadhwani,et al.  Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis , 2011 .

[27]  Qingbo He,et al.  Time–frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction , 2016 .

[28]  X L Zhang,et al.  Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm , 2010 .

[29]  Pavan Kumar Kankar,et al.  Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier , 2015 .