Research and application of improved adaptive MOMEDA fault diagnosis method

Abstract In a strong noise environment, vibration signals are easily submerged by noise. In recent years, many scholars have studied a large number of noise reduction methods. In 2017, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault diagnosis of gearbox. Although MOMEDA overcomes Maximum correlation kurtosis deconvolution (MCKD) defects and it can extract continuous impulse signal, but it still has the following problems: 1) It can only extract single periodic pulse. If we want to extract the characteristics of multiple periodic pulse signals, we need to further update the algorithm; 2) In a strong noise environment, MOMEDA can also search for a fixed periodic signal, but most of the information is false component, it is easy to cause misdiagnosis, therefore, the signal needs to be preprocessed; 3) The accuracy of MOMEDA noise reduction is affected by the search interval and filter size, and McDondald did not reasonably explain them, so an adaptive selection method is needed. Considering these problems. Firstly the article preprocesses the composite fault with ensemble empirical mode decomposition (EEMD) and then reconstructs the intrinsic mode function with the same time scale. Further, proposing kurtosis spectral entropy as the objective function, the grid search method is used to search the filter length of MOMEDA, and the reconstructed intrinsic mode function is further denominated by MOMEDA. Finally, the proposed method is used to search the complex fault pulse signals in strong noise environment. It proves its reliability.

[1]  Bing Li,et al.  Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform , 2015 .

[2]  Jin Chen,et al.  Application of Maximum Correlated Kurtosis Deconvolution on Rolling Element Bearing Fault Diagnosis , 2015 .

[3]  Guoyu Meng,et al.  Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM , 2006 .

[4]  Ming Zhao,et al.  Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis , 2016 .

[5]  Fengshou Gu,et al.  A novel procedure for diagnosing multiple faults in rotating machinery. , 2015, ISA transactions.

[6]  María Eugenia Torres,et al.  Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..

[7]  Qing Zhao,et al.  Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection , 2017 .

[8]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

[9]  Ling Xiang,et al.  A self-adaptive time-frequency analysis method based on local mean decomposition and its application in defect diagnosis , 2016 .

[10]  Yaguo Lei,et al.  Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings , 2008 .

[11]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[12]  Ming Liang,et al.  Identification of multiple transient faults based on the adaptive spectral kurtosis method , 2012 .

[13]  Jing Yuan,et al.  Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications for rotating machinery fault detection , 2012 .

[14]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

[15]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[16]  Paul K. Romano,et al.  Optimizations of the energy grid search algorithm in continuous-energy Monte Carlo particle transport codes , 2015, Comput. Phys. Commun..

[17]  Robert B. Randall,et al.  Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .

[18]  Feng Wu,et al.  Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform , 2015 .

[19]  Liu Hong,et al.  A time domain approach to diagnose gearbox fault based on measured vibration signals , 2014 .

[20]  Zhijian Wang,et al.  Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD , 2017, Entropy.