Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis

Abstract An improved maximum correlated kurtosis deconvolution (MCKD) method based on quantum genetic algorithm (QGA) named QGA-MCKD is proposed, which can be used for gear and bearing compound fault diagnosis. Two key parameters, filter length (L) and deconvolution period (T) of MCKD, corresponding to each single fault are adaptively selected by QGA. MCKD is set by the obtained key parameters to process the compound fault signal, and each single fault feature related to the single failed part can be extracted. QGA-MCKD was applied to process the simulated and experimental compound fault signals of planetary gear tooth breakage and bearing rolling element damage, and the gear and bearing fault signals were extracted, respectively. Then power spectrum analysis of gear fault signal and envelop spectrum analysis of bearing fault signal were carried out to diagnose the compound faults. The superiority of QGA-MCKD was verified in comparison with direct spectrum analysis and ensemble empirical mode decomposition (EEMD). The stability of QGA-MCKD was verified in the compound fault diagnosis of gear tooth wear and bearing outer race fault. Results show that QGA-MCKD has a good effectiveness in improving the accuracy of gearbox gear and bearing compound fault diagnosis.

[1]  Sofie Van Hoecke,et al.  Thermal image based fault diagnosis for rotating machinery , 2015 .

[2]  Zhipeng Feng,et al.  Time-frequency space vector modulus analysis of motor current for planetary gearbox fault diagnosis under variable speed conditions , 2019, Mechanical Systems and Signal Processing.

[3]  Yong Li,et al.  Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum , 2018, Sensors.

[4]  Anand Parey,et al.  Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system , 2019, Applied Acoustics.

[5]  Qing Zhao,et al.  Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .

[6]  Fengshou Gu,et al.  Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis , 2013 .

[7]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[8]  Shuilong He,et al.  Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis , 2016 .

[9]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[10]  Bin Li,et al.  Genetic Algorithm Based-On the Quantum Probability Representation , 2002, IDEAL.

[11]  Hongkai Jiang,et al.  An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .

[12]  Siliang Lu,et al.  Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system , 2017, Mechanical Systems and Signal Processing.

[13]  Xiaobo Liu,et al.  Compound fault diagnosis of rotating machinery based on adaptive maximum correlated kurtosis deconvolution and customized multiwavelet transform , 2018, Measurement Science and Technology.

[14]  Q. K. Han,et al.  Detection and Localization of Tooth Breakage Fault on Wind Turbine Planetary Gear System considering Gear Manufacturing Errors , 2014 .

[15]  Adam Glowacz,et al.  Acoustic-Based Fault Diagnosis of Commutator Motor , 2018, Electronics.

[16]  Fulei Chu,et al.  Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods , 2017 .

[17]  Adam Glowacz,et al.  Fault diagnosis of single-phase induction motor based on acoustic signals , 2019, Mechanical Systems and Signal Processing.

[18]  Mangesh B. Chaudhari,et al.  Compound gear-bearing fault feature extraction using statistical features based on time-frequency method , 2018, Measurement.

[19]  Yaguo Lei,et al.  Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings , 2017 .

[20]  Adam Glowacz,et al.  Diagnosis of the three-phase induction motor using thermal imaging , 2017 .

[21]  Hu Xiao,et al.  Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance , 2015 .

[22]  Haiyang Pan,et al.  Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[23]  Min Wang,et al.  A method for the compound fault diagnosis of gearboxes based on morphological component analysis , 2016 .

[24]  Feng Zhipeng,et al.  Vibration Spectral Characteristics of Distributed Gear Fault of Planetary Gearboxes , 2013 .

[25]  Jiming Ma,et al.  A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation , 2018 .

[26]  Adam Glowacz,et al.  Vibration-Based Fault Diagnosis of Commutator Motor , 2018, Shock and Vibration.

[27]  Claude Delpha,et al.  Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing , 2018, Eng. Appl. Artif. Intell..

[28]  Xiaolong Wang,et al.  Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution , 2016 .

[29]  Li Li,et al.  Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis , 2013 .

[30]  İbrahim Uzmay,et al.  Experimental analysis on fault detection for a direct coupled rotor-bearing system , 2013 .

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

[32]  Dingcheng Zhang,et al.  Multi-fault diagnosis of gearbox based on resonance-based signal sparse decomposition and comb filter , 2017 .