A time–frequency-based maximum correlated kurtosis deconvolution approach for detecting bearing faults under variable speed conditions

Rolling bearing vibration signals induced by local faults are highly correlated with structural dynamics. This leads to a great deal of research about signal processing for rolling bearing condition monitoring and fault detection. However, the common diagnostic approaches which were proposed to manage vibration signals with constant speeds are unavailable for variable speed cases. Tacholess order tracking is a powerful method to break the limitation of conventional methods while avoiding trouble with tachometer installation and reducing measurement cost. Time frequency analysis methods are used to estimate the instantaneous rotation frequency (IRF) from vibration signals directly. However, it is difficult to extract IRF accurately due to the strong nonstationary property of the signal. Therefore, parameterized time-frequency transform (PTFT) methods are proposed to solve this problem. Polynomial chirplet transform (PCT) is one of the PTFTs that can produce an excellent time frequency representation (TFR) with a polynomial kernel function. In this paper, the PCT is employed to estimate the IRF of rolling bearings from the vibration signals. On this basis, a maximum correlated kurtosis deconvolution (MCKD) based envelope order spectrum is applied to detect the bearing fault characteristic order (FCO). The efficiency of the proposed method is certified by numerical signal and rolling bearing vibration data. The diagnostic results indicate that the new fault detection algorithm is superior for rolling bearing fault diagnosis under varying speed conditions.

[1]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[2]  Wei Hua,et al.  Mine gearbox fault diagnosis based on multiwavelets and maximum correlated kurtosis deconvolution , 2017 .

[3]  Wilson Wang,et al.  An enhanced Hilbert–Huang transform technique for bearing condition monitoring , 2013 .

[4]  Yi Qin,et al.  The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind Turbines , 2019, IEEE Transactions on Industrial Electronics.

[5]  Jing Liu,et al.  An improved analytical model for a lubricated roller bearing including a localized defect with different edge shapes , 2018 .

[6]  Qing Zhao,et al.  Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection , 2017 .

[7]  Hongkai Jiang,et al.  Rolling bearing fault feature extraction under variable conditions using hybrid order tracking and EEMD , 2016 .

[8]  Jiawei Xiang,et al.  Kernel regression residual signal-based improved intrinsic time-scale decomposition for mechanical fault detection , 2018, Measurement science and technology.

[9]  Yu Guo,et al.  Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings , 2017, Mechanical Systems and Signal Processing.

[10]  Jiangtao Wen,et al.  Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning , 2018, IEEE Transactions on Instrumentation and Measurement.

[11]  Qingbo He,et al.  Sparse Signal Reconstruction Based on Time–Frequency Manifold for Rolling Element Bearing Fault Signature Enhancement , 2016, IEEE Transactions on Instrumentation and Measurement.

[12]  Lingli Cui,et al.  Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis , 2017, IEEE Transactions on Industrial Electronics.

[13]  G. Meng,et al.  Spline-Kernelled Chirplet Transform for the Analysis of Signals With Time-Varying Frequency and Its Application , 2012, IEEE Transactions on Industrial Electronics.

[14]  Na Wu,et al.  Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary , 2016 .

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

[16]  Kun Zhang,et al.  Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis , 2019, Measurement Science and Technology.

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

[18]  G. Meng,et al.  General Parameterized Time-Frequency Transform , 2014, IEEE Transactions on Signal Processing.

[19]  Yang Yang,et al.  Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances , 2019, Mechanical Systems and Signal Processing.

[20]  W. M. Zhang,et al.  Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation , 2011, IEEE Transactions on Instrumentation and Measurement.

[21]  Xun Sun,et al.  Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network , 2017 .

[22]  Robert X. Gao,et al.  Prognosis of Defect Propagation Based on Recurrent Neural Networks , 2011, IEEE Transactions on Instrumentation and Measurement.

[23]  Peng Qian,et al.  Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines , 2019, IEEE Transactions on Industrial Electronics.

[24]  Jin Bae Park,et al.  Multiple Chirp Reflectometry for Determination of Fault Direction and Localization in Live Branched Network Cables , 2017, IEEE Transactions on Instrumentation and Measurement.

[25]  Guang Meng,et al.  Characterize highly oscillating frequency modulation using generalized Warblet transform , 2012 .

[26]  Shuhui Wang,et al.  Convolutional neural network-based hidden Markov models for rolling element bearing fault identification , 2017, Knowl. Based Syst..

[27]  G. Jacobs,et al.  Acoustic Emission Source Localization in Ring Gears from Wind Turbine Planetary Gearboxes , 2019, Forschung im Ingenieurwesen.

[28]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[29]  Lu Wang,et al.  A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed , 2019, IEEE Access.

[30]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[31]  Yimin Shao,et al.  Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges , 2017 .

[32]  Konstantinos Gryllias,et al.  A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions , 2019, Mechanical Systems and Signal Processing.

[33]  Shungen Xiao,et al.  Nonlinear dynamic response of reciprocating compressor system with rub-impact fault caused by subsidence , 2019, Journal of Vibration and Control.

[34]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[35]  Wei Qiao,et al.  Current-Aided Order Tracking of Vibration Signals for Bearing Fault Diagnosis of Direct-Drive Wind Turbines , 2016, IEEE Transactions on Industrial Electronics.

[36]  Qing Li,et al.  Physics-based intelligent prognosis for rolling bearing with fault feature extraction , 2018 .

[37]  Ming Liang,et al.  A method for tachometer-free and resampling-free bearing fault diagnostics under time-varying speed conditions , 2019, Measurement.

[38]  Tianyang Wang,et al.  Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis , 2014 .

[39]  Jing Wang,et al.  Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[40]  Zhengjia He,et al.  Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine , 2013 .

[41]  Yongxiang Zhang,et al.  Fault Diagnosis Method for Rolling Element Bearings Under Variable Speed Based on TKEO and Fast-SC , 2018, Journal of Failure Analysis and Prevention.

[42]  Shuilong He,et al.  A hybrid approach to fault diagnosis of roller bearings under variable speed conditions , 2017 .

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

[44]  Arun K. Samantaray,et al.  Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate , 2016 .