Practical framework of Gini index in the application of machinery fault feature extraction

Abstract Gini index (GI) is an outstanding sparsity index that has high robustness for the interference of the random impulse noise. Yet, as a new index, the definition of GI in different domains is blurry which brings misdirection and restrictions for its application of machinery fault feature extraction. In view of this, this paper attempts to compensate for the loophole. With the mathematical deduction, a new GI evaluation frame, including the definition of new indexes based on GI, is firstly built. Based on this, enhancing signal processing methods via GI, such as spectrum kurtosis, decomposition methods, and multi-objective optimization algorithms, are designed. In addition, two blind deconvolution methods based on GI and its variants are originally proposed in this paper. Finally, the performance superiority of this application of GI is verified by the numerical simulation and real experimental cases compared with the most popular and the state-of-the-art methods in the field of machinery fault diagnosis.

[1]  M. G. A. Nassef,et al.  An adaptive variational mode decomposition based on sailfish optimization algorithm and Gini index for fault identification in rolling bearings , 2020 .

[2]  Dong Wang,et al.  Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults , 2015 .

[3]  Yaguo Lei,et al.  Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .

[4]  Marco Buzzoni,et al.  Blind deconvolution based on cyclostationarity maximization and its application to fault identification , 2018, Journal of Sound and Vibration.

[5]  Scott T. Rickard,et al.  Comparing Measures of Sparsity , 2008, IEEE Transactions on Information Theory.

[6]  Kaiyun Wang,et al.  A two-level adaptive chirp mode decomposition method for the railway wheel flat detection under variable-speed conditions , 2021 .

[7]  Nader Sawalhi,et al.  Rolling element bearing fault identification using a novel three-step adaptive and automated filtration scheme based on Gini index. , 2020, ISA transactions.

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

[9]  Qiang Wang,et al.  Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis , 2019, Measurement.

[10]  Jing Lin,et al.  Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals , 2016, Sensors.

[11]  Ming Zhang,et al.  Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump , 2017 .

[12]  Xiaodong Jia,et al.  A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery , 2017 .

[13]  Jijian Lian,et al.  Adaptive variational mode decomposition method for signal processing based on mode characteristic , 2018, Mechanical Systems and Signal Processing.

[14]  Ming Zhao,et al.  Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis. , 2021, ISA transactions.

[15]  Yonghao Miao,et al.  Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification , 2017 .

[16]  Minqiang Xu,et al.  A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection , 2017 .

[17]  Jing Lin,et al.  Health Assessment of Rotating Machinery Using a Rotary Encoder , 2018, IEEE Transactions on Industrial Electronics.

[18]  Dong Wang,et al.  Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients , 2018 .

[19]  Peter W. Tse,et al.  The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2” , 2013 .

[20]  Jay Lee,et al.  A geometrical investigation on the generalized lp/lq norm for blind deconvolution , 2017, Signal Process..

[21]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[22]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[23]  Chuan Li,et al.  A nonparametric health index and its statistical threshold for machine condition monitoring , 2021 .

[24]  Yonghao Miao,et al.  Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings , 2016 .

[25]  Bingchang Hou,et al.  A Comparison of Machine Health Indicators Based on the Impulsiveness of Vibration Signals , 2021, Acoustics Australia.

[26]  Pietro Borghesani,et al.  A statistical methodology for the design of condition indicators , 2019, Mechanical Systems and Signal Processing.

[27]  J. Antoni The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[28]  Jing Lin,et al.  A Data-Driven Monitoring Scheme for Rotating Machinery Via Self-Comparison Approach , 2019, IEEE Transactions on Industrial Informatics.

[29]  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.

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

[31]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

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

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

[34]  Qiang Miao,et al.  An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis , 2018 .

[35]  Shaopu Yang,et al.  A general multi-objective optimized wavelet filter and its applications in fault diagnosis of wheelset bearings , 2020 .

[36]  Ming Zhao,et al.  Research on sparsity indexes for fault diagnosis of rotating machinery , 2020 .

[37]  M. N. Albezzawy,et al.  Early Rolling Bearing Fault Detection Using A Gini Index Guided Adaptive Morlet Wavelet Filter , 2019, 2019 IEEE 10th International Conference on Mechanical and Aerospace Engineering (ICMAE).

[38]  Yonghao Miao,et al.  Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings , 2016 .

[39]  Qing Ni,et al.  A novel correntropy-based band selection method for the fault diagnosis of bearings under fault-irrelevant impulsive and cyclostationary interferences , 2021 .

[40]  Alessandro Fasana,et al.  The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis , 2018 .

[41]  Lifeng Xi,et al.  The sum of weighted normalized square envelope: A unified framework for kurtosis, negative entropy, Gini index and smoothness index for machine health monitoring , 2020, Mechanical Systems and Signal Processing.

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

[43]  Zhike Peng,et al.  Box-Cox sparse measures: A new family of sparse measures constructed from kurtosis and negative entropy , 2021 .