A novel method for real time gear fault detection based on pulse shape analysis

Early identification of faults in gearboxes is a challenging task, especially when the time is a critical factor. In this paper, a novel method for real time fault detection in gearboxes is proposed using adaptive features extraction algorithm to deal with non-stationary faulty signals. Moreover, integration of different techniques is presented in order to detect faults in a real time environment. Evolutionary algorithms are commonly used in different applications and have strong ability for optimization. However, they are inherently slow and not suitable for real time applications. The proposed method is based on a combination of conventional one-dimensional and multi-dimensional search methods, which showed high performance and accurate fault detection results compared with evolutionary algorithms. The effectiveness, feasibility and robustness of the proposed method have been demonstrated on experimental data. An average speed up factor of 87% has been successfully achieved with approximately 5% quality degradation in the results as compared with evolutionary algorithms like genetic algorithms.

[1]  Markus Timusk,et al.  Fault detection in variable speed machinery: Statistical parameterization , 2009 .

[2]  Robert B. Randall,et al.  Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .

[3]  Håkan Grahn,et al.  Fast kd -Tree Construction for 3D-Rendering Algorithms Like Ray Tracing , 2007, ISVC.

[4]  Yaguo Lei,et al.  A multidimensional hybrid intelligent method for gear fault diagnosis , 2010, Expert Syst. Appl..

[5]  V. Sugumaran,et al.  Minimum sample size determination of vibration signals in machine learning approach to fault diagnosis using power analysis , 2010, Expert Syst. Appl..

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

[7]  Yaguo Lei,et al.  New clustering algorithm-based fault diagnosis using compensation distance evaluation technique , 2008 .

[8]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[9]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[10]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[11]  George J. Vachtsevanos,et al.  Genetically programmed-based artificial features extraction applied to fault detection , 2008, Eng. Appl. Artif. Intell..

[12]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[13]  Weizhong Yan,et al.  Feature Extraction for Bearing Prognostics and Health Management (PHM) - A Survey (Preprint) , 2008 .

[14]  Hossam A. Gabbar,et al.  Improved qualitative fault propagation analysis , 2007 .

[15]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..

[16]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

[17]  Hossam A. Gabbar,et al.  Design of fault simulator , 2009, Reliab. Eng. Syst. Saf..

[18]  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 .

[19]  Zhengjia He,et al.  Crack detection in a shaft by combination of wavelet-based elements and genetic algorithm , 2008 .

[20]  Ronald L. Rardin,et al.  Optimization in operations research , 1997 .

[21]  Fugee Tsung,et al.  Adaptive time-frequency decomposition for transient vibration monitoring of rotating machinery , 2004 .

[22]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[23]  Yuh-Tay Sheen,et al.  On the study of applying Morlet wavelet to the Hilbert transform for the envelope detection of bearing vibrations , 2009 .

[24]  Zhongkui Zhu,et al.  Detection of signal transients based on wavelet and statistics for machine fault diagnosis , 2009 .

[25]  Nadine Martin,et al.  Spectrogram segmentation by means of statistical features for non-stationary signal interpretation , 2002, IEEE Trans. Signal Process..

[26]  F. Combet,et al.  Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .

[27]  Y. Zi,et al.  Gear fault detection using customized multiwavelet lifting schemes , 2010 .

[28]  Sirish L. Shah,et al.  Time domain averaging across all scales: A novel method for detection of gearbox faults , 2008 .

[29]  Steven C. Chapra,et al.  Applied Numerical Methods with MATLAB for Engineers and Scientists , 2004 .