An offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter

Planetary gearbox is increasingly used in many kinds of rotary machinery in recent years. Due to the specialty of its structure, fault diagnosis for planetary gearbox is very difficult compared with the fixed shaft gearbox. This paper proposed an offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter. Firstly, the framework of the method proposed in this paper was introduced. Then, experimental data and industrial data were utilized to validate the effectiveness of the method. And the combination of empirical mode decomposition and adaptive multi-scale morphological dilation-erosion gradient filter was found very suitable to be used in the planetary gearbox fault diagnosis compared with other five filters. The proposed method was demonstrated to be of good performance on both extracting faults characteristic frequency and de-noising.

[1]  Sophia Hassiotis,et al.  Identification of damage using natural frequencies and Markov parameters , 2000 .

[2]  Dai Qingyun,et al.  The Advances of Mathematical Morphology in Image Processing , 2001 .

[3]  Ming J. Zuo,et al.  Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis , 2013 .

[4]  S. J. Loutridis,et al.  Damage detection in gear systems using empirical mode decomposition , 2004 .

[5]  Ming J. Zuo,et al.  Fault diagnosis of planetary gearboxes via torsional vibration signal analysis , 2013 .

[6]  Ming Liang,et al.  Detection and diagnosis of bearing and cutting tool faults using hidden Markov models , 2011 .

[7]  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.

[8]  Jonathan A. Keller,et al.  Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis , 2006 .

[9]  Walmir M. Caminhas,et al.  SVM practical industrial application for mechanical faults diagnostic , 2011, Expert Syst. Appl..

[10]  Bing Li,et al.  Gear fault detection using multi-scale morphological filters , 2011 .

[11]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[12]  Bing Li,et al.  An adaptive morphological gradient lifting wavelet for detecting bearing defects , 2012 .

[13]  Hui Li,et al.  Wear detection in gear system using Hilbert-Huang transform , 2006 .

[14]  Katherine T. McClintic,et al.  OF VIBRATION ANALYSIS METHODS FOR GEARBOX DIAGNOSTICS AND PROGNOSTICS , 2001 .

[15]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[16]  G. Yen,et al.  Fault classification on vibration data with wavelet based feature selection scheme , 2006, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[17]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[18]  Kong Detong.  Research Advances of Fault Diagnosis Technique for Planetary Gearboxes , 2011 .

[19]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planetary gearboxes , 2012 .

[20]  Jun Cai,et al.  Multi-fault classification based on support vector machine trained by chaos particle swarm optimization , 2010, Knowl. Based Syst..

[21]  José-Luis Sánchez-Romero,et al.  Mathematical morphology for design and manufacturing , 2011, Math. Comput. Model..

[22]  Paolo Pennacchi,et al.  Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions , 2011 .