Gear Fault Diagnosis Based on Angular Measurements and Support Vector Machines in Normal and Nonstationary Conditions

Contrary to time-sampled acceleration signals (TA), angular measurements like instantaneous angular speed (IAS), transmission error (TE), and angular sampled acceleration (AA) represent all potential sources of relevant information in fault detection and diagnosis systems, but also to construct feature vector (FV) to make the methods of classification robust and effective even for different running speed or load conditions. In this work, we propose to use angular measurements and support vector machines (SVM) to detect and diagnose gear faults in normal and nonstationary conditions. For this purpose, features are extracted from angular and angle frequency domains of AA, TE, and IAS. Then, the classification is performed by SVM in order to improve the detection and identification of gear defects.

[1]  Ahmed Felkaoui,et al.  Contribution of Angular Measurements in the Diagnosis of Gear Faults by Artificial Neural Networks , 2013 .

[2]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[3]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[4]  D. Rémond.  Practical performances of high-speed measurement of gear Transmission Error or torsional vibrations with optical encoders , 1998 .

[5]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

[6]  Jérôme Antoni,et al.  Comparison between angular sampling and angular resampling methods applied on the vibration monitoring of a gear meshing in non stationary conditions , 2010 .

[7]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[8]  Rajiv Tiwari,et al.  Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms , 2014 .

[9]  Robert B. Randall,et al.  Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation) , 2005 .

[10]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[11]  Rajiv Tiwari,et al.  Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data , 2014 .

[12]  Chih-Jen Lin,et al.  A Comparison of Methods for Multi-class Support Vector Machines , 2015 .

[13]  Qinghua Hu,et al.  Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine , 2012 .

[14]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[15]  L. Renaudin,et al.  Natural roller bearing fault detection by angular measurement of true instantaneous angular speed , 2010 .

[16]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[17]  Miguel Figueroa,et al.  Competitive learning with floating-gate circuits , 2002, IEEE Trans. Neural Networks.

[18]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[19]  Andrew Ball,et al.  The measurement of instantaneous angular speed , 2005 .