Application of psychoacoustics for gear fault diagnosis using artificial neural network

Identification of correct working of gearbox is a very important function during end of line inspection in the assembly line while manufacturing the gearbox. Such inspection is performed by an operator by listening to the sound of gearbox while running it on a test bench. Based on the sound emitted by the gearbox combined with experience and judgment of the operator, the gearbox is passed or rejected for fitting inside the vehicle. This paper makes an attempt to use artificial intelligence techniques to identify gearbox condition in the above environment by using psychoacoustic features to replace human hearing. Experiments are carried out on a gearbox test rig and sound data are acquired for good and faulty gear conditions. Psychoacoustic features and statistical indices are extracted from the data and these are then used as input to an artificial neural network. The artificial neural network output is the condition of gearbox. Performances of psychoacoustic and statistical indices are then compared. It is found that psychoacoustic features are able to predict gearbox condition with an accuracy of 99% and 98% for good and faulty conditions, respectively, whereas the statistical features are able to do the same with 97% and 98% accuracy. Therefore, it is concluded that psychoacoustic features have the potential to be used for the end of line inspection of gearbox in manufacturing environment and the process of inspection can be made objective by eliminating operator’s ability and judgment.

[1]  Giorgio Bonori,et al.  Non-smooth dynamics of spur gears with manufacturing errors , 2007 .

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

[3]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[4]  Victor V. Krylov,et al.  Simplified Modelling of Vehicle Interior Noise: Comparison of Analytical, Numerical and Experimental Approaches , 2006 .

[5]  Arne Nykänen,et al.  Specification of component sound quality applied to automobile power windows , 2009 .

[6]  Etienne Parizet,et al.  Analysis of car door closing sound quality , 2008 .

[7]  Y. S. Wang,et al.  Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model , 2007 .

[8]  Radislav Smid,et al.  Condition Indicators for Gearbox Condition Monitoring Systems , 2005 .

[9]  Teik C. Lim,et al.  Correlations between deficiencies in power window systems influencing sound quality and some psychoacoustic metrics , 2001 .

[10]  Young-Joon Lee,et al.  Sound metric design for evaluation of tonal sound in laser printer , 2012 .

[11]  Markus Bodden,et al.  Instrumentation for Sound Quality Evaluation , 1997 .

[12]  T. Bernard,et al.  The Development of a Sound Quality-Based End-of-Line Inspection System for Powered Seat Adjusters , 2001 .

[13]  Sahin Yildirim,et al.  Sound quality analysis of cars using hybrid neural networks , 2008, Simul. Model. Pract. Theory.

[14]  Theodoros Loutas,et al.  Condition Monitoring of Gears and Advanced Signal Processing Techniques towards More Effective Diagnostic Schemes , 2010 .

[15]  Girish Kumar Singh,et al.  Induction machine drive condition monitoring and diagnostic research—a survey , 2003 .

[16]  Wenli Shang,et al.  An intelligent fault diagnosis system for newly assembled transmission , 2014, Expert Syst. Appl..

[17]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[18]  Junda Zhu,et al.  Survey of Condition Indicators for Condition Monitoring Systems (Open Access) , 2014 .

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  Cheng Wei Li,et al.  Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy , 2015 .

[21]  Pan Cunzhi,et al.  Nonlinear Dynamics of a Spur Gear Pair with Time-Varying Stiffness and Backlash , 2004 .

[22]  M. Farid Golnaraghi,et al.  Assessment of Gear Damage Monitoring Techniques Using Vibration Measurements , 2001 .

[23]  Ahad Ali,et al.  End-of-line inspection for annoying noises in automobiles: Trends and perspectives , 2012 .

[24]  Hugo Fastl.  The Psychoacoustics of Sound-Quality Evaluation , 1996 .

[25]  Ali M. Abd-El-Tawwab,et al.  Characteristics of Agriculture Tractor Interior Noise , 2000 .

[26]  Anand Parey,et al.  Fault Diagnosis of Gearbox Using Various Condition Monitoring Indicators for Non-Stationary Speed Conditions : A Comparative Analysis , 2015 .

[27]  Linjing Zhao,et al.  An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine , 2014 .

[28]  Hugo Fastl.  Psychoacoustic basis of sound quality evaluation and sound engineering , 2006 .

[29]  Wei Haiqiao.  Statistical Evaluation and Regression Analysis of Vehicle Sound Quality , 2006 .

[30]  F. Guillet,et al.  New applications of the real cepstrum to gear signals, including definition of a robust fault indicator , 2004 .

[31]  Joël Perret-Liaudet,et al.  Variability of modal behavior in terms of critical speeds of a gear pair due to manufacturing errors and shaft misalignments , 2006 .

[32]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM) , 2010, Appl. Soft Comput..

[33]  Sun Wenlei,et al.  Vibration and Noise Radiation Characteristics of Gear Transmission System , 2014 .

[34]  Satish C. Sharma,et al.  Rolling element bearing fault diagnosis using wavelet transform , 2011, Neurocomputing.

[35]  Mengyan Nie,et al.  Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox , 2013 .