Condition monitoring of helical gears using automated selection of features and sensors

Abstract The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or redundant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi’s orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications.

[1]  Hui Li,et al.  Bearing Fault Detection Using Envelope Spectrum Based on EMD and TKEO , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[2]  Hao Xu,et al.  Automatic thesaurus construction for spam filtering using revised back propagation neural network , 2010, Expert Syst. Appl..

[3]  Eyuphan Manay,et al.  A Taguchi approach for optimization of design parameters in a tube with coiled wire inserts , 2011 .

[4]  Xianyi Zeng,et al.  Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network , 2010, Expert Syst. Appl..

[5]  V. Balasubramanian,et al.  Process parameters optimization for friction stir welding of RDE-40 aluminium alloy using Taguchi technique , 2008 .

[6]  Theodoros Loutas,et al.  Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements , 2009 .

[7]  Yi Qin,et al.  Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD , 2011 .

[8]  Fanrang Kong,et al.  Subspace-based gearbox condition monitoring by kernel principal component analysis , 2007 .

[9]  Ying Liu,et al.  Extended Kalman filter-based Elman networks for industrial time series prediction with GPU acceleration , 2013, Neurocomputing.

[10]  Marcantonio Catelani,et al.  Fault diagnosis of electronic analog circuits using a radial basis function network classifier , 2000 .

[11]  Hui Li,et al.  Research on the Gear Fault Diagnosis Using Order Envelope Spectrum , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[12]  Jiao Weidong.  Fault diagnosis of gearbox by FastICA and residual mutual information based feature extraction , 2009, 2009 International Conference on Information and Automation.

[13]  K. I. Ramachandran,et al.  Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN) , 2010, Expert Syst. Appl..

[14]  Lei Guo,et al.  Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine , 2009 .

[15]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..

[16]  W. H. Sneed,et al.  On-board real-time railroad bearing defect detection and monitoring , 1998, Proceedings of the 1998 ASME/IEEE Joint Railroad Conference.

[17]  Amin Al-Habaibeh,et al.  Comprehensive experimental evaluation of a systematic approach for cost effective and rapid design of condition monitoring systems using Taguchi’s method , 2002 .

[18]  Xiaomin Zhao,et al.  Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking , 2013 .

[19]  Omar D. Mohammed,et al.  Vibration signal analysis for gear fault diagnosis with various crack progression scenarios , 2013 .

[20]  Viliam Makis,et al.  Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test , 2009 .

[21]  Amin Al-Habaibeh,et al.  A new approach for systematic design of condition monitoring systems for milling processes , 2000 .

[22]  Li Lin,et al.  Signal feature extraction based on an improved EMD method , 2009 .

[23]  M. Zuo,et al.  Feature separation using ICA for a one-dimensional time series and its application in fault detection , 2005 .

[24]  Theodoros Loutas,et al.  The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery , 2011 .

[25]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[26]  Lei Nie,et al.  Feature Extraction for Gear Failure Detection Based on Nonlinear Mapping , 2006 .