Application of pattern recognition in gear faults based on the matching pursuit of a characteristic waveform

Abstract Aiming at the issue of recognizing the damage pattern of gear signals, a new matching method based on the morphological characteristics of the vibration waveforms was proposed in this paper. The signals of different types of gear faults share the same modulated frequency; therefore, only whether a gear is damaged or not can be identified from the frequency domain. However, it is difficult to recognize the different fault types of the gears. There are significant differences among the signal waveforms in the time domain of different fault types; thus, research on the recognition of the types of gear-damaged faults is conducted using the repeated characteristic waveforms of the same fault type based on the morphological characteristics of the gear fault signal. The signal morphological characteristics of different gear fault types were extracted using a searching algorithm, and then the matching effect was achieved from the basic atom library, which was constructed by the multi-stage characteristic waveform after an optimized process by the method optimal direction (MOD). Finally, the simulation signals of four types of different gear fault types and real engineering signals were processed using this method, and the effectiveness of the method was proved.

[1]  Wieslaw J. Staszewski,et al.  Advanced data pre-processing for damage identification based on pattern recognition , 2000, Int. J. Syst. Sci..

[2]  Xu Jin-wu.  Fault pattern recognition of rolling bearings based on characteristic waveform sparse matching , 2010 .

[3]  Huaqing Wang,et al.  Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis , 2011, Sensors.

[4]  Jing Wang,et al.  Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[5]  Mathieu Feuilloy,et al.  Acoustic emission pattern recognition approach based on Hilbert–Huang transform for structural health monitoring in polymer-composite materials , 2013 .

[6]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[7]  Yan Jie Zhou,et al.  Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence , 2013 .

[8]  Jian-Da Wu,et al.  Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network , 2009, Expert Syst. Appl..

[9]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[10]  Cesare Alippi,et al.  A Cognitive Fault Diagnosis System for Distributed Sensor Networks , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Kjersti Engan,et al.  Multi-frame compression: theory and design , 2000, Signal Process..

[12]  Zrar Kh. Abdul,et al.  A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection , 2016 .

[13]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[14]  Wei He,et al.  A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine , 2012 .

[15]  Bing He,et al.  Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification , 2016 .

[16]  Jian Xiao,et al.  Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings , 2014, Appl. Math. Comput..

[17]  Yu Song,et al.  The Fault Diagnosis Research Based on SOM-BP Composite Neural Network Learning Algorithm , 2012 .

[18]  Jian-Da Wu,et al.  Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[19]  Na Wu,et al.  Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary , 2016 .

[20]  Min Zhang,et al.  Intelligent Recognition of Mixture Control Chart Pattern Based on Quadratic Feature Extraction and SVM with AMPSO , 2015 .

[21]  Stavros Ntalampiras.  Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling , 2015, IEEE Transactions on Neural Networks and Learning Systems.