Fuzzy logic based classification of faults in mechanical differential

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential.

[1]  Krzysztof Jemielniak,et al.  Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling , 2014, Inf. Sci..

[2]  Zhou Yunlong,et al.  Vibration Fault Diagnosis Method of Centrifugal Pump Based on EMD Complexity Feature and Least Square Support Vector Machine , 2012 .

[3]  Saeid Farokhzad,et al.  Fault Diagnosis of Crown Wheel and Pinion in Differential Using Acoustic Signals with Discrete Wavelet Transform and Neural Network , 2013 .

[4]  K. I. Ramachandran,et al.  Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing , 2007 .

[5]  Ma Xiao-xiao,et al.  An Intelligent Fault Diagnosis Method Based on Empirical Mode Decomposition and Support Vector Machine , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[6]  Zhaowei Sun,et al.  Structural Damage Identification Method Based on IMF Model Energy Feature and BP Neural Network , 2011, 2011 International Conference on Intelligence Science and Information Engineering.

[7]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[8]  Türkay Dereli,et al.  Industrial applications of type-2 fuzzy sets and systems: A concise review , 2011, Comput. Ind..

[9]  K. I. Ramachandran,et al.  Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique , 2009, Expert Syst. Appl..

[10]  Bo-Suk Yang,et al.  Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[11]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[12]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[13]  Taner Ustuntas,et al.  Wind turbine power curve estimation based on cluster center fuzzy logic modeling , 2008 .

[14]  Oscar Castillo,et al.  An improved method for edge detection based on interval type-2 fuzzy logic , 2010, Expert Syst. Appl..

[15]  Peng Zhang,et al.  Research of Fault Diagnosis in Flight Control System Based on Fuzzy Neural Network , 2011 .

[16]  Mahmoud Omid,et al.  Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier , 2011, Expert Syst. Appl..

[17]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[18]  Mahmoud Omid,et al.  An intelligent model based on data mining and fuzzy logic for fault diagnosis of external gear hydraulic pumps , 2009 .

[19]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[20]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[21]  Radoslaw Zimroz,et al.  Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions , 2013 .

[22]  Hui Li,et al.  Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[23]  Ming Yang,et al.  ARX model-based gearbox fault detection and localization under varying load conditions , 2010 .

[24]  Tomasz Barszcz,et al.  Wind Turbines States Classification by a Fuzzy-ART Neural Network with a Stereographic Projection as a Signal Normalization , 2011, ICANNGA.

[25]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[26]  Ashish Ghosh,et al.  Fuzzy clustering algorithms for unsupervised change detection in remote sensing images , 2011, Inf. Sci..

[27]  Neeraj Bhargava,et al.  Decision Tree Analysis on J48 Algorithm for Data Mining , 2013 .

[28]  Hui Li.  Bearing fault detection based on instantaneous energy spectrum , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.