hasmat malik ppt-12110307
TRANSCRIPT
APresentation
on
Artificial Intelligence (AI) and its Applications in Gear Fault Detection
By
Hasmat Malik (Enrollment No. 12110307)
Mechanical Engineering Department, IIT Indore
Contents1. Introduction
2. Vibration Condition Monitoring
3. Artificial Intelligence (AI) Technique
4. Literature Review
5. Proposed Work Plan
6. Conclusion
7. Future Work
References01/12/2012 2
Gear is one of the most important mechanical elements in power
transmission.
It is used to transmit motion and/or torque mechanically b/w
parallel, intersecting or non-intersecting shafts
Power transmission using gears result in cyclic loading for the
individual gear tooth. Cyclic loading may leads to various gear
faults
1. Introduction
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Cracked tooth Brocken tooth Chipped tooth Missing tooth Spalling tooth and Worn tooth
Demand for Gear fault diagnosis &condition monitoring is increasing day byday.
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Example of gear tooth faults Cont..
Vibration signals is most commonly used method in condition
monitoring.
Diagnosing a gear system by examining the vibration signals is
the most commonly used method for detecting gear failures.
Vibration signal encountered in machines and such as machine
tool and gear box can be classified as:
Stationary
Non-stationary
2. Vibration Condition Monitoring
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The conventional methods for processing measured
vibration data are:
Time-domain technique
Frequency-domain technique
Time-frequency-domain technique
Cont..
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AI is the science and engineering of making intelligent machines,
specially intelligent computer programs.
3. Artificial Intelligence (AI) Technique
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Different AI Techniques are as follows:
Fuzzy logic (FL)
Artificial neural network (ANN)
Support vector machine (SVM)
Genetic algorithm (GA)
Genetic programming (GP)
Swarm optimization
Cont..
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AI Techniques Area Number of Paper
Neural Networks (NN) Comparison of neural classifiers for vehicles gear estimation
Gear fault classification
2
Fuzzy-Logic (FL) Gear fault diagnosis and condition assessment 4
Support vector Machine
(SVM)
Gear fault/Oil Analysis and condition monitoring
Processing of end effects of HHT
5
ANN with EMD Condition monitoring 1
Neuro-Fuzzy (ANFIS) Gear system monitoring and Fault identification and classification
A hybrid tool for detection of bearing faults
3
ANN with SVM Model for condition monitoring of transformer
Incipient gear box fault diagnosis
Fault diagnosis of spur bevel gear box
3
SVM with PSO Gear fault classification 1
Expert system-ES Fault Diagnosis for Gear Box 1
ANN with ES Automated on-line monitoring and fault diagnosis system 7
4. Literature Review
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Wavelet Transform-WT Efficient fault diagnosis system 20
Wavelet with ANN Faulted gear identification 2
Wavelet with SVM Fault diagnosis of spur bevel gear box
Fault analysis of bearings
3
GA Bearing fault diagnosis 1
GA with SVM A novel fault diagnosis model for gear box 1
NSGA Fault diagnosis of gearbox 1
ANN-SVM with GA Gear fault detection 1
ANN with GA Gear/Gearbox fault detection
Hybrid system for gear fault diagnosis
4
EMD Gear Fault Diagnosis
Gearbox condition monitoring
12
Improved EMD Fault signature analysis
IMF selection criterion
6
EDM with SVM A Novel Intelligent Gear Fault Diagnosis 2
Cont..
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Research Gap in AI Techniques
1. AI Techniques for Fault Classification are:
Probabilistic Neural Network (PNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
Gene Expression Programming (GEP)
Hybrid system
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Cont..
2. AI Techniques for Optimization are:
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Bee Colony Optimization (BCO)
Genetic Programming (GP)
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Cont..
A. Design Methodology for Fuzzy FaultDiagnosis System
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B. The Design Methodology of ANN FaultDiagnostic System
Neural Network System
Planning
NW Assigning
Assign NW Performance
Collect the Data
Create the Network
Configure the Network
Initialize the Weights and Biases
Train the Network
Validate the Network
Use the Network
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5. Proposed Work Plan
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Conclusions
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Literature survey of AI application in gear fault diagnosis
is presented.
Time-frequency feature extraction methods have been
explained which are used as a input feature in AI model.
AI based model is presented for gear fault detection.
Design methodology for Fuzzy-logic and ANN based gear
fault detection is presented.
Future Work
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Implementation of PNN, LVQ, SOM, SVM and hybrid model
for gear fault diagnosis
Implementation of parameter optimization system for ANN,
fuzzy logic and SVM.
To find the Co-relation between all purposed method for Gear
condition assessment and other new AI based techniques
1. Y. Lei, et al., A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process.
(2012), http://dx.doi.org/10.1016/j.ymssp.2012.09.015
2. Bi, Y., Guan, J., & Bell, D. (2008). The combination of multiple classifiers using an evidential reasoning approach. Artificial
Intelligence, 172, 1731–1751.
3. Halima, E. B., Shoukat Choudhury, M. A. A., Shah, S. L., & Zuo, M. J. (2008). Time domain averaging across all scales: A novel
method for detection of gearbox faults. Mechanical Systems and Signal Processing, 22, 261–278.
4. Hu, Q., Yu, D., & Xie, Z. (2008). Neighborhood classifiers. Expert Systems with Applications, 34, 866–876.
5. Lei, Y. G., He, Z. J., Zi, Y. Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with
GAs. Mechanical Systems and Signal Processing, 21, 2280–2294.
6. Lei, Y. G., He, Z. J., & Zi, Y. Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with
Applications, 35, 1593–1600.
7. Pal, S. K., Bandyopadhyay, S., & Murthy, C. A. (1998). Genetic algorithms for generation of class boundaries. IEEE Transactions
on Systems, Man, Cybernetics, 28, 816–828.
8. Peng, Z. K., & Chu, F. L. (2003). Application of wavelet transform in machine condition monitoring and fault diagnostics: A review
with bibliography. Mechanical Systems and Signal Processing, 17, 199–221.
9. Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. H. (2007). Intelligent condition monitoring of a gearbox using artificial neural
network. Mechanical Systems and Signal Processing, 21, 1746–1754.
10. Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms.
Mechanical Systems and Signal Processing, 18, 625–644.
References
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THANK YOU
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