Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system

Insight: Non-Destructive Testing and Condition Monitoring

Volumn 52, Issue 10, 2010, Pages 561-566

  Ebrahimi, E ^a   Mollazade, K ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b UNIVERSITY OF TEHRAN   (Iran)

Author keywords

ANFIS; Fault detection; Statistical features; Tractor; Vibration signal

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; AGRICULTURAL TRACTORS; ANFIS; ANFIS MODEL; CLASSIFICATION ACCURACY; CLASSIFICATION PROCESS; FAULT CLASSIFICATION; FAULT DIAGNOSIS; FILTERING METHOD; INPUT VECTOR; INTELLIGENT FAULT DIAGNOSIS; INTELLIGENT METHOD; REAL APPLICATIONS; STATISTICAL FEATURES; STATISTICAL PARAMETERS; SUPERIOR FEATURE; TESTING DATA; TIME AND FREQUENCY DOMAINS; TRACTOR; VIBRATION MONITORING; VIBRATION SIGNAL;

DATA MINING; FEATURE EXTRACTION; FUZZY INFERENCE; FUZZY SYSTEMS; SIGNAL DETECTION; STARTERS; STATISTICAL TESTS; TRACTORS (AGRICULTURAL); TRACTORS (TRUCK); VIBRATION MEASUREMENT;

FAULT DETECTION;

EID: 78049407349     PISSN: 13542575     EISSN: None     Source Type: Journal    
DOI: 10.1784/insi.2010.52.10.561     Document Type: Article

References (22)

1. Q Yang, 'Model-based and data driven fault diagnosis methods with applications to process monitoring', PhD Thesis, Case Western Reserve University, 2004.
2. H Mohamadi Monavar, H Ahmadi, S S Mohtasebi and S Hasani, 'Vibration condition monitoring techniques for fault diagnosis of electromotor with 1.5 kW power', Journal of Applied Sciences, Vol 8, No 7, pp 1268-1273, 2008.
3. H Ahmadi and K Mollazade, 'Fault diagnosis of an electro-pump in a marine ship using vibration condition monitoring', Insight, Vol 51, No 8, pp 431-438, 2009.
4. H Ahmadi and K Mollazade, 'A practical approach to electromotor fault diagnosis of Imam Khomaynei silo by vibration condition monitoring', African Journal of Agricultural Research, Vol 4, No 4, pp 383-388, 2009.
5. K S Gaeid and H A F Mohamed, 'Diagnosis and fault tolerant control of the induction motors techniques - a review', Australian Journal of Basic and Applied Sciences, Vol 4, No 2, pp 227-246, 2010.
6. H D Bloch and F K Geither, 'Machinery failure analysis and troubleshooting', Gulf Publishing Company, Houston, Texas, 1990.
7. J Undacheck and V R Dodd, 'Progress and way out of a machinery surveillance and diagnostic system', In Proceedings of the ASME Conference on Pressure Vessel and Piping, Mexico City, Mexico, September 1976.
8. G K Sing and S A K S Ahmed, 'Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine', Electrical Power Systems Research, Vol 68, No 2, pp 119-136, 2004.
9. I Chindurza, D G Dorrell and C Cossar, 'Vibration analysis of a switched reluctance machine with eccentric rotor', In Proceedings of the Second International Conference on Power Electronics, Machines and Drives (PEMD), Vol 2, No 3, pp 481-486, March-April 2004.
10. K Mollazade, H Ahmadi, M Omid and R Alimardani, 'An intelligent model based on data mining and fuzzy logic for fault diagnosis of external gear hydraulic pumps', Insight, Vol 51, No 11, pp 594-600, 2009.
11. D Skarlatos, K Karakasis and A Trochidis, 'Railway wheel fault diagnosis using a fuzzy logic method', Applied Acoustics, Vol 65, No 10, pp 951-966, 2004.
12. L J D Miguel and L F Blázquez, 'Fuzzy logic-based decision making for fault diagnosis in a DC motor', Engineering Applications of Artificial Intelligence, Vol 18, No 4, pp 423- 450, 2004.
13. J D Wu and J M Kuo, 'An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network', Expert Systems with Applications, Vol 36, pp 9776- 9783, 2009.
14. J D Wu, C K Huang, Y W Chang and Y J Shiao, 'Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network', Expert Systems with Applications, Vol 37, No 2, pp 949-958, 2010.
15. N Saravanan and K I Ramachandran, 'Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)', Expert Systems with Applications, Vol 37, No 6, pp 4168-4181, 2010.
16. Y Zhang and R B Randall, 'Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram', Mechanical Systems and Signal Processing, Vol 23, No 5, pp 1509-1517, 2009.
17. M A Akcayol, 'Application of adaptive neuro-fuzzy controller for SRM', Advances in Engineering Software, Vol 35, No 3-4, pp 129-137, 2004.
18. C E Goering and A C Hansen, 'Engine and tractor power', American Society of Agricultural and Biological Engineering, 4th edition, USA, 2004.
19. M Omid, A Mahmoudi and M H Omid, 'An intelligent system for sorting pistachio nut varieties', Expert Systems with Applications, Vol 36, pp 11528-11535, 2009.
20. Y A Lei, Z He and Y Zi, 'A new approach to intelligent fault diagnosis of rotating machinery', Expert Systems with Applications, Vol 35, pp 1593-1600, 2008.
21. I H Witten and E Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Press, 2nd edition, 560 pages, 2005.
22. S Alavandar and M J Nigam, 'Adaptive neuro-fuzzy inference system based control of six DOF robot manipulator', Journal of Engineering Science and Technology Review, Vol 1, pp 106- 111, 2008.