Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

Expert Systems with Applications

Volumn 37, Issue 6, 2010, Pages 4040-4049

  Sakthivel, N R ^a   Sugumaran, V ^b   Babudevasenapati, S ^a  

^a AMRITA SCHOOL OF ENGINEERING   (India)

^b SRM UNIVERSITY   (India)

Author keywords

C4.5 algorithm; Fault diagnosis; Monoblock centrifugal pump; Statistical features

Indexed keywords

ARTIFICIAL NEURAL NETWORK; C4.5 ALGORITHM; C4.5 DECISION TREE ALGORITHM; CONTINUOUS MONITORING; FAULT DIAGNOSIS; FAULTY CONDITION; GAINING MOMENTUM; MACHINE-LEARNING; STATISTICAL FEATURES; VIBRATION SIGNAL;

CENTRIFUGAL PUMPS; CONDITION MONITORING; DECISION TREES; FAILURE ANALYSIS; FUZZY LOGIC; FUZZY NEURAL NETWORKS; HYDRAULIC MACHINERY; PUMPING PLANTS; VIBRATION ANALYSIS;

PUMPS;

EID: 77249155059     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.10.002     Document Type: Article

References (24)

1. Alfayez L., Mba D., and Dyson G. The application of acoustic emission for detecting incipient cavitation and the best efficiency point of a 60 kW mono block centrifugal pump. NDT&E International 38 (2005) 354-358
2. Cempel C. Vibroacoustical diagnostics of machinery - An outline. Mechanical Systems and Signal Processing 2 (1988) 135-151
3. Chen C., and Mo C. A method for intelligent fault diagnosis of rotating machinery. Digital Signal Processing 14 (2004) 203-217
4. Kong F., and Chen R. A combined method for triplex pump fault diagnosis based on wavelet transform, fuzzy logic and neural-networks. Mechanical Systems and Signal Processing 18 (2004) 161-168
5. Koo I.S., and Kim W.W. The development of reactor coolant pump vibration monitoring and a diagnostic system in the nuclear power plant. ISA Transactions 39 (2000) 309-316
6. McFadden P.D., and Smith J.D. Vibration monitoring of rolling element bearings by high frequency resonance technique-a review. Tribology International 17 (1984) 3-10
7. Peck J.P., and Burrows J. On-line condition monitoring of rotating equipment using neural networks. ISA Transactions 33 (1994) 159-164
8. Polat K., and Günes S. A novel hybrid intelligent method based on C4. 5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36 (2009) 1587-1592
9. Quinlan J.R. C4.5: Programs for machine learning (1993), Morgan Kaufmann, San Mateo, CA
10. Quinlan J.R. Improved use of continuous attributes in C4.5. Journal of Artificial Research 4 (1996) 77-90
11. Rajakarunakaran S., Venkumar P., Devaraj D., and Surya Prakasa Rao K. Artificial neural network approach for fault detection in rotary system. Applied Soft Computing 8 (2008) 740-778
12. Sanz J., Perera R., and Huerta C. Fault diagnosis of rotating machinery based on auto associative neural networks and wavelet transforms. Journal of Sound and Vibration 302 (2007) 981-999
13. Sugumaran V., Muralidharan V., and Ramachandran K.I. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing 21 (2007) 930-942
14. Sugumaran V., and Ramachandran K.I. Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mechanical Systems and Signal Processing 21 (2007) 2237-2247
15. Sun W., Chen J., and Li J. Decision tree and PCA-based fault of rotating machinery. Mechanical Systems and Signal Processing 21 (2007) 1300-1317
16. Tran V.T., Yang B.-S., Oh M.-S., and Tan A.C.C. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications 36 (2009) 1840-1849
17. Wang H., and Chen P. Sequential condition diagnosis for centrifugal pump system using fuzzy neural network. Neural Information Processing - Letters and Reviews 11 3 (2007) 41-50
18. Wang J., and Hu H. Vibration-based fault diagnosis of pump using fuzzy technique. Measurement 39 (2006) 176-185
19. Witten I.H., and Frank E. Data mining: Practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufmann, San Francisco
20. Xiang X., Zhou J., An X., Peng B., and Yang J. Fault diagnosis based on Walsh transform and support vector machine. Mechanical Systems and Signal Processing 22 (2008) 1685-1693
21. Yuan S.-F., and Chu F.-L. Support vector machines-based fault diagnosis for turbo-pump rotor. Mechanical Systems and Signal Processing 20 (2006) 939-952
22. Yuan S.-F., and Chu F.-L. Fault diagnostics based on particle swarm optimization and support vector machines. Mechanical Systems and Signal Processing 21 (2007) 1787-1798
23. Zhang S., Asakura T., Xu X., and Xu B. Fault diagnosis system for rotary machine based on fuzzy neural networks. JSME International Journal 46 (2003) 1035-1041
24. Zhang L., Jack L.B., and Nandi A.K. Fault detection using genetic programming. Mechanical Systems and Signal Processing 19 (2005) 271-289