Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques

Mechanical Systems and Signal Processing

Volumn 23, Issue 3, 2009, Pages 690-700

  Liu, Xiaofeng ^a   Ma, Lin ^a   Mathew, Joseph ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Data fusion; Fault diagnosis; Fuzzy c means; Fuzzy integrals; Fuzzy measures

Indexed keywords

BEARINGS (STRUCTURAL); DATA FUSION; FUZZY CLUSTERING; FUZZY SYSTEMS; INFORMATION FUSION; MACHINERY; MOTORS; ROLLING;

ANALYSIS METHODS; DATA FUSION TECHNIQUES; DECISION LEVELS; ELECTRICAL MOTORS; FAULT DIAGNOSIS; FEATURE SETS; FUSION MODELS; FUZZY C-MEANS; FUZZY INTEGRALS; FUZZY MEASURES; MACHINERY FAULTS; MEASURE THEORIES; MOTOR FAULTS; RECORDED SIGNALS; ROLLING ELEMENT BEARINGS;

MATHEMATICAL MODELS;

EID: 58049136406     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2008.07.012     Document Type: Article

References (25)

1. Javadpour R., and Knapp G.M. A fuzzy neural network approach to machine condition monitoring. Computers & Industrial Engineering 45 (2003) 323-330
2. T. Kobayashi, D.L. Simon, A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics, AIAA-2001-3763, 2001.
3. He Y., Guo D., and Chu F. Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system. Mathematics and Computers in Simulation 57 (2001) 95-108
4. Milne R., Nicol C., and Trave-Massuyes L. TIGER with model based diagnosis: initial deployment. Knowledge-Based System 14 (2001) 213-222
5. H.G. Park, H. Cannon, A. Bajwa, R. Mackey, M. James, W. Maul, Hybrid diagnostic system: beacon-based exception analysis for multimission-livingstone integration, in: Proceedings of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, USA, 2004.
6. Yu H., Chen C., Zhang S., and Zhou J. Neural Network Based Intelligent Diagnosis (2000), Metallurgy Industry Press, Beijing
7. Mikenina L., and Zimmermann H.-J. Improved feature selection and classification by the 2-additive fuzzy measure. Fuzzy Sets and Systems 107 (1999) 197-218
8. Osareh A., Mirmehdi M., Thomas B., and Markham R. Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. Medical Image Understanding and Analysis (2001) 49-52
9. M. Bao, Classification of multi-temporal SAR images and INSAR coherence images using adaptive neighborhood model and simulated annealing approach, in: Proceedings of the 20th Asia Conference on Remote Sensing, Hongkong, PR China, 1999.
10. Gader P.D., Mohamed M.A., and Keller J.M. Fusion of handwritten word classifiers. Pattern Recognition Letters 17 (1996) 577-584
11. Grabisch M. The representation of importance and interaction of features by fuzzy measures. Pattern Recognition Letters 17 (1996) 567-575
12. Kuncheva L.I., Bezdek J.C., and Duin R.P.W. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34 (2001) 299-314
13. Mirhosseini A.R., and Yan H. Human face image recognition: an evidence aggregation approach. Computer Vision and Image Understanding 71 (1998) 213-230
14. Pham T.D., and Yan H. Color image segmentation using fuzzy integral and mountain clustering. Fuzzy Sets and Systems 107 (1999) 121-130
15. Kwak K.-C., and Pedrycz W. Face recognition using fuzzy integral and wavelet decomposition method. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 34 (2004) 1666-1675
16. Li J., Chen G., Chi Z., and Lu C. Image coding quality assessment using fuzzy integral with a three-component image model. IEEE Transaction on Fuzzy Systems 12 1 (2004) 99-106
17. Auephanwiriyakul S., Keller J.M., and Gader P.D. Generalized Choquet fuzzy integral fusion. Information Fusion 3 (2002) 69-85
18. Sun J. Pattern Recognition (2002), Defence Technology University, Changsha
19. Niu G., Han T., Yang B.-S., and Tan A.C.C. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 21 (2007) 1285-1299
20. Yang B.-S., and Kim K.J. Application of Dempster-Shafer theory in fault diagnosis of induction motor using vibration and current signals. Mechanical Systems and Signal Processing 20 (2006) 403-420
21. Klir G.J., Wang Z., and Harmanec D. Constructing fuzzy measures in expert systems. Fuzzy Sets and Systems 92 (1997) 251-264
22. Wang Z., and Klir G.J. Fuzzy Measure Theory (1992), Plenum Publishing Corporation, New York, USA
23. Keller J.M., and Osborn J. Training the fuzzy integral. International Journal of Approximate Reasoning 15 (1996) 1-24
24. Berry J.E. Proven Method for Specifying Both Special Alarm Bands as Well as Narrowband Alarm Envelopes Using Today's Condition Monitoring Software Systems. 4th ed (2002), Technical Associates of Charlotte, P.C., Charlotte, NC, USA
25. X. Liu, L. Ma, J. Mathew, Machinery fault diagnosis based on feature level fuzzy integral data fusion techniques, in: 2006 IEEE International Conference on Industrial Informatics, Grand Copthorne Waterfront Hotel, Singapore, 2006.