Feature evaluation for effective bearing prognostics

Quality and Reliability Engineering International

Volumn 29, Issue 4, 2013, Pages 477-486

  Camci, F ^a   Medjaher, K ^b   Zerhouni, N ^b   Nectoux, P ^b  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b FEMTO ST INSTITUTE   (France)

Author keywords

Bearing; Diagnostics; Feature evaluation; Prognostics

Indexed keywords

FEATURE EVALUATION; HEALTH STATE; LABORATORY ENVIRONMENT; PROGNOSTICS; REDUCED COST; REMAINING USEFUL LIVES; ROLLING ELEMENT BEARING;

BEARINGS (STRUCTURAL); PLASMA DIAGNOSTICS; SYSTEMS ENGINEERING;

QUALITY CONTROL;

EID: 84878016226     PISSN: 07488017     EISSN: 10991638     Source Type: Journal    
DOI: 10.1002/qre.1396     Document Type: Article

References (25)

1. Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, Liang S,. Adaptive Prognostics For Rolling Element Bearing Condition. Mechanical Systems and Signal Processing 1999; 13 (1): 103-113.
2. Enzo CCL, Ngan HW,. Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis. IEEE Transactions on Instruments and Measurement 2010; 59 (10): 2683-2690.
3. Wade Richard A,. A Need-focused Approach to Air Force Engine Health Management Research. Health Management Research IEEE Aerospace Conference Big Sky, Montana, 2005
4. Marble S, Morton BP,. Predicting the Remaining Useful Life of Propulsion System Bearings. Proceedings of the 2006 IEEE Aerospace Conference, Big Sky, MT, USA, 2006.
5. Li Y, Kurfess TR, Liang SY,. Stochastic Prognostics For Rolling Element Bearing. Mechanical Systems and Signal Processing 2000; 14 (5): 747-762.
6. Zhang B, Sconyers C, Orchard M, Patrick R, Vachtsevanos G,. Fault Progression Modeling: An Application to Bearing Diagnosis and Prognosis. Proceedings of American Control Conference, MD USA, 2010.
7. Davaney M, Eren L,. Detecting Motor Bearing Faults. IEEE Instrumentation and Measurement Magazine 2004; 7 (4): 30-50.
8. McFadden PD, Smith JD,. Vibration monitoring of rolling element bearings by the high frequency resonance technique-a review. Tribology International 1984; 17: 3-10.
9. Tandon N, Choudhury A,. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International 1999; 32: 469-480.
10. Su W, Wang F, Zhu H, Zhang Z, Guo Z,. Rolling element bearing faults diagnosis based on optimal Morlet Wavelet filter and autocorrelation enhancement. Mechanical Systems and Signal Processing 2010; 24: 1458-1472.
11. Bozchalooi IS, Liang M,. A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals. Mechanical Systems and Signal Processing 2008; 22: 915-933.
12. He W, Jiang ZN, Feng K,. Bearing fault detection based on optimal wavelet filter and sparse code shrinkage. Measurement 2009; 42: 1092-1102.
13. Immovilli F, Bellini A, Rubini R, Tassoni C,. Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison. IEEE Transactions on Industry Applications 2010; 46 (4): 1350-1359.
14. Renaudin L, Bonnardot F, Musy O, Doray JB, Remond D,. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing 2010; 24: 1998-2011.
15. Rafiee J, Rafiee MA, Tse PW,. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 2010; 37: 4568-4579.
16. Paya BA, Esat II, Badi MNM,. Artificial neural network based fault diagnosis for rotating machinery using wavelet transforms as a pre-processor. Mechanical Systems and Signal Processing 1997; 11 (5): 751-765.
17. Samanta B,. Gear fault detection using artificial neural network & support vector machine with genetic algorithms. Mechanical Systems and Signal Processing 2004; 18 (3): 625-644.
18. Saravanan N, Cholairajan S, Ramachandran KI,. Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Systems with Applications 2009; 36 (2): 3119-3135.
19. Ocak H, Loparo KA, Discenzo FM,. Online Tracking of bearing wear using wavelet packet decomposition and probabilistic modelling: A method for bearing prognostics. Journal of Sound and Vibration 2007; 302: 951-961.
20. Chinnam RB, Baruah P,. Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering. International Journal of Production Research 2009; 47 (23): 6739-6758.
21. Dong M, He D,. A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing 2007; 21: 2248-2266.
22. Camci F, Chinnam RB,. Health-State Estimation and Prognostics in Machining Processes. IEEE Transactions on Automation Science and Engineering 2010; 7 (3): 581-597.
23. Baruah P, Chinnam RB,. HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research 2005; 43 (6): 1275-1293.
24. Calinski RB, Harabasz J,. A Dendrite Method for Cluster Analysis. Communications in Statistics 1974; 3: 1-27.
25. Eker OF, Camci F, Guclu A, Yilboga H, Sevkli M, Baskan S,. A Simple State-based Prognostic Model for Railway Turnout Systems. IEEE Transactions on Industrial Electronics 2011; 58 (5): 1718-1726.