Essential steps in prognostic health management

2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings

Volumn , Issue , 2011, Pages

  Das, Sreerupa ^a   Hall, Richard ^a   Herzog, Stefan ^a   Harrison, Gregory ^a   Bodkin, Michael ^a   Martin, Lockheed ^a  

^a LOCKHEED MARTIN   (United States)

Author keywords

CNC Milling cutters; Condition Indicators; Neural Network; Prognostic Health Management

Indexed keywords

ANALYTICAL MODEL; CONDITION INDICATORS; FACE MILLING; FAULT CONDITIONS; HEALTH INDICATORS; IN-BUILDINGS; NEURAL NETWORK MODEL; PROGNOSTIC HEALTH MANAGEMENTS; RAW SENSOR; REMAINING USEFUL LIVES; SENSOR DATA; TIME AND FREQUENCY DOMAINS;

HEALTH; MACHINERY; MATHEMATICAL MODELS; MILLING (MACHINING); NETWORK MANAGEMENT; SENSORS;

NEURAL NETWORKS;

EID: 80053622111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPHM.2011.6024332     Document Type: Conference Paper

References (8)

1. Hahn Stefan L., Hilbert transforms in signal processing, Artech House, Inc.,Boston, 1996.
2. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5-32.
3. X. Li, B. S. Lim, J. H. Zhou, S. Huang, S. J. Phua, K. C. Shaw and M. J. Er (2009). Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation, Annual Conference of the Prognostics and Health Management Society, San Diego, CA.
4. I. Kandilli, M. Sönmez, H. M. Ertunc and B. Çakir (2007). Online Monitoring Of Tool Wear In Drilling and Milling By Multi-Sensor Neural Network Fusion, IEEE International Conference on Mechatronics and Automation.
5. V. P. Astakov, S. Shvets (2004). The assessment of plastic deformation in metal cutting. Journal of Materials Processing Technology 146 .
6. P. Scanlon, A. Lyons and A. O'Loughlin (2007). Acoustic signal processing for degradation analysis of rotating machinery to determine the remaining useful life, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
7. D. E. Rumelhart, G. E. Hinton, and R. J. Williams (1986). Learning representations by back-propagating error. Nature, pp. 533-536.
8. M. Riedmiller and H. Braun (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proc. IEEE International Conference On Neural Network, pp. 586-591.