Skip to main content
Article thumbnail
Location of Repository

A review of research in manufacturing prognostics

By K. M. Goh, Benny Tjahjono, Tim S. Baines and S. Subramaniam

Abstract

With the fast changing global business landscape, manufacturing companies are facing increasing challenge to reduce cost of production, increase equipment utilization and provide innovative products in order to compete with countries with low labour cost and production cost. On of the methods is zero down time. Unfortunately, the current research and industrial solution does not provide user friendly development environment to create “Adaptive microprocessor size with supercomputer performance” solution to reduce downtime. Most of the solutions are PC based computer with off the shelf research software tools which is inadequate for the space constraint manufacturing environment in developed countries. On the other hand, to develop solution for various manufacturing domain will take too much time, there is lacking tools available for rapid or adaptive way of create the solution. Therefore, this research is to understand the needs, trends, gaps of manufacturing prognostics and defines the research potential related to rapid embedded system framework for prognos

Topics: maintenance, prognostics, condition based maintenance, prognostics health monitoring
Year: 2006
DOI identifier: 10.1109/INDIN.2006.275836
OAI identifier: oai:dspace.lib.cranfield.ac.uk:1826/1388
Provided by: Cranfield CERES

Suggested articles

Citations

  1. (2003). A Neural Network Approach to Condition Based Maintenance: Case Study of Airport Ground Transportation Vehicles,"
  2. (2002). A testbed for data fusion for engine diagnostics and prognostics, doi
  3. (2002). AI-based condition monitoring of the drilling process, doi
  4. (1996). An expert system based framework for an incipient failure detection and predictive maintenance system, Intelligent systems applications to power systems, doi
  5. (2003). An interacting multiple model approach to model-based prognostics, Systems, Man and Cybernetics, doi
  6. (2003). An open systems architecture for prognostic inference during condition-based monitoring, doi
  7. Data Mining of Aviation Data for Advancing Health Management, doi
  8. (2001). Fiber optic sensors for predictive health monitoring, doi
  9. (2006). Fuzzy logic – an introduction, part 2,
  10. (2001). Hybrid reasoning for prognostic learning doi
  11. (2004). Integration of material-based simulation into prognosis architectures, doi
  12. (1990). Intelligent system for air-traffic control, doi
  13. (2001). Manufacturing Strategy: Literature Review and Some Issues’, doi
  14. (2005). Methods for Fault Detection, Diagnostics, Prognostics for Building Systems— A Review, Part I, doi
  15. (2003). Neural Networks for Intelligent Signal Processing, Singapore, World Scientific Publishing Co, doi
  16. (1995). Nuclear science, development of an on-line fuzzy expert system for integrated alarm processing in nuclear power plants, doi
  17. (2000). Prognostics, the real issues involved with predicting life remaining, Aerospace Conference Proceedings, doi
  18. (2001). Reasoning and modelling systems in diagnosis and prognosis, doi
  19. (2004). Residual life predictions from vibration-based degradation signals: A neural network approach, doi
  20. (2003). Review of Process Fault Diagnosis - Part I: Quantitative ModelBased Methods”, doi
  21. (2001). Rolling bearing fault diagnostic system using fuzzy logic, IEEE-International Fuzzy system conference, doi
  22. (2004). Smart products and service systems for e-business transformation, 3e Conférence Francophone de MOdélisation et SIMulation « Conception,
  23. (1997). TG; “Smart sensors and system health management tools for avionics and mechanical systems,” doi
  24. (2002). The pursuit of competitive advantage value manufacturing in Singapore, Manufacturing Sub-committee, Singapore Economic review committee, www.edb.gov.sg,

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.