Proper detection and diagnosis of failing system components is crucial to efficient mining operations. However, the harsh mining environment offers special challenges to these types of actions. The atmosphere is damp, dirty, and potentially explosive, and equipment is located in confined areas far from shop facilities. These conditions, coupled with the increasing cost of downtime and complexity of mining equipment, have forced researchers and operators to investigate alternatives for improving equipment maintainability. This paper surveys monitoring and diagnosis technologies that offer opportunities for improving equipment availability in mining. Expert systems, model-based approaches, and neural nets are each discussed in the context of fault detection and diagnosis. The paper concludes with a comparative discussion summarizing the advantages and disadvantages of each.
|Number of pages||7|
|Journal||IEEE Transactions on Industry Applications|
|State||Published - Oct 1 1994|
Bibliographical noteFunding Information:
Paper PID 94-04 approved by the Mininig Industry Committee of the IEEE Industry Applications Society for presentation at the 1992 U S Annual Meeting. This work was supported in part by the National Science Foundation under Grant ECS-9308737, by the National Aeronautics and Space Administration under Grant NGT40049, by Rockwell International, and by the Center for Robotics and Manufacturing Sys- tems, University of Kentucky. Manuscript released for publication April 12, 1994.
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering