Fault Detection and Identification for Longwall Machinery Using SCADA Data

Daniel R. Bongers and Hal Gurgenci
Additional contact information
Daniel R. Bongers: Australian Cooperative Research Centre Mining
Hal Gurgenci: The University of Queensland

Chapter 25 in Complex System Maintenance Handbook, 2008, pp 611-641 from Springer

Abstract: Abstract Despite the most refined maintenance strategies, equipment failures do occur. The degree to which an industrial process or system is affected by these depends on the severity of the faults/failures, the time required to identify the faults and the time required to rectify the faults. Real-time fault detection and identification (FDI) offers maintenance personnel the ability to minimise, and potentially eliminate one or more of these factors, thereby facilitating greater equipment utilisation and increased system availability.

Keywords: Fault Detection; Preventative Maintenance; Longwall Mining; Longwall Face; Maintenance Event (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-84800-011-7_25

Ordering information: This item can be ordered from
http://www.springer.com/9781848000117

DOI: 10.1007/978-1-84800-011-7_25

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().