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Fault Detection and Identification for Longwall Machinery Using SCADA Data

In: Complex System Maintenance Handbook

Author

Listed:
  • Daniel R. Bongers

    (Australian Cooperative Research Centre Mining)

  • Hal Gurgenci

    (The University of Queensland)

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.

Suggested Citation

  • Daniel R. Bongers & Hal Gurgenci, 2008. "Fault Detection and Identification for Longwall Machinery Using SCADA Data," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 25, pages 611-641, Springer.
    • Handle: RePEc:spr:ssrchp:978-1-84800-011-7_25
    DOI: 10.1007/978-1-84800-011-7_25
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    Cited by:

    1. Edyta Brzychczy & Paulina Gackowiec & Mirko Liebetrau, 2020. "Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case," Resources, MDPI, vol. 9(2), pages 1-17, February.

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