Towards true dynamic decision making in maintenance

Adam Adgar, Erkki Jantunen, Aitor Arnaiz

    Research output: Contribution to conferenceConference articleScientificpeer-review

    Abstract

    The maintenance of machinery and assets in European industry has been shown to account for a significant proportion of operating costs, however substantial savings are possible through the use of more technologically advanced approaches. Modern industrial production systems are experiencing ever increasing demands for improved machinery reliability, efficiency, safety and environmental performance. Maintenance system technology has progressed to some extent but complete solutions with the flexibility to satisfy the demands of a wide range of users are still not widely utilised. One current research project, DYNAMITE (Dynamic Decisions in Maintenance) intends to address this problem by developing and applying a blend of leading-edge communications and sensor technology, combined with state-of-the-art diagnostic and prognostic techniques. The objective of the project is to deliver a prototype maintenance system to enable the monitoring of machines and processes for predictive maintenance and control. An infrastructure for mobile monitoring technology is to be developed along with devices incorporating sensors and algorithms to support enhanced capability for decision support systems. A key strategy of this project involves the extensive use of stored and transmitted electronic data in order to ensure availability fo up-to-date, accurate and detailed information. This strategy provides great advantages for both human and machine-based decision making capability. For instance the system aims to assist in the inspection and maintenance process by identifying priority cases, collating and delivering detailed documentation on maintenance procedures and also to plan and schedule these activities. Several key aspects of the project will be identified and the methods and technologies used to develop the maintenance infrastructures that allow such rapid, efficient, and costeffective decisions to be made will be discussed.

    Original languageEnglish
    Publication statusPublished - 1 Dec 2008
    Event62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT - Virginia Beach, VA, United States
    Duration: 6 Apr 20088 Apr 2008

    Conference

    Conference62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT
    CountryUnited States
    CityVirginia Beach, VA
    Period6/04/088/04/08

    Fingerprint

    Decision making
    Machinery
    Monitoring
    Sensors
    Decision support systems
    Operating costs
    Inspection
    Availability
    Communication
    Industry

    Keywords

    • Condition monitoring
    • Decision making
    • Diagnostics
    • Maintenance strategy
    • Reliability.

    Cite this

    Adgar, A., Jantunen, E., & Arnaiz, A. (2008). Towards true dynamic decision making in maintenance. Paper presented at 62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT, Virginia Beach, VA, United States.
    Adgar, Adam ; Jantunen, Erkki ; Arnaiz, Aitor. / Towards true dynamic decision making in maintenance. Paper presented at 62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT, Virginia Beach, VA, United States.
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    Adgar, A, Jantunen, E & Arnaiz, A 2008, 'Towards true dynamic decision making in maintenance', Paper presented at 62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT, Virginia Beach, VA, United States, 6/04/08 - 8/04/08.

    Towards true dynamic decision making in maintenance. / Adgar, Adam; Jantunen, Erkki; Arnaiz, Aitor.

    2008. Paper presented at 62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT, Virginia Beach, VA, United States.

    Research output: Contribution to conferenceConference articleScientificpeer-review

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    AU - Jantunen, Erkki

    AU - Arnaiz, Aitor

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    N2 - The maintenance of machinery and assets in European industry has been shown to account for a significant proportion of operating costs, however substantial savings are possible through the use of more technologically advanced approaches. Modern industrial production systems are experiencing ever increasing demands for improved machinery reliability, efficiency, safety and environmental performance. Maintenance system technology has progressed to some extent but complete solutions with the flexibility to satisfy the demands of a wide range of users are still not widely utilised. One current research project, DYNAMITE (Dynamic Decisions in Maintenance) intends to address this problem by developing and applying a blend of leading-edge communications and sensor technology, combined with state-of-the-art diagnostic and prognostic techniques. The objective of the project is to deliver a prototype maintenance system to enable the monitoring of machines and processes for predictive maintenance and control. An infrastructure for mobile monitoring technology is to be developed along with devices incorporating sensors and algorithms to support enhanced capability for decision support systems. A key strategy of this project involves the extensive use of stored and transmitted electronic data in order to ensure availability fo up-to-date, accurate and detailed information. This strategy provides great advantages for both human and machine-based decision making capability. For instance the system aims to assist in the inspection and maintenance process by identifying priority cases, collating and delivering detailed documentation on maintenance procedures and also to plan and schedule these activities. Several key aspects of the project will be identified and the methods and technologies used to develop the maintenance infrastructures that allow such rapid, efficient, and costeffective decisions to be made will be discussed.

    AB - The maintenance of machinery and assets in European industry has been shown to account for a significant proportion of operating costs, however substantial savings are possible through the use of more technologically advanced approaches. Modern industrial production systems are experiencing ever increasing demands for improved machinery reliability, efficiency, safety and environmental performance. Maintenance system technology has progressed to some extent but complete solutions with the flexibility to satisfy the demands of a wide range of users are still not widely utilised. One current research project, DYNAMITE (Dynamic Decisions in Maintenance) intends to address this problem by developing and applying a blend of leading-edge communications and sensor technology, combined with state-of-the-art diagnostic and prognostic techniques. The objective of the project is to deliver a prototype maintenance system to enable the monitoring of machines and processes for predictive maintenance and control. An infrastructure for mobile monitoring technology is to be developed along with devices incorporating sensors and algorithms to support enhanced capability for decision support systems. A key strategy of this project involves the extensive use of stored and transmitted electronic data in order to ensure availability fo up-to-date, accurate and detailed information. This strategy provides great advantages for both human and machine-based decision making capability. For instance the system aims to assist in the inspection and maintenance process by identifying priority cases, collating and delivering detailed documentation on maintenance procedures and also to plan and schedule these activities. Several key aspects of the project will be identified and the methods and technologies used to develop the maintenance infrastructures that allow such rapid, efficient, and costeffective decisions to be made will be discussed.

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    KW - Maintenance strategy

    KW - Reliability.

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    Adgar A, Jantunen E, Arnaiz A. Towards true dynamic decision making in maintenance. 2008. Paper presented at 62nd Meeting of the Society for Machinery Failure Prevention Technology, MFPT, Virginia Beach, VA, United States.