Predictive maintenance as an integral part of asset life cycle maintenance model

© Springer Nature Switzerland AG 2019. In a lean economy and stringent maintenance budget, it is necessary to implement a flexible and dynamic Asset Life Cycle Maintenance Model (ALCMM). Also, it is important to achieve the availability and reliability goals. Maintenance best practice entails a sequ...

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Main Authors: Hassan, M., Boehl, C., Kuruppu, Mahinda
Format: Book Chapter
Published: 2019
Online Access:http://hdl.handle.net/20.500.11937/70888
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author Hassan, M.
Boehl, C.
Kuruppu, Mahinda
author_facet Hassan, M.
Boehl, C.
Kuruppu, Mahinda
author_sort Hassan, M.
building Curtin Institutional Repository
collection Online Access
description © Springer Nature Switzerland AG 2019. In a lean economy and stringent maintenance budget, it is necessary to implement a flexible and dynamic Asset Life Cycle Maintenance Model (ALCMM). Also, it is important to achieve the availability and reliability goals. Maintenance best practice entails a sequence of activities in a maintenance strategy- proactive maintenance, predictive maintenance, preventive maintenance and reactive maintenance. However, in the current mining economic downturn, this trend was interrupted due to maintenance budget cuts. In reality, this trend costs more money than following the best maintenance practice sequence. The author aims to depict a simple approach which integrates the predictive maintenance (using vibration analysis) with preventive maintenance targeting the critical equipment to maximise the equipment reliability and availability. The proposed approach will be implemented in three steps-working out the reliability of equipment using First Order Reliability Method (FORM), comparing with the preventive maintenance timeline and finally using the right logical decision to undertake the maintenance of equipment to avoid failures.
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spelling curtin-20.500.11937-708882018-12-13T09:33:09Z Predictive maintenance as an integral part of asset life cycle maintenance model Hassan, M. Boehl, C. Kuruppu, Mahinda © Springer Nature Switzerland AG 2019. In a lean economy and stringent maintenance budget, it is necessary to implement a flexible and dynamic Asset Life Cycle Maintenance Model (ALCMM). Also, it is important to achieve the availability and reliability goals. Maintenance best practice entails a sequence of activities in a maintenance strategy- proactive maintenance, predictive maintenance, preventive maintenance and reactive maintenance. However, in the current mining economic downturn, this trend was interrupted due to maintenance budget cuts. In reality, this trend costs more money than following the best maintenance practice sequence. The author aims to depict a simple approach which integrates the predictive maintenance (using vibration analysis) with preventive maintenance targeting the critical equipment to maximise the equipment reliability and availability. The proposed approach will be implemented in three steps-working out the reliability of equipment using First Order Reliability Method (FORM), comparing with the preventive maintenance timeline and finally using the right logical decision to undertake the maintenance of equipment to avoid failures. 2019 Book Chapter http://hdl.handle.net/20.500.11937/70888 10.1007/978-3-319-95711-1_23 restricted
spellingShingle Hassan, M.
Boehl, C.
Kuruppu, Mahinda
Predictive maintenance as an integral part of asset life cycle maintenance model
title Predictive maintenance as an integral part of asset life cycle maintenance model
title_full Predictive maintenance as an integral part of asset life cycle maintenance model
title_fullStr Predictive maintenance as an integral part of asset life cycle maintenance model
title_full_unstemmed Predictive maintenance as an integral part of asset life cycle maintenance model
title_short Predictive maintenance as an integral part of asset life cycle maintenance model
title_sort predictive maintenance as an integral part of asset life cycle maintenance model
url http://hdl.handle.net/20.500.11937/70888