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Sociotechnical Dynamics in Maintenan...

do Rego Ferreira Lima, Mateus Jose,

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Sociotechnical Dynamics in Maintenance Operations: Addressing Human Biases in Time-Based and Condition-Based Maintenance Strategies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sociotechnical Dynamics in Maintenance Operations: Addressing Human Biases in Time-Based and Condition-Based Maintenance Strategies // Mateus Jose do Rego Ferreira Lima.
Author:	do Rego Ferreira Lima, Mateus Jose,
Description:	1 electronic resource (195 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Behavioral sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31836936
ISBN:	9798346852919

Sociotechnical Dynamics in Maintenance Operations: Addressing Human Biases in Time-Based and Condition-Based Maintenance Strategies /
do Rego Ferreira Lima, Mateus Jose,

Sociotechnical Dynamics in Maintenance Operations: Addressing Human Biases in Time-Based and Condition-Based Maintenance Strategies /Mateus Jose do Rego Ferreira Lima. - 1 electronic resource (195 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

This dissertation explores the integration of human-centered strategies and sensor-based technologies to enhance condition-based preventive maintenance (CB-PM) in asset-intensive industries. While IoT and sensor advancements offer the potential to optimize maintenance by monitoring equipment in real time, human factors continue to hinder the effectiveness of CB-PM, echoing challenges from time-based preventive maintenance (TB-PM). The first paper models how operator effort investments, alongside sensor signals, can optimize CB-PM policies. Using a Markov decision process, it identifies optimal thresholds for maintenance and operator engagement, revealing the critical role of human input in CB-PM?s success. The second paper examines the risks of postponed maintenance in large fleet operations, analyzing data from approximately 6,000 maintenance events. It demonstrates how delayed maintenance and misleading signals, such as battery charge levels, increase the likelihood of equipment failure, emphasizing the limits of TB-PM and the need for human oversight. The third paper offers a solution to the postponement problem by integrating TB-PM as a secondary signal within CB-PM. Through a controlled experiment, it shows how behavioral nudges and supplemental signals improve adherence to maintenance protocols, reducing costs and risks associated with non-compliance.

English

ISBN: 9798346852919Subjects--Topical Terms:

529833
Behavioral sciences.
Subjects--Index Terms:

Time-based preventive maintenance

Sociotechnical Dynamics in Maintenance Operations: Addressing Human Biases in Time-Based and Condition-Based Maintenance Strategies /
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520 $a This dissertation explores the integration of human-centered strategies and sensor-based technologies to enhance condition-based preventive maintenance (CB-PM) in asset-intensive industries. While IoT and sensor advancements offer the potential to optimize maintenance by monitoring equipment in real time, human factors continue to hinder the effectiveness of CB-PM, echoing challenges from time-based preventive maintenance (TB-PM). The first paper models how operator effort investments, alongside sensor signals, can optimize CB-PM policies. Using a Markov decision process, it identifies optimal thresholds for maintenance and operator engagement, revealing the critical role of human input in CB-PM?s success. The second paper examines the risks of postponed maintenance in large fleet operations, analyzing data from approximately 6,000 maintenance events. It demonstrates how delayed maintenance and misleading signals, such as battery charge levels, increase the likelihood of equipment failure, emphasizing the limits of TB-PM and the need for human oversight. The third paper offers a solution to the postponement problem by integrating TB-PM as a secondary signal within CB-PM. Through a controlled experiment, it shows how behavioral nudges and supplemental signals improve adherence to maintenance protocols, reducing costs and risks associated with non-compliance.
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