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Thirupathi Samala
Department of Mechanical Engineering, NIT Warangal, Warangal 506004, India

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Journal article
Published: 10 May 2021 in Sustainability
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Complex systems consist of multiple machines that are designed with a certain extent of redundancy to control any unanticipated events. The productivity of complex systems is highly affected by unexpected simultaneous machine failures due to overrunning of machines, improper maintenance, and natural characteristics. We proposed realistic configurations with multiple machines having several flexibilities to handle the above issues. The objectives of the proposed model are to reduce simultaneous machine failures by slowing down the pace of degradation of machines, to improve the average occurrence of the first failure time of machines, and to decrease the loss of production. An approach has been developed using each machine’s degradation information to predict the machine’s residual life based on which the job adjustment strategy where machines with a lower health status will be given a high number of jobs to perform is proposed. This approach is validated by applying it in a fabric weaving industry as a real-world case study under different scenarios and the performance is compared with two other key benchmark strategies.

ACS Style

Thirupathi Samala; Vijaya Manupati; Bethalam Nikhilesh; Maria Varela; Goran Putnik. Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status. Sustainability 2021, 13, 5295 .

AMA Style

Thirupathi Samala, Vijaya Manupati, Bethalam Nikhilesh, Maria Varela, Goran Putnik. Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status. Sustainability. 2021; 13 (9):5295.

Chicago/Turabian Style

Thirupathi Samala; Vijaya Manupati; Bethalam Nikhilesh; Maria Varela; Goran Putnik. 2021. "Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status." Sustainability 13, no. 9: 5295.

Review
Published: 25 February 2021 in Future Internet
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Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

ACS Style

Thirupathi Samala; Vijaya Manupati; Maria Varela; Goran Putnik. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. Future Internet 2021, 13, 57 .

AMA Style

Thirupathi Samala, Vijaya Manupati, Maria Varela, Goran Putnik. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. Future Internet. 2021; 13 (3):57.

Chicago/Turabian Style

Thirupathi Samala; Vijaya Manupati; Maria Varela; Goran Putnik. 2021. "Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review." Future Internet 13, no. 3: 57.