Predictive Maintenance -- Bridging Artificial Intelligence and IoT

Samatas, Gerasimos G.; Moumgiakmas, Seraphim S.; Papakostas, George A.

doi:10.48550/arxiv.2103.11148

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Tools such as barcodes, radio frequency identification (RFID) tags, Near Field Communications (NFC) and other technologies used by wireless-enabled devices may allow automated asset information monitoring and control at all levels of an operation (Eckfeldt, 2005;Boriello, 2005;Chongwatpol and Sharda, 2013;Chen, 2017). Research and development are also ongoing regarding PdM systems that utilise Machine Learning algorithms and AI derived models for data-driven decision support based on collected data (Samatas et al, 2021). When applied to PdM, such systems allow for the dynamic adjustment of maintenance scheduling, purchasing, inventory, and production in response to changing operational conditions (Cassingham and Allen, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimisation of Maintenance Operations Involving Three Integrated Departments at a Local Oil Company in Trinidad

Harrison¹,

Bansee²,

Chowdary³

2022

WIJE

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A simulation case study was conducted to assess the utility of predictive maintenance and database management for lean maintenance applications within a Petroleum Company. At this company the maintenance of pumps was performed internally where feasible. The study explored a proposed lean strategy on the operations of three departments related to the maintenance of pumping equipment. These departments were, the Pump Shop, Stores Department and Shipping and Receiving. Rockwell Automation’s Arena® simulation software was used to study existing and proposed models of the maintenance system and track the key performance indicators of Flow Time, Waiting Time, and Work-in-Process. Analysis of the performance indicators showed a 76% and a 96% reduction in average Flow Time and Waiting Time, respectively. No difference was determined for Work-in-Process at the 95% confidence interval.

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Section: Literature Reviewmentioning

confidence: 99%

Optimisation of Maintenance Operations Involving Three Integrated Departments at a Local Oil Company in Trinidad

Harrison¹,

Bansee²,

Chowdary³

2022

WIJE

View full text Add to dashboard Cite

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Untitled

2022

WIJE

View full text Add to dashboard Cite

A simulation case study was conducted to assess the utility of predictive maintenance and database management for lean maintenance applications within a Petroleum Company. At this company the maintenance of pumps was performed internally where feasible. The study explored a proposed lean strategy on the operations of three departments related to the maintenance of pumping equipment. These departments were, the Pump Shop, Stores Department and Shipping and Receiving. Rockwell Automation's Arena ® simulation software was used to study existing and proposed models of the maintenance system and track the key performance indicators of Flow Time, Waiting Time, and Work-in-Process. Analysis of the performance indicators showed a 76% and a 96% reduction in average Flow Time and Waiting Time, respectively. No difference was determined for Work-in-Process at the 95% confidence interval.

show abstract

Predictive Maintenance -- Bridging Artificial Intelligence and IoT

Cited by 2 publications

References 17 publications

Optimisation of Maintenance Operations Involving Three Integrated Departments at a Local Oil Company in Trinidad

Optimisation of Maintenance Operations Involving Three Integrated Departments at a Local Oil Company in Trinidad

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