RCAM based maintenance plan of the power transformers using k-means clustering algorithm

Koksal, Aysun; Özdemir, Aydoğan; Ata, Oğuz

doi:10.1109/isap.2017.8071391

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The proposed model was developed to predict the prioritisation of three primary strategic maintenance management, namely preventive maintenance, corrective maintenance, and replacement programme. Sixteen medical equipment features were extracted from the datasets consisting of 13,352 medical equipment and were automatically clustered using the unsupervised k-Means algorithm ( 38 – 42 ). Three priority numbers for each preventive maintenance, corrective maintenance, and replacement programme were determined from the 16 medical equipment features.…”

Section: Methodsmentioning

confidence: 99%

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

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The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

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Section: Methodsmentioning

confidence: 99%

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

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“…Health index calculation supports risk-based maintenance strategies, and industry 4.0 tools significantly benefit the health index calculation. In fact, criticality and health index calculation can be obtained using algorithms, such as the weighted sum algorithm [57], the linear programming algorithm [42], and the k-means clustering algorithm [58]. Authors often use those algorithms to calculate the health index of a single type of equipment [42,57,58].…”

mentioning

confidence: 99%

Industry 4.0 Contribution to Asset Management in the Electrical Industry

Biard

Abdul-Nour

2021

Sustainability

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Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.

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A Strategic Corrective Maintenance Prioritization Assessment for Medical Equipment

Zamzam

Al-Ani

Hasikin‬

et al. 2022

6th Kuala Lumpur International Conference on Biomedical Engineering 2021

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RCAM based maintenance plan of the power transformers using k-means clustering algorithm

Cited by 5 publications

References 27 publications

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Industry 4.0 Contribution to Asset Management in the Electrical Industry

A Strategic Corrective Maintenance Prioritization Assessment for Medical Equipment

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