Evolution of Technical Systems Maintenance Approaches – Review and a Case Study

Nowakowski, Tomasz; Tubis, Agnieszka; Werbińska-Wojciechowska, Sylwia

doi:10.1007/978-3-319-97490-3_16

Cited by 16 publications

(28 citation statements)

References 55 publications

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“…Different corporations may have different maintenance strategies depending on several factors, such as maintenance goals, equipment's nature, operational process design, and work environment. Commonly used maintenance approaches are categorised into a) corrective maintenance (CM), b) preventive maintenance (PM), c) predictive maintenance (PdM), and d) proactive maintenance [3]. Amongst these approaches, the PdM aims to make timely maintenance decisions timely by focusing on fault detection, component diagnosis, degradation monitoring, and…”

Section: An Overview Of the Application Of Machine Learning In Predictive Maintenancementioning

confidence: 99%

“…No routine maintenance activity is scheduled in this approach, and maintenance actions only occur after equipment breakdown. It is, therefore, impossible to optimise equipment performance with respect to economic or reliability criteria [3], and subsequently, equipment availability and reliability performance are substandard. Despite being inferior to other approaches, the CM approach is still in use due to the low cost of implementation, and suitable for equipment that has a low budget to repair or replace.…”

Section: Evolution Of Maintenance Approachesmentioning

confidence: 99%

“…In PdM, the optimal time point for maintenance actions is predicted by analysing the system's health state and historical maintenance data to make a timely repair to avoid costly repairs due to system breakdown and premature maintenance activities which may induce infant mortality. The PdM approach is used in industrial sectors where reliability is paramount, like nuclear power plants, transportation systems, or emergency systems [3]. The most used monitoring and diagnostic techniques include vibration monitoring, thermography, tribology, and visual inspection.…”

Section: Reactive/ Breakdownmentioning

confidence: 99%

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An overview of the application of machine learning in predictive maintenance

Trần

Trieu

Tran

et al. 2021

PVJ

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With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world. Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years. This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.

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Section: An Overview Of the Application Of Machine Learning In Predictive Maintenancementioning

confidence: 99%

Section: Evolution Of Maintenance Approachesmentioning

confidence: 99%

Section: Reactive/ Breakdownmentioning

confidence: 99%

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An overview of the application of machine learning in predictive maintenance

Trần

Trieu

Tran

et al. 2021

PVJ

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“…Maintenance management has evolved significantly over time. Progress has been made in the way that maintenance is applied to industrial equipment [2]. Initially, corrective maintenance was the common rule at the early beginning of factory use, where equipment failures were solved as they occur.…”

Section: Introductionmentioning

confidence: 99%

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

Venegas¹,

Ivorra

Ortega

et al. 2022

Sensors

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The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.

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“…Os primeiros passos para a evolução da manutenção se deram aproximadamente nos anos de 1940. As primeiras abordagens relacionadas com manutenção eram chamadas de "Run to failure" (correr para o fracasso) ou ainda estratégia de manutenção corretiva, um outro termo frequentemente utilizado na época [19].…”

Section: Evolução Historíca Da Manutençãounclassified

Análise de dados de manutenção para avaliação da disponibilidade de equipamentos de ressonância magnética em estabelecimentos assistenciais de saúde

Alves¹

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Magnetic Resonance Imaging (MRI) or only Magnetic Resonance (MR) is one of the most important imaging diagnostic and biomedical equipment on the market. However, this equipment is subject to unwanted downtime, affecting the availability of the device and, consequently, causing a reduction in the number of examinations performed by Health Care Establishments (HCEs), in addition to the high operating costs that are spent. The objective of this work was to conduct a study on the main factors affecting the availability and operating costs of magnetic resonance imaging equipment in public and private EASs, and through these values to verify their viability. The method initially adopted was the search for data in the field, that is, to obtain the pertinent data to the historical of occurrences of five different MR equipment. In addition, we tried to analyze the data related to the historical of costs, being, income and expenses for an EAS RM equipment. A priori from the history of occurrences it can be seen that there are categories of service interruptions that actually affect the availability of MRI equipment and, consequently, the amount of examinations performed, leading to economic losses by EASs. In addition, it can be seen that for the analyzed EAS the equipment expense amounts were operating at more than four times the revenue received by the EAS. The data show that there are categories of service interruption that affect the availability of MRI equipment, being detrimental to the safes of the EASs. Moreover, through the data it can be seen that for the analyzed EAS the provision of the MRI service is not feasible.

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Evolution of Technical Systems Maintenance Approaches – Review and a Case Study

Cited by 16 publications

References 55 publications

An overview of the application of machine learning in predictive maintenance

An overview of the application of machine learning in predictive maintenance

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

Análise de dados de manutenção para avaliação da disponibilidade de equipamentos de ressonância magnética em estabelecimentos assistenciais de saúde

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