An Architecture for Proactive Maintenance in the Machinery Industry

Canito, Alda; Fernandes, Marta; Conceição, Luís; Praça, Isabel; Santos, Magno; Rato, Ricardo; Cardeal, Gonçalo; Leiras, Francisco; Marreiros, Goreti

doi:10.1007/978-3-319-61118-1_31

Cited by 9 publications

(3 citation statements)

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“…The CPS architecture implemented in maintenance is defined as another research problem investigated in the reviewed literature. The main aspects regard: the implementation of a reference architecture for cyber–physical systems (CPS) to support the condition-based maintenance (CBM) of industrial assets [ 288 ], the designing of industrial control system architecture for large-scale industrial and infrastructure construction projects [ 289 ], self-aware machines development and support [ 290 , 291 ], predictive maintenance system architecture [ 292 , 293 ], manufacturing process scheduling and control [ 294 ], an industrial event-driven architecture implementation [ 295 ], and key performance indicators for CPS development [ 296 ].…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, 4) Maintenance system architecture, and 5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

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Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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“…Also, the authors mention that several sources of information in current prognostics methods remain untapped, such as peer-to-peer evaluation and historical life-cycle information from identical assets. Insightful discussions and guidelines regarding solutions for PMS can also be found in the current literature [17,18], with some common denominators including the employment of CPPS for virtualization, ML models for data analysis (e.g. early fault detection, quality control), decentralization and self-adjustment.…”

Section: Related Workmentioning

confidence: 99%

IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

Peres

Rocha

Leitão

et al. 2018

Computers in Industry

133

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A B S T R A C TThe manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.

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“…The second step includes all necessary activities in order to achieve the target. The third step is based on continuous control activities on monitoring and controlling (Canito et al, 2017). The most significant root causes of Sungun lubrication system problems were contamination and oil additive level decreasing.…”

Section: Introductionmentioning

confidence: 99%

The role of sustainable oil maintenance on lubrication system reliability

Fatemi¹

2022

10.5267/j.jfs

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This article discusses the value of sustainable oil maintenance by using submicron high-tech offline filtration based on oil condition monitoring to reduce wear particles and improve system reliability. These activities incorporate broader goals of establishing a proactive maintenance approach, defined as continuous monitoring and controlling of the root causes of machine failure. Among these causes, contamination is the industry's most severe, popular, and most recognized failure cause. In order to ensure all necessary proactive maintenance activities, this paper uses practical analysis for oil contamination monitoring based on ISO 4405. The author applied a 0.1-micron high-tech offline filtration for lubrication systems of ball mills in the Sungun copper mine as an oil maintenance activity to control the system lubrication contamination as a root cause of failures.

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An Architecture for Proactive Maintenance in the Machinery Industry

Cited by 9 publications

References 10 publications

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

The role of sustainable oil maintenance on lubrication system reliability

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