PROGRAMS project approach to maintenance management

Surico, Michele; Ricatto, Raffaele; Merlo, Angelo; Németh, István; Sardelis, Andreas; Villoslada, M.; Montejo, Elena; Frenkel, N.; Aivaliotis, P.; Celada, Ignacio Martinez De la Pera; Sidiropoulos, J.; Eytan, Amit; Papavasileiou, A.; Aggogeri, Francesco

doi:10.1016/j.ifacol.2020.11.050

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…It should be noted that simply looking for explicit definitions for SRM would have yielded poor results. Only a few of the reviewed papers mentioned the word retrofit (Uhlmann et al, 2017;Cattaneo & Macchi, 2019;Sepehri et al, 2018;Strauß et al, 2018;Hesser & Markert, 2019;Surico et al, 2020;Bernal et al, 2018), and they used it without attempting to formalize a definition. Because of this, the following approach was taken to answer this research question.…”

Section: Rq1: Definitionsmentioning

confidence: 99%

“…This can be done through preexisting sensing capabilities if they are available (Luo, 2020; Chau et al, 2015;Calabrese et al, 2020), but other times the installation of additional sensors is necessary (Huber et al, 2019;Prathima et al, 2020;Kiangala & Wang, 2018). Such sensors are sometimes added in the form of sensor nodes (Christou et al, 2020;Surico et al, 2020;Catenazzo et al, 2018), which are IoT devices that integrate both sensing and communication capabilities. Although it might not always be possible, some authors emphasize the need to collect data in a non-invasive manner.…”

Section: Data Collectionmentioning

confidence: 99%

“…The most frequently measured variables were vibration (Surico et al, 2020;Aqueveque et al, 2021;Magadán et al, 2020) and temperature (Ciancio et al, 2020;Iqbal et al, 2019;Short & Twiddle, 2019), followed by electric current (Ge et al, 2019;Strauß et al, 2018;Barksdale et al, 2018). The most analyzed components were motors, induction or otherwise (Rubio et al, 2018;Eiskop et al, 2017;Talmoudi et al, 2019), followed by bearings (Richter et al, 2019;Bernal et al, 2018).…”

Section: Proactive Maintenancementioning

confidence: 99%

“…It also includes maintenance itself, both labor-wise and parts-wise. Numerous examples can be found in which SRM is demonstrated to save resources, such as maintenance costs (Ramani et al, 2016;Surico et al, 2020), materials (including spare parts) (Bernal et al, 2018;Priller et al, 2014), and energy (Alonso et al, 2018;Pignatelli et al, 2015). Of special interest are ways in which SRM can simplify the maintenance process, potentially saving money and time.…”

Section: Costsmentioning

confidence: 99%

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Smart retrofitting in maintenance: a systematic literature review

2022

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The last decade saw the rise of digitalization and data-supported decision making in the manufacturing industry: the Fourth Industrial Revolution. This trend, also known as Industry 4.0, allows manufacturing enterprises to discover manufacturing uncertainties and measure their real manufacturing capability. One of the ways in which Industry 4.0 trends have been exploited is in the improvement of maintenance, which went from following planning-focused paradigms to more proactive-focused stances. Enabling the Industry 4.0 vision for maintenance purposes has historically required companies to either replace or upgrade their existing legacy devices. It is through the latter course of action that Smart retrofitting in maintenance (SRM) intends to bring value to enterprises. This work aims to present a systematic literature review on SRM, utilizing the oft-cited PRISMA methodology. Through this analysis, a definition of SRM that reflects the current state of the art is proposed. Furthermore, the research in SRM applied in the context of different maintenance strategies is assessed (i.e. reactive, planned, proactive and strategic maintenance), and the most common drivers and challenges in SRM are presented. Finally, a roadmap for the implementation of SRM is proposed. The analysis of the SRM literature reveals that there are important research opportunities in the exploitation of SRM for strategic maintenance and asset management. The authors hope that this document leads to the consolidation of a new research area that aims to add value to maintenance in enterprises through the application of smart retrofitting in preexisting legacy devices.

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Section: Rq1: Definitionsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Proactive Maintenancementioning

confidence: 99%

Section: Costsmentioning

confidence: 99%

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Smart retrofitting in maintenance: a systematic literature review

2022

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“…Since their degradation and damage level can be affected by working operations as well as other disturbances in harsh manufacturing environments, such as lubricants, soiling, and temperature, the results from traditional wear and fatigue models are connected with considerable uncertainties. DT-driven condition monitoring (CM) and predictive maintenance (PdM) highlight the possibility for the process-parallel monitoring and diagnosis of the health status of the critical components (i.e., tools [ 116 , 117 ], bearings [ 118 ], ball screws [ 119 ], gears [ 15 , 120 , 121 ], pumps [ 122 ]) and the energy efficiency of the equipment [ 123 , 124 ] in order to handle the conflict between the unplanned maintenance operations and the resulting costs and productivity, particularly in SMEs, due to their limited capacity for the full deployment of a PdM strategy [ 125 ]. Additionally, DT-based optimal control [ 126 , 127 ] as well as machine dynamics issues [ 128 , 129 ] from a rotating system [ 130 ] to feed drive [ 131 , 132 ] are other important aspects.…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems

Abbate,

Franciosi,

Voisin

et al. 2024

IET Collab Intel Manufact

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Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.

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PROGRAMS project approach to maintenance management

Cited by 3 publications

References 5 publications

Smart retrofitting in maintenance: a systematic literature review

Smart retrofitting in maintenance: a systematic literature review

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems

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