Intelligent Maintenance Systems and Predictive Manufacturing

Lee, Jay; Ni, Jun; Singh, Jaskaran; Jiang, Baoyang; Azamfar, Moslem; Feng, Jianshe

doi:10.1115/1.4047856

Cited by 80 publications

(35 citation statements)

References 192 publications

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“…Origins of manufacturing systems until the modern I4.0 paradigm have correspondences with cognitive features of human behavior to get smart control (Galán et al, 2000). For example, contemporary automation systems are addressed by distributed artificial intelligence from various perspectives, such us: Multi-agent Systems (MAS) (Leitão et al, 2016;Salvador Palau et al, 2019;Seitz et al, 2021), Services Oriented Architecture or SoA for manufacturing (Garc´ıa V. et al, 2013;Gamboa Q. et al, 2013), HMS (Leitão and Restivo, 2008;Hsieh, 2009;Borangiu et al, 2014;Valckenaers, 2020), HMS for continuous processes (Chokshi and McFarlane, 2008a;Chacón et al, 2009;Indriago et al, 2014), Cyber-Physiscal Systems (CPS) (Lee et al, 2020;Monostori et al, 2016). All of them are different approaches but they follow common requirements to archive the I4.0 paradigm, as introduced in the next sub-section.…”

Section: Literature Review: Intelligent Automation Systems Addressing I40 Requirementsmentioning

confidence: 99%

“…In the decision-making process, the human must have the possibility of interacting in fully automated environments, to systems with manual decision-making (Cardin, 2019). Despite the human intervention in the operational level has been restricted to reduce variability in operations, the worker operator is indispensable in some situations as systems' repair and maintenance Lee et al, (2020). The main roles of the human in the system are:…”

Section: The Human In the Loopmentioning

confidence: 99%

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A control architecture for continuous production processes based on industry 4.0: water supply systems application

et al. 2021

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Industry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes is fast becoming a key driver for manufacturing operations. Manufacturing control systems have recently been developed for distributed or semi-heterarchical architectures, e.g., holonic systems improving global efficiency and manufacturing operations’ reactiveness. So far, previous studies and applications have not dealt with continuous production processes, such as applications for Water Supply System (WSS), oil refining, or electric power plants. The complexity of continuous production is that a single fault can degrade extensively and even cause service disruption. Therefore, this paper proposes the Holonic Production Unit (HPU) architecture as a solution to control continuous production processes. An HPU is created as a holon unit depicting resources in a continuous process. This unit can detect events within the environment, evaluate several courses of action, and change the parameters aligned to a mission. The proposed approach was tested using a simulated model of WSS. The experiments described in this paper were conducted using a traditional WSS, where the communication and decision-making features allow the application of HPU. The results suggest that constructing a holarchy with different holons can fulfill I4.0 requirements for continuous production processes.

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Section: Literature Review: Intelligent Automation Systems Addressing I40 Requirementsmentioning

confidence: 99%

Section: The Human In the Loopmentioning

confidence: 99%

A control architecture for continuous production processes based on industry 4.0: water supply systems application

et al. 2021

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“…Cyber-physical system (CPS) refers to the mechanism that tightly integrates the computational and physical resources using data analytical techniques (Lee et al, 2015). CPS has been drawing more and more attention from both academia and industry in recent years Lee et al, 2020). One primary benefit of adapting CPS to a healthcare application is enabling doctors and clinicians to monitor patients from anywhere at any time via information and sensing technologies (Zhang et al, 2015).…”

Section: Current Practice In Healthcare Cpsmentioning

confidence: 99%

Development of An Integrated Framework for Cyber Physical System (CPS)-Enabled Rehabilitation System

Feng

Zhu

Пин

et al. 2021

IJPHM

Self Cite

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A Cyber-Physical System (CPS)-enabled rehabilitation system framework for enhanced recovery rate in gait training systems is presented in this paper. Recent advancements in sensing and data analytics have paved the way for the transformation of healthcare systems from experience-based to evidence-based. To this end, this paper introduces a CPS-enabled rehabilitation system that collects, processes, and models the data from patient and rehabilitative training machines. This proposed system consists of a set of sensors to collect various physiological data as well as machine parameters. The sensors and data acquisition systems are connected to an edge computing unit that handles the data preprocessing, analytics, and results visualization. Advanced machine learning algorithms are used to analyze data from physiological data, machine parameters, and patients’ metadata to quantify each patient’s recovery progress, devise personalized treatment strategies, adjust machine parameters for optimized performance, and provide feedback regarding patient’s adherence to instructions. Moreover, the accumulation of the knowledge gathered by patients with different conditions can provide a powerful tool for better understanding the human-machine interaction and its impact on patient recovery. Such system can eventually serve as a ‘Virtual Doctor’, providing accurate feedback and personalized treatment strategies for patients.

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“…There exists a large number of terminologies that are supposed to define maintenance management in Industry 4.0 such as, e-Maintenance [32], intelligent maintenance [33,34], smart maintenance [35], deep digital maintenance [36], and Maintenance 4.0 [37]. However, this paper adopts the terminology of intelligent maintenance, which intentionally differs from other terminologies e.g., smart maintenance [35], e-maintenance [32], as the focus is not primarily based on data analysis i.e., detection, diagnosis, and prognosis.…”

Section: Intelligent Maintenance Vs Maintenance 40mentioning

confidence: 99%

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

Nordal

El‐Thalji

2021

Applied Sciences

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The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

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Intelligent Maintenance Systems and Predictive Manufacturing

Cited by 80 publications

References 192 publications

A control architecture for continuous production processes based on industry 4.0: water supply systems application

A control architecture for continuous production processes based on industry 4.0: water supply systems application

Development of An Integrated Framework for Cyber Physical System (CPS)-Enabled Rehabilitation System

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

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