A CPS for Condition Based Maintenance Based on a Multi-agent System for Failure Modes Prediction in Grid Connected PV Systems

Bermejo, Jesús Ferrero; Fernández, Juan Pablo Murga; López, Antonio J. Guillén; Polo, Fernando Olivencia; Márquez, Adolfo Crespo; Díaz, Vicente

doi:10.1007/978-3-030-20704-5_8

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The manufacturing industry faces ongoing challenges in reducing equipment downtime and preventing unexpected failures, which are essential for maintaining operational efficiency and reducing costs [99]. Traditional maintenance methods, largely based on periodic inspections, often prove inadequate, leading to increased inefficiencies and downtime [35], [100]. Addressing these issues, we examine the effectiveness of our newly developed MAS framework designed to significantly improve PdM in this sector.…”

Section: Case Study Backgroundmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

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Section: Case Study Backgroundmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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“…Wang et al [12] introduced a fault diagnosis technique for photovoltaic panels, which uses sampled data related to current-voltage characteristics. Ferrero Bermejo et al [13] proposed a sophisticated Cyber-Physical Systems (CPS) approach for condition monitoring of grid-connected photovoltaic systems. They use multiagent system architecture and artificial neural-networks on top of the existing control and data acquisition system.…”

Section: Of 13mentioning

confidence: 99%

An Optimal Preventive Maintenance Policy for a Solar Photovoltaic System

et al. 2020

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In this paper, we develop a preventive maintenance (PM) strategy for a solar photovoltaic system composed of solar panels functioning as a series system. The photovoltaic system is considered in a failed state whenever its efficiency drops below a predefined threshold or any electrical wiring element is damaged. In such a situation of failure, a minimal repair is performed. The proposed PM strategy suggests systematically replacing n panels with their respective wiring system every time units T over a finite operating time span H. The panels to be preventively replaced are selected by the maintenance agent after an on-site overall assessment of all panels, making sure every time not to replace panels previously replaced during a given replacement cycle of all panels of the system. An analytical model is proposed in order to simultaneously determine the optimal PM period, T, and the optimal number of solar panels, n, to be replaced at each PM. This is done by modeling and minimizing the expected total maintenance cost over the finite operating time horizon H. A numerical example is presented to illustrate the use of the proposed modelling approach and to discuss the obtained results. The latter provide the optimal solutions (T*, n*) for different combinations of input parameters. They also show the economic relevance of the proposed PM strategy through estimation of the economic gain when comparing the situations with and without preventive maintenance.

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ML2MAS: A Multi-Agent Reinforcement Learning and BNNs-GAN Integration Framework for Smart Manufacturing Optimization

Baroud,

Yahaya

2024

Preprint

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A CPS for Condition Based Maintenance Based on a Multi-agent System for Failure Modes Prediction in Grid Connected PV Systems

Cited by 3 publications

References 22 publications

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

An Optimal Preventive Maintenance Policy for a Solar Photovoltaic System

ML2MAS: A Multi-Agent Reinforcement Learning and BNNs-GAN Integration Framework for Smart Manufacturing Optimization

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