A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case

Filho, Arnaldo Rabello de Aguiar Vallim; Moraes, Daniel Farina; Vallim, Marco Vinicius Bhering de Aguiar; Silva, Leilton Santos da; Silva, Leandro Augusto da

doi:10.3390/en15103724

Cited by 12 publications

(5 citation statements)

References 62 publications

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“…The prediction accuracy of a machine learning-based predictive model depends on the size of the training data, the underlying features of the data and the selection of the ML algorithm [22]. The FEA simulations data set is small (27 samples) and the test matrix for the AFP FEA model is sparse as shown in Table 3.…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning based process monitoring of automated composites manufacturing to enable digital twin development

Mujtaba¹,

Islam

Kaeding³

et al. 2022

Preprint

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Automated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite manufacturing. The interlaminar strength of AFP-manufactured composites depends on the in-situ thermal history during manufacturing. The thermal history is controlled by the choice of processing conditions and improper interfacial temperatures may result in insufficient bonding. Being able to better predict such maintenance issues in real-time is an important focus of smart manufacturing and Industry 4.0 to improve manufacturing operations. This study focuses on developing a digital tool for process monitoring which integrates the physical and digital space of the AFP process. The digital tool constitutes a machine learning model to predict the in-situ thermal history during AFP manufacturing. The predicted thermal history can be compared with the real-time in-situ temperatures during manufacturing to predict the quality of the layup. A GUI application is developed to provide benchmarking data for comparison with real-time temperatures during manufacturing enabling monitoring and predictive maintenance of the AFP process paving way for the development of a digital twin of the AFP composites manufacturing process.

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Section: Methodsmentioning

confidence: 99%

Machine-Learning based process monitoring of automated composites manufacturing to enable digital twin development

Mujtaba¹,

Islam

Kaeding³

et al. 2022

Preprint

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“…Such strategies are becoming increasingly critical across various industries, promising enhanced maintenance efficiency and cost savings. A significant illustration of this is in [95], which investigates ML applications for PdM in hydroelectric power plants, with a specific focus on turbine load cycle optimization. The study developed a predictive model using load cyclerelated variables and evaluated four ML algorithms, achieving an impressive accuracy rate of approximately 98% for maintenance forecasting, thus highlighting ML's potential in PdM for industrial applications, especially in hydroelectric power generation.…”

Section: Prognostics Powered By Ai For Pdmmentioning

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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“…Nowadays, along with the popularization of IoT devices, data-driven predictive maintenance studies have been conducted by researchers in many fields of the industry, including on wind turbines [15], photovoltaic cells [16], and electrical motors [17,18]. HVAC systems are also among the fields of interest for predictive maintenance.…”

Section: Related Workmentioning

confidence: 99%

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

Tian,

Gomez-Rosero,

Capretz

2023

Energies

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Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment. The adoption of Internet of Things (IoT) sensors integrated into HVAC systems has paved the way for predictive maintenance (PdM) grounded in real-time operational metrics. However, HVAC systems without such sensors cannot leverage the advantages of current data-driven PdM techniques. This work introduces a novel data-driven framework, the health prognostics classification with autoencoders (HPC-AE), designed specifically for PdM. It utilizes solely HVAC power consumption and outside temperature readings for its operations, both of which are readily obtainable. The primary objective of the HPC-AE framework is to facilitate PdM through a health prognostic approach. The HPC-AE framework utilizes an autoencoder for feature enrichment and then applies an artificial neural network to classify the daily health condition of an HVAC system. A multi-objective evaluation metric is employed to ensure optimal performance of the autoencoder within this framework. This metric evaluates the autoencoder’s proficiency in reducing reconstruction discrepancies in standard data conditions and its capability to differentiate between standard and degraded data scenarios. The HPC-AE framework is validated in two HVAC fault scenarios, including a clogged air filter and air duct leakage. The experimental results show that compared to methods used in similar studies, HPC-AE exhibits a 5.7% and 2.1% increase in the F1 score for the clogged air filter and duct leakage scenarios.

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A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case

Cited by 12 publications

References 62 publications

Machine-Learning based process monitoring of automated composites manufacturing to enable digital twin development

Machine-Learning based process monitoring of automated composites manufacturing to enable digital twin development

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

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

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