Improving Maintenance Data Quality: Application of Natural Language Processing to Asset Management

Payette, Mathieu; Abdul-Nour, Georges; Meango, Toualith Jean-Marc; Côté, Alain

doi:10.1007/978-3-031-25448-2_54

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also encompasses reliability, availability, maintainability, and safety (RAMS), which examines overall group characteristics. Unlike RAMS, PHM uses a detailed approach to monitor individual components [95]. Specifically, the reliability assessment of Li-ion batteries for • Provides high fidelity modelling of the aging phenomena of the battery cell [86].…”

Section: Reliability Assessment Techniques Of Li-ion Batterymentioning

confidence: 99%

“…Preliminaries: Machine Learning (ML) is a subset of artificial intelligence (AI) and refers to a specific approach where computers can learn and improve their performance on a task without being explicitly programmed for the task. Instead of following a fixed set of rules, machine learning algorithms learn patterns and insights from data, enabling them to make predictions, classify objects, or make decisions based on past experiences [95]. It is a branch of computer science that focuses on creating algorithms to derive valuable insights from the data.…”

Section: ) Machine Learning Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

View full text Add to dashboard Cite

Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research.INDEX TERMS Capacity fade, causes of failure, data-driven, degradation trajectory, electric vehicle, electrochemical impedance spectroscopy, failure modes mechanisms and effects analysis, incremental capacity and differential voltage analysis, Li-ion batteries, machine learning, model-based, power fade, qualitative analysis, quantitative analysis, remaining useful life, reliability, state of health, state of charge, transfer learning, X-ray computed tomography.

show abstract

Section: Reliability Assessment Techniques Of Li-ion Batterymentioning

confidence: 99%

Section: ) Machine Learning Techniquesmentioning

confidence: 99%

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…CamemBERT, a pre-trained transformer architecture, is used for French language processing. In [44], a classification model is developed based on a pre-trained model for reclassifying maintenance orders in the context of an electrical utility. Some reviews of the literature also present interesting perspectives on reliability.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Applications for Reliability Engineering: A Review

Payette

Abdul-Nour

2023

Sustainability

View full text Add to dashboard Cite

The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of artificial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of artificial intelligence (AI) modeling techniques combined with the various research topics increases the difficulty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.

show abstract

“…For this purpose, hypothesis testing and analysis of variance (ANOVA) are used for validation and parameter selection purposes, respectively. In [21], they use statistical metrics, particularly the confusion matrix, to evaluate the results of a text classification model. In [22], they used hypothesis testing (Pearson's Chi-square tests) to validate the choice and estimation of the parameters of a Weibull distribution.…”

Section: Statistical Modelsmentioning

confidence: 99%

Asset Management, Reliability and Prognostics Modeling Techniques

Payette

Abdul-Nour

2023

Sustainability

Self Cite

View full text Add to dashboard Cite

In recent years, reliability engineering has seen significant growth in data-driven modeling, mainly due to the democratization of sensing technologies, big data processing, and computing capabilities. It has also seen a paradigm shift, with Engineering of Asset Management (EAM) becoming widely accepted as a high-level framework to support corporate policies and strategies. The rapid evolution of research leads to the development of multiple research communities, making it difficult for the uninitiated to navigate the literature. Indeed, system reliability encompasses several research subfields that focus on maximizing the life cycle of assets, including Reliability, Availability, Maintainability, and Safety (RAMS), Prognostics and Health Management (PHM), and Engineering of Asset Management. This article proposes a review of these concepts with the aim of identifying the different scientific communities, what differentiates them, and what connects them. It also addresses RAMS and PHM modeling techniques and highlights the significance of these disciplines in ensuring the functioning of complex systems. In summary, this article aims to clarify the interrelationship between the topics of reliability engineering, to simplify the search and selection for modeling methods.

show abstract

Improving Maintenance Data Quality: Application of Natural Language Processing to Asset Management

Cited by 5 publications

References 8 publications

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Machine Learning Applications for Reliability Engineering: A Review

Asset Management, Reliability and Prognostics Modeling Techniques

Contact Info

Product

Resources

About