Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review

Bhat, Darshankumar; Muench, Stefan; Roellig, M.

doi:10.1016/j.prime.2023.100166

Cited by 18 publications

(5 citation statements)

References 50 publications

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“…Physics model-based strategies refer to techniques grounded solely in the mathematical representation of a system and its failure modes. These methods encompass physics of failure (PoF) and system modeling [2]. PoF focuses on understanding failure patterns influenced by life cycle loading conditions, considering the geometry and material properties of the target component to analyze potential failure modes and evaluate RUL.…”

Section: Prognostics Rul Estimationmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

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Section: Prognostics Rul Estimationmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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“…The ability to generate cost-effective data and analyze them finds application in the manufacturing industry [12]. By constantly monitoring systems, a vast amount of data can be gathered and analyzed, opening up a new area of exploration [13]. The study offers a comprehensive overview of the use of ML algorithms in the field of electronics and highlights the state-of-the-art advancements in various electronic components.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Prediction Model for Analysis of Sensor Data

Yotov,

Aleksieva-Petrova

2024

Electronics

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In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset.

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“…ML algorithms have proven their performance and utility in a variety of fields, such as speech recognition, text mining, medicine, data analysis, aeronautics, data analysis, stock market analysis, and many others [52,53]. This wide range of applications is possible due to a variety of existing algorithms which are presented in Figure 1 based on [51][52][53][54][55] (the graph does not exhaust all currently used algorithms). Sarker [52] has divided ML algorithms into four groups, including supervised learning (algorithms: classification and regression), unsupervised learning (clustering), semisupervised learning (classification and clustering, based on labelled and unlabeled data) and reinforcement learning (positive and negative).…”

Section: Machine Learning Algorithms-an Overviewmentioning

confidence: 99%

“…As Pugliese et al explains in [54], the popularity of reinforcement algorithms (algorithms based on interactions with the environment) reflects their use to solve realworld problems in a variety of fields, such as game theory, control theory, operation analysis, information theory, simulation-based optimization, manufacturing, supply chain logistics, swarm intelligence, aircraft control, robot motion control, laparoscopic surgery, traffic forecasting service, smart cities development, etc. [55]. Oxidation of carbon in MgO-C refractories, especially below 1400 • C, is one of the main problems in the application of these materials [56].…”

Section: Machine Learning Algorithms-an Overviewmentioning

confidence: 99%

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

Sado,

Jastrzębska,

Zelik

et al. 2023

Materials

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Nowadays, digitalization and automation in both industrial and research activities are driving forces of innovations. In recent years, machine learning (ML) techniques have been widely applied in these areas. A paramount direction in the application of ML models is the prediction of the material service time in heating devices. The results of ML algorithms are easy to interpret and can significantly shorten the time required for research and decision-making, substituting the trial-and-error approach and allowing for more sustainable processes. This work presents the state of the art in the application of machine learning for the investigation of MgO-C refractories, which are materials mainly consumed by the steel industry. Firstly, ML algorithms are presented, with an emphasis on the most commonly used ones in refractories engineering. Then, we reveal the application of ML in laboratory and industrial-scale investigations of MgO-C refractories. The first group reveals the implementation of ML techniques in the prediction of the most critical properties of MgO-C, including oxidation resistance, optimization of the C content, corrosion resistance, and thermomechanical properties. For the second group, ML was shown to be mostly utilized for the prediction of the service time of refractories. The work is summarized by indicating the opportunities and limitations of ML in the refractories engineering field. Above all, reliable models require an appropriate amount of high-quality data, which is the greatest current challenge and a call to the industry for data sharing, which will be reimbursed over the longer lifetimes of devices.

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Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review

Cited by 18 publications

References 50 publications

Challenges on prognostics and health management for wind turbine components

Challenges on prognostics and health management for wind turbine components

Data-Driven Prediction Model for Analysis of Sensor Data

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

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