Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems

Villalón-Falcón, Adriana; Prieto-Moreno, Alberto; Quiñones-Grueiro, Marcos; Llanes‐Santiago, Orestes

doi:10.1007/s40314-021-01752-8

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Machine Learning (ML) is a popular data-driven approach that has been extensively used in predicting the RUL of rotating machines. Several studies, including [11][12][13][14], have employed well-known ML models such as Linear Regression (LR), Random Forest (RF), and Support Vector Machines (SVM) to forecast RUL. However, these methods have some significant drawbacks, such as suboptimal performance due to inflexible mathematical formulas and time-consuming computations for big input data.…”

Section: Introductionmentioning

confidence: 99%

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Tran,

Vu,

Pham

et al. 2024

Mathematics

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Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring condition monitoring data, particularly vibration signals, for RUL estimation. To address these challenges, this research presents a novel Robust Multi-Branch Deep Learning (Robust-MBDL) model. Robust-MBDL stands out by leveraging diverse data sources, including raw vibration signals, time–frequency representations, and multiple feature domains. To achieve this, it adopts a specialized three-branch architecture inspired by efficient network designs. The model seamlessly integrates information from these branches using an advanced attention-based Bi-LSTM network. Furthermore, recognizing the importance of data quality, Robust-MBDL incorporates an unsupervised LSTM-Autoencoder for noise reduction in raw vibration data. This comprehensive approach not only overcomes the limitations of existing methods but also leads to superior performance. Experimental evaluations on benchmark datasets such as XJTU-SY and PRONOSTIA showcase Robust-MBDL’s efficacy, particularly in rotating machine health prognostics. These results underscore its potential for real-world applications, heralding a new era in predictive maintenance practices.

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Section: Introductionmentioning

confidence: 99%

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Tran,

Vu,

Pham

et al. 2024

Mathematics

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“…Third, how should dynamic models and data effectively be integrated to ensure the interpretability and generalization of the prediction model? In view of the above three challenges, national and international scholars have carried out research on the RUL prediction of rotary motion mechanisms based on degradation mechanisms [23], deep learning [6,24], fusion methods [25], etc. However, they have encountered difficulties in exploring the RUL of reciprocating motion mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Summarization of Remaining Life Prediction Methods for Special Power Plants

Liang,

Li,

Zhao

et al. 2023

Applied Sciences

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With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

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A Proposal of Training Data Selection for Remaining Job Cycle Time Prediction in Industrial Monitoring: A Real Case Study

García-Celis,

Villalón-Falcón,

Piñero-Aguilar

et al. 2023

Studies in Systems, Decision and Control

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Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems

Cited by 4 publications

References 32 publications

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Summarization of Remaining Life Prediction Methods for Special Power Plants

A Proposal of Training Data Selection for Remaining Job Cycle Time Prediction in Industrial Monitoring: A Real Case Study

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