Enhanced Online Sequential Parallel Extreme Learning Machine and its Application in Remaining Useful Life Prediction of Integrated Modular Avionics

Gao, Zehai; Ma, Cunbao; Zhang, Jianfeng; Xu, Guolei

doi:10.1109/access.2019.2960406

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…Algorithms were treated to be adaptive learners able to fit data changes in a sequential way. In [81], an enhanced OSELM architecture was proposed for the RUL prediction of integrated modular avionic systems. The neural network was therefore reinforced with a robust denoising AE to be able to learn efficient representations from data.…”

Section: Elmmentioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

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Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

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Section: Elmmentioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

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“…More details can be found in [16]. Recent applications of Machine learning models in reliability engineering include methodology development, system diagnostic, remaining useful life estimation and prognostic health management [17][18][19][20][21]. Unsupervised learning consists in examining datasets with only input variables or features, and no labels or response variable.…”

Section: B Machine Learning For Anomaly Detectionmentioning

confidence: 99%