Regularized Length Changeable Extreme Learning Machine with Incremental Learning Enhancements for Remaining Useful Life Prediction of Aircraft Engines

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.

show abstract

“…Studied Types of ML Models [26] LSTM [27] LSTM [32] CNN [28] CNN and LSTM [29] LSTM [33] CNN [34] OSELM [35] GRU [31] LSTM…”

Section: Referencementioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Unlike deep learning algorithms, conventional ML paradigm does not focus much more on learning from representations than on universal approximation [7]. Their main objective is to achieve greater accuracy by producing the best loss error.…”

Section: A Conventional Machine Learning Modelsmentioning

confidence: 99%

Machine Learning for Photovoltaic Systems Condition Monitoring: A Review

Berghout

Benbouzid²,

et al. 2021

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

Self Cite

View full text Add to dashboard Cite

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

show abstract