Remaining Useful Life estimation for noisy degradation trends

Nguyen, Thi-Bich-Lien; Djeziri, Mohand; Ananou, Bouchra; Ouladsine, Mustapha; Pinaton, Jacques

doi:10.1016/j.ifacol.2015.09.509

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Clearly, the prediction of the RUL is the core to accomplish successful PdM. Following statistical probability distributions [22,[34][35][36][37], conventional burn-in tests derive the actual RUL at a normal condition given an object is operated in a rigorous condition like high temperature. However, it is difficult to justify its validity because we cannot wait for the equipment or devices to really fail.…”

Section: Discussion and Insightsmentioning

confidence: 99%

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Wang

Liu

2022

Mathematical Problems in Engineering

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The prediction of remaining useful life (RUL) is a critical issue in many areas, such as aircrafts, ships, automobile, and facility equipment. Although numerous methods have been presented to address this issue, most of them do not consider the impacts of feature engineering. Typical techniques include the wrapper approach (using metaheuristics), the embedded approach (using machine learning), and the extraction approach (using component analysis). For simplicity, this research considers feature selection and feature extraction. In particular, principal component analysis (PCA) and sliced inverse regression (SIR) are adopted in feature extraction while stepwise regression (SR), multivariate adaptive regression splines (MARS), random forest (RF), and extreme gradient boosting (XGB) are used in feature selection. In feature selection, the original 15 sensors can be reduced to only four sensors that accumulate more than 80% degrees of importance and not seriously decrease the predictive performances. In feature extraction, only the top three principal components can account for more than 80% variances of original 15 sensors. Further, PCA combined with RF is more recommended than PCA and CNN (convolutional neural network) because it can achieve satisfactory performances without incurring tedious computation.

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Section: Discussion and Insightsmentioning

confidence: 99%

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Wang

Liu

2022

Mathematical Problems in Engineering

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“…However, the R2F data are generally limited as the equipment is not allowed to be used until failure for finance and safety issues. Besides, these two types of indicators are very noisy in the real world applications due to the measurement process and environments [6]. Hence, to overcome these issues, we propose in this paper a RUL prediction method.…”

Section: Introductionmentioning

confidence: 99%

“…First, the indicators are pre-processed by the 'percentile filter' [6]. This filter generates a set of monotonic profiles from the indicator conserving thus its tendency and isolating the useful information from noises.…”

Section: Introductionmentioning

confidence: 99%

Similarity-based Brownian Motion Approach for Remaining Useful Life Prediction

Jamal

Ananou

Graton

et al. 2021

2021 International Conference on Control, Automation and Diagnosis (ICCAD)

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This paper proposes a data-driven framework for Remaining Useful Life (RUL) prediction of an operating equipment unit in the case of noisy and limited data. It consists of introducing the Brownian Motion model (BM) in the similarity framework and computing the RUL based on a collection of generated HIs (Health Indicators). For that, the percentile filter is used to pre-process the HIs by generating a collection of profiles from the operating equipment unit's HI and a set of references from a given R2F (Run-to-Failure) indicator. Then, the similarity is computed between each profile of this collection and the references in order to pick the most similar reference to each profile for modeling and RUL prediction. The final RUL is calculated as a weighted aggregation of the obtained RULs of the profile collection. A numerical application using simulated data illustrates the accuracy of this approach.

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A survey of the state of condition-based maintenance (CBM) in the nuclear power industry

Ayo-Imoru

Cilliers

2018

Annals of Nuclear Energy

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Remaining Useful Life estimation for noisy degradation trends

Cited by 7 publications

References 11 publications

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Similarity-based Brownian Motion Approach for Remaining Useful Life Prediction

A survey of the state of condition-based maintenance (CBM) in the nuclear power industry

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