Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine

Berghout, Tarek; Mouss, Leïla Hayet; Kadri, Ouahab; Saïdi, Lotfi; Benbouzid, Mohamed

doi:10.1016/j.engappai.2020.103936

Cited by 57 publications

(18 citation statements)

References 22 publications

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“…Recent DL research works using the C-MAPSS dataset as a case study have focused on feature extraction to improve models' performance, supervised health state estimation, and optimal RMSE values. For instance, Berghout [46] used a denoising autoencoder as a feature extractor coupled with an update selection strategy to determine the training sequences used in an extreme learning machine (ELM) prognosis model. Here, only the FD001 subdataset was trained.…”

Section: Resultsmentioning

confidence: 99%

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Cofre-Martel

Droguett

Modarres

2021

Shock and Vibration

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Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

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Section: Resultsmentioning

confidence: 99%

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Cofre-Martel

Droguett

Modarres

2021

Shock and Vibration

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“…The choice of a method for HI extraction depends mainly on the type of data collected, and the considered application [1][2][3][4][5][18][19][20][21][22][23][24]. As highlighted in Figure 9, the HI can be found using signal-based and model-based methods.…”

Section: Prognostic Metricsmentioning

confidence: 99%

“…These data are subsequently used as a database of training dataset for prediction models based mainly on artificial intelligence, present and future states of the system, and thus provide a priori an estimate of the RUL with confidence bounds. Moreover, it is the most used and most developed approach, with research based on the use of neural networks and their variants, support vector machine [1][2][3][4][5][6][7][8][9][20][21][22][23][24], probabilistic methods (Bayesian networks, Markov models and their derivatives) [1,4,[31][32][33][34][35][36], stochastic models [21,33,35,[37][38][39][40][41][42][43], state and filtering models (Kalman filter and their variants, particle filter, etc.) [4,15,[43][44][45][46][47][48][49][50][51][52][53][54], regression tools (support ve...…”

Section: Data-driven Prognosismentioning

confidence: 99%

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

Saïdi

Benbouzid

2021

Electronics

Self Cite

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The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain

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“…Denoising has become such a common instrument that it has been integrated in most mathematical software. In the section concerning the data analysis of the Glymphatic system of a mouse brain we have, for example, we use the denoising autoencoder of MATLAB Berghout et al, 2020Berghout et al, , 2021. In particular, this modified denoise autoencoder (Bengio and LeCun, 2007) is based on a supervised ML algorithm which models noise based on a training set.…”

Section: Denoising Toolsmentioning

confidence: 99%

“…Figure 3 summarizes the analysis results for the light microscopy video of the living mouse brain tissue with a flow of the transparent lymph fluid through the capillaries of the glymphatic system. The capillaries of the glymphatic system are not directly visible either in the raw video data, see Figure 3A, or in the mean obtained by using a commercial deep learning denoising autoencoder from the ''Deep Learning Toolbox'' of MATLAB Berghout et al, 2020Berghout et al, , 2021, see Figure 3B. Application of the common GMM entropy (Zoran and Weiss, 2011;Bouman et al, 2018;The Event Horizon Telescope Collaboration, 2019a;Greggio et al, 2012) does allow to visualize only surface capillaries, see Figure 3C.…”

Section: Glymphatic System Of a Mouse Brainmentioning

confidence: 99%

A deeper look into natural sciences with physics-based and data-driven measures

et al. 2021

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Summary With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mouse brain microscopy data. What sets these techniques apart is the relaxation of restrictive assumptions typical of many machine learning models and instead incorporating aspects that best fit the dynamical systems at hand.

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Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine

Cited by 57 publications

References 22 publications

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

A deeper look into natural sciences with physics-based and data-driven measures

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