Journal ArticleDOI
Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm
TLDR
An enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine.About:
This article is published in Aerospace Science and Technology.The article was published on 2019-01-01. It has received 65 citations till now. The article focuses on the topics: Extreme learning machine & Stability (learning theory).read more
Citations
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Journal ArticleDOI
Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics
Juan José Montero Jimenez,Juan José Montero Jimenez,Sébastien Schwartz,Sébastien Schwartz,Rob A. Vingerhoeds,Bernard Grabot,Michel Salaün +6 more
TL;DR: This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.
Journal ArticleDOI
Crack detection using fusion features-based broad learning system and image processing
TL;DR: Compared with some well‐known deep convolutional neural networks, the FF‐BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times, and this substantially reduces the training cost.
Journal ArticleDOI
Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine
Tarek Berghout,Leila Hayet Mouss,O. Kadri,Lotfi Saidi,Lotfi Saidi,Mohamed Benbouzid,Mohamed Benbouzid +6 more
TL;DR: A new Denoising Online Sequential Extreme Learning Machine with double dynamic forgetting factors (DDFF) and Updated Selection Strategy (USS) is proposed, which proves the effectiveness of the new integrated robust feature extraction scheme by showing more stability of the network responses even under random solutions.
Journal ArticleDOI
A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current
Tingting Xu,Zhen Peng,Lifeng Wu +2 more
TL;DR: Experimental results show that the minimum battery capacity RMSE predicted is 1.0294, and the cycle capacity error is mostly within the range of -3mAh∼3mAh, which proves that the method can more accurately estimate the capacity of lithium-ion batteries under RVC conditions.
Journal ArticleDOI
Hybrid MultiGene Genetic Programming - Artificial neural networks approach for dynamic performance prediction of an aeroengine
TL;DR: Different machine learning techniques were used to estimate and predict an engine parameter; the Exhaust Gas Temperature (EGT) has been chosen as the key parameter and a MultiGene Genetic Programming (MGGP) technique has been used to derive simple mathematical relationships between different input parameters and the EGT.
References
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Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
TL;DR: The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance on benchmark problems drawn from the regression, classification and time series prediction areas.
Proceedings ArticleDOI
Damage propagation modeling for aircraft engine run-to-failure simulation
TL;DR: In this article, the authors describe how damage propagation can be modeled within the modules of aircraft gas turbine engines and generate response surfaces of all sensors via a thermo-dynamical simulation model.
Journal ArticleDOI
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Olivier Janssens,Viktor Slavkovikj,Bram Vervisch,Kurt Stockman,Mia Loccufier,Steven Verstockt,Rik Van de Walle,Sofie Van Hoecke +7 more
TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.
Proceedings ArticleDOI
A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems
TL;DR: This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated.
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