An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction

Li, Zhixiong; Wu, Dazhong; Hu, Chao; Terpenny, Janis P.

doi:10.1016/j.ress.2017.12.016

Cited by 115 publications

(57 citation statements)

References 28 publications

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“…The presented solutions, methods, and approaches can be improved and used in the future. Moreover, mechanical engineering is essential for fault diagnosis of machines [10][11][12][13][14][15][16][17][18][19][20][21][22] and the analysis of temperature [23][24][25]. The mechanical properties of materials are also investigated in the literature [26][27][28].…”

Section: The Contentmentioning

confidence: 99%

Introduction to Special Issue on Symmetry in Mechanical Engineering

Królczyk

Legutko

et al. 2020

Symmetry

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Section: The Contentmentioning

confidence: 99%

Introduction to Special Issue on Symmetry in Mechanical Engineering

Królczyk

Legutko

et al. 2020

Symmetry

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“…Ensemble learning methods are meta-algorithms that combine multiple base learners into a single predictive model in order to improve prediction performance [3]. Ensemble learning methods are classified into two categories: parallel and sequential ensemble methods.…”

Section: Ensemble Learning-based Predictive Modelingmentioning

confidence: 99%

“…Predictive maintenance requires health monitoring systems and predictive modeling technologies. The existing literature pertaining to RUL prediction for aircraft engines can be classified into two categories: model-based and data-driven prognostics [3][4][5]. Model-based prognostic methods describe system behavior and system degradation using physics-based models typically in combination with state estimators such as the Kalman filter, the particle filter, and the hidden Markov model [6].…”

Section: Introductionmentioning

confidence: 99%

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Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

Goebel

2018

Journal of Engineering for Gas Turbines and Power

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Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature.

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“…In addition to these approaches which use the raw input, a Vanilla LSTM neural network model has been used in [26] for RUL prediction, where a dynamic differential technology was proposed to extract inter-frame information and achieved high prediction accuracy. Moreover, an ensemble learning-based prognostic method is proposed in [27], which combines prediction results from multiple learning algorithms to get better performance. However, all these modern deep learning approaches for RUL prediction have a major limitation: they all require the availability of a large amount of training data for training these deep neural networks, while in real-world applications, it is often impossible to obtain a large number of failure progression samples.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

et al. 2018

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Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

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An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction

Cited by 115 publications

References 28 publications

Introduction to Special Issue on Symmetry in Mechanical Engineering

Introduction to Special Issue on Symmetry in Mechanical Engineering

Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

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