Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture

Yang, Boyuan; Liu, Ruonan; Zio, Enrico

doi:10.1109/tie.2019.2924605

Cited by 275 publications

(94 citation statements)

References 30 publications

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“…CNN architectures have been extensively explored also for fault prognosis. These methods have been mainly applied to opensource evaluation platforms such as the popular NASA's C-MAPSS dataset (Saxena and Goebel, 2008) for aero-engine unit prognostics (Babu et al, 2016;Li et al, 2018a;Li et al, 2018bWen et al, 2019a and the PRONOSTIA dataset (Ali et al, 2015) for bearings health assessment (Ren et al, 2018a;Zhu et al, 2018;Li et al, 2019c;Wang et al, 2019b;Yang et al, 2019).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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“…Due to its excellent self-learning function, it has also been introduced into the RUL prediction by many researchers, one of the most is the deep learning methods. A double convolutional neural network architecture is presented to predicted RUL in literature [22]. This method does not need any feature extractor, only needs to input the original vibration signal, and can predict RUL with high accuracy.…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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“…Zhang et al [ 17 ] adopted a fully convolutional neural network for feature self-learning and reduced training parameters; a weighted average method was used to denoise the prediction results, and the bearing-accelerated life experiment verified the effectiveness of the proposed method. Yang et al [ 18 ] proposed an intelligent RUL prediction method based on the dual CNN model architecture to predict the turbofan engine RUL, the first CNN model determines the initial failure point, and the second CNN model is used for RUL prediction. This method does not require any feature extractor.…”

Section: Introductionmentioning

confidence: 99%

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Cheng

Chen

et al. 2021

Sensors

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture

Cited by 275 publications

References 30 publications

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

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