Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction

Xiang, Sheng; Qin, Yi; Zhu, Caichao; Wang, Yangyang; Chen, Haizhou

doi:10.1016/j.engappai.2020.103587

Cited by 110 publications

(33 citation statements)

References 23 publications

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“…The application of RNN architectures to fault prognosis have been explored on various industrial components such that lithium-ion-batteries (Zhang et al, 2018), gears (Xiang et al, 2020), fuel cells (Liu et al, 2019a), and on the C-MAPSS dataset (Yuan et al, 2016;Zheng et al, 2017;Wu et al, 2018a;Wu et al, 2018b;Chen et al, 2019;Elsheikh et al, 2019;Wu et al, 2020). One of the most popular RNN-based approaches proposed in the literature is the work of Wu et al (2018b).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…The authors combine LSTM layers with a feed-forward neural network, showing that the proposed approach provides better performances than ANNs, SVM and CNNs. In Xiang et al (2020), the attention mechanism is used to enhance the performances of an LSTM network on the prediction of the RUL on gears. The aforementioned model, named LSTMP-A, is trained with time-domain and frequency-domain features and its comparison with other recurrent models shows that it provides the best prediction accuracy.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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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: Recurrent Neural Networkmentioning

confidence: 99%

Section: Recurrent 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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“…In contrast, as one of the most popular variants of RNN, the LSTM neural network can avoid such drawbacks by using hidden memory. It extends RNN with three types gates: the input gate determining whether the current input should be stored, the forget gate controlling whether the historical information is forgotten of the cell memory, and the output gate determining the information that flows into node output [36].…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

“…With the special memory structure and gated designing, LSTM has a better ability to learn long-term dependency. The structure of LSTM can be described by the following equations [36,37]:…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

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Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

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Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

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Plastic gear remaining useful life prediction using artificial neural network

Kien

Iba

Tsutsui

et al. 2021

Forsch Ingenieurwes

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Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction

Cited by 110 publications

References 23 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

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Plastic gear remaining useful life prediction using artificial neural network

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