Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory

Wang, Hang; Peng, Minjun; Zhang, Miao; Liu, Yong-kuo; Ayodeji, Abiodun; Chen, Hao

doi:10.1016/j.isatra.2020.08.031

Cited by 64 publications

(19 citation statements)

References 30 publications

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“…Figure 7 shows the illustration of the RUL prediction . The DL technologies with strong feature extraction capability are more and more widely used in RUL prediction of key components in the nuclear industry [96,111,112] . Key Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin, China studied the RUL prediction of electric valves in NPPs based on DL models [96,111,112] .…”

Section: Computer Vision In Nuclear Industrymentioning

confidence: 99%

“…The DL technologies with strong feature extraction capability are more and more widely used in RUL prediction of key components in the nuclear industry [96,111,112] . Key Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin, China studied the RUL prediction of electric valves in NPPs based on DL models [96,111,112] . The aging and degradation of electric valves are mostly caused by the uneven lattice of valve body, uneven fluid impact, fluid corrosion effect, and radioactive material irradiation [97] .…”

Section: Computer Vision In Nuclear Industrymentioning

confidence: 99%

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Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy.Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

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Section: Computer Vision In Nuclear Industrymentioning

confidence: 99%

Section: Computer Vision In Nuclear Industrymentioning

confidence: 99%

Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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“…In the problem of predicting the remaining life of mechanical equipment based on a supervised learning model, adding labels to the data is equivalent to modeling the degradation state of the equipment. At present, there are two main ways to add labels [38][39][40][41][42]. One is to use a linear function, as shown in Figure 12a.…”

Section: Prediction Of the Rul Based On 1d-cnnmentioning

confidence: 99%

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

et al. 2021

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Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

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“…The residual convolution described in Research Object is improved by concatenating the results of the convolution filter. Moreover, the leaky ReLU activation function and sparse dropout operation are also used instead of ReLU or Sigmoid activation function based on the previous impressive performance of the Leaky ReLU activation function on nonlinear sequential datasets (Wang et al, 2020b).…”

Section: Model Architecture and Implementation Flowchartmentioning

confidence: 99%

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

Wang

Peng

et al. 2020

Front. Energy Res.

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Proper risk assessment and monitoring of critical component is crucial to the safe operation of Nuclear Power Plants. One of the ways to ensure real-time monitoring is the development of Prognostics and Health Management systems for safety-critical equipment. Recently, the remaining useful life prediction (RUL) has been found to be important in ensuring predictive maintenance and avoiding critical component failure. With the development of artificial intelligent techniques, deep learning algorithms are becoming popular for RUL prediction. Consequently, this paper presents RUL prediction techniques for nuclear plant electric gate valves with a temporal convolution network (TCN). The main advantage of using TCN is its ability to capture and process useful information in short-term sensor measurement changes. Moreover, the efficiency of the proposed TCN is enhanced by incorporating a convolution auto-encoder as a preprocessing layer in its structure, which greatly improved the residual convolution mode. The proposed method is verified on the electric gate valves experimental dataset that represents the real-world operation of the valve, and the result obtained is compared with other conventional data-driven approaches. The evaluation result shows impressive performance of the proposed model in predicting the remaining service life of the gate valves used in the nuclear reactor control system. Moreover, the generalization of the proposed model is evaluated on the turbofan engine benchmark dataset. The evaluation result also shows improved performance in the predicted RUL. Broader application of the proposed TCN is envisaged for critical components in other industries.

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Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory

Cited by 64 publications

References 30 publications

Deep learning in nuclear industry: A survey

Deep learning in nuclear industry: A survey

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

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