A Prediction Method for the RUL of Equipment for Missing Data

Chen, Wenbai; Liu, Chang; Chen, Weizhao; Liu, Huixiang; Chen, Qili; Wu, Peiliang

doi:10.1155/2021/2122655

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…Therefore, the min-max normalization method is used to unify the data into the range [−1,1]. Each measurement x i,j is min-max normalized and can be expressed as [17]:…”

Section: Data Processingmentioning

confidence: 99%

A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

Chen

Liu

et al. 2022

Mathematics

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To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions.

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“…Therefore, the min-max normalization method is used to unify the data into the range [−1,1]. Each measurement x i,j is min-max normalized and can be expressed as [17]:…”

Section: Data Processingmentioning

confidence: 99%

A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

Chen

Liu

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

A Missing Data Imputation Method for Spatio-temporal Decoupled Remaining Useful Life Prediction of CNC Machines

Chen,

Niu,

Xiao

et al. 2024

Communications in Computer and Information Science

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Small data challenges for intelligent prognostics and health management: a review

Li,

Feng

et al. 2024

Artif Intell Rev

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Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

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A Prediction Method for the RUL of Equipment for Missing Data

Cited by 6 publications

References 21 publications

A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

A Missing Data Imputation Method for Spatio-temporal Decoupled Remaining Useful Life Prediction of CNC Machines

Small data challenges for intelligent prognostics and health management: a review

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