A Novel Cap-LSTM Model for Remaining Useful Life Prediction

Zhao, Chengying; Huang, Xianzhen; Li, Yuxiong; Li, Shangjie

doi:10.1109/jsen.2021.3109623

Cited by 37 publications

(11 citation statements)

References 29 publications

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“…By incorporating gated attention mechanism and Bayesian layer, Zhao et al [204] further improved the prediction performance of CapsNet and quantified the uncertainty. In addition, [205][206][207] also combined CapsNet with LSTM to enhance the ability to capture long-term dependencies between features.…”

Section: Cutting-edge Methods In DLmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

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Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial Internet of Things (IoT) and artificial intelligence (AI) technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter.

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Section: Cutting-edge Methods In DLmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

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“…Similar to CNN, CapsNet is also composed of multiple nonlinear layers, including a primary capsule layer and a digit capsule layer. [30][31][32] However, in CapsNet, vector neurons (which look like capsules) perform network operations instead of scalar neurons. The vector of each dimension represents a feature of a single object, while the length of the vector represents the probability that the feature exists.…”

Section: Capsule Layersmentioning

confidence: 99%

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Sun

Zhao

Huang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In industrial production, effectively predicting the remaining useful life (RUL) of cutting tools can avoid overuse or underuse, which is of great significance for ensuring the processing quality of products and reducing enterprises’ production costs. This paper proposes a new method for RUL prediction of cutting tools based on robust empirical mode decomposition (REMD) and capsule bidirectional long short-term memory (Capsule-BiLSTM) network to improve accuracy. On one hand, new state features are extracted using REMD as the input of the deep learning network. On the other hand, a Capsule-BiLSTM network structure is designed to achieve RUL prediction of cutting tools by connecting the four layers. Finally, the effectiveness of the proposed method is verified by a series of cutting tool life tests. Comparison with some mainstream methods indicates that the proposed method has more advantages in RUL prediction of cutting tools with the average accuracy reaching up to 93.97%.

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“…Figure 5 shows the distribution of degradation measurements for the sensors as input. The indices are 2, 3,4,7,8,9,11,12,13,14,15,17,20 and 21.…”

Section: =mentioning

confidence: 99%

“…To validate the superiority of the proposed method, the performance of the NT-TCN model is compared to other state-of-the-art models. In this part, some classic and latest methods including CNN [3], LSTM [9], DCNN [5], BiLSTM [15], MS-DCNN [7], ALSTM [18], AGCNN [1], Cap-LSTM [17], BiGRU-MMoE [13], MLSA-TCN [24] and our proposed NT-TCN are considered for comparison. Table 6 shows the results.…”

Section: Comparisons With Other Approachesmentioning

confidence: 99%

“…Jiang et al [16] incorporated the preprocessed data into MSCNN and BiLSTM, respectively, and the extracted features of the two networks were concatenated for RUL prediction. To extract the highly correlated spatial feature information from the multivariate time-series sensor data, Zhao et al [17] presented a two-channel hybrid model combining capsule neural network with LSTM (Cap-LSTM) for RUL prediction. In order to obtain more favorable information for the predicted outcome, attention mechanisms are applied to RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

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Remaining useful life prediction combining temporal convolutional network with nonlinear target function

Liu¹,

Xu²,

Sun³

et al. 2022

Meas. Sci. Technol.

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Remaining useful life prediction based on degraded data is the premise of intelligent maintenance of equipment. Since the degradation process is usually complex and changeable, the general deep learning prediction method brings large prediction deviation since using linear target function. In this paper, the temporal convolutional network combined with nonlinear target function (NT-TCN) is proposed to improve the prediction accuracy. The nonlinear target function is constructed by piecewise function to label degraded data. By means of dilated causal convolution and Mish activation function, the NT-TCN learn the time series characteristics of degeneration, thus high precision of life prediction is achieved. By analyzing NASA's C-MAPSS dataset, the prediction results show that the NT-TCN model has lower root mean squared error and evaluation score than the neural network model with the linear target function, it is also superior to some existing prediction methods.

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A Novel Cap-LSTM Model for Remaining Useful Life Prediction

Cited by 37 publications

References 29 publications

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Remaining useful life prediction combining temporal convolutional network with nonlinear target function

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