Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network

Kong, Ziqian; Jin, Xiaohang; Xu, Zhengguo; Zhang, Bin

doi:10.1109/tim.2022.3184352

Cited by 40 publications

(10 citation statements)

References 34 publications

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“…By replacing the original linear connection in GRU with GCN, Wang et al [167] developed a gated GCN for machinery prognosis of multi-source sensing signals and obtained its CI using quantile regression. In addition, some scholars also proposed a series of prediction methods that combined GNN and attention mechanism [168][169][170], further improving the RUL prediction performance. However, in RUL prediction, the data often consists of multiple time series signals from different sensors, and constructing an appropriate graph structure to represent the relationships between these signals can be non-trivial.…”

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.

View full text Add to dashboard Cite

show abstract

“…In addition, the attention mechanism as a member of DL, it can improve the ability to express features, emphasizing important features while ignoring unimportant features [10]. Kong et al [11] proposed spatio-temporal fusion attention, but it is only applicable to graph networks and requires a priori knowledge of the device. Li et al [12] proposed a multi-sensor channel temporal attention, which can measure the importance between different sensor datasets, but is not suitable for single sensor datasets.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Zhu,

Lyu,

Gao

et al. 2024

Meas. Sci. Technol.

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The maintenance costs, productivity, health, and safety of mechanical equipment all heavily rely on the remaining usable life (RUL) of the bearings. Bearing datasets from running to failure are often in the form of life cycle sequences, which contain information about potential degradation failures in both the long and short term. Recently, the Transformer has been widely used in the RUL field due to its ability to capture some of the degradation information of the bearing. However, the Transformer is weak in acquiring local information and fails to extract temporal features from the degradation process. To solve the above problems, this paper proposes a spatio-temporal convolutional Transformer (STCT) model, which mainly consists of the dual convolutional spatio-temporal network (DCSTN) and multi-scale Transformer (MST). It not only captures the degradation features of the bearings from the temporal and spatial perspectives but also enhances the ability of the Transformer to acquire local information. We propose DCSTN as a feature extraction module, and the proposed spatio-temporal attention can capture the relevant degradation state features at different moments. In addition, MST uses a new module of multi-scale dilated causal convolution combined with multi-head attention to realize the combination of global degradation information and local contextual information capturing ability. We demonstrate the effectiveness and sophistication of the STCT model by conducting comparative experiments with ablation experiments on publicly available datasets.

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“…Data-driven methods can be further divided into shallow machine learning-based methods and deep learning-based methods. Methods based on shallow machine learning include statistical regression analysis [9], support vector machines [10,11], neural networks [12][13][14], and so on. However, the hierarchical structure of shallow machine learning-based methods is relatively simple, which limits the model's ability to extract deep-level structures and abstract features from the data.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

Cao,

Zhang,

Zhao

et al. 2024

Applied Sciences

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For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

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Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network

Cited by 40 publications

References 34 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

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

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