| 1. |
- Zhang, Weiting, et al.
(author)
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AESGRU: An Attention-based Temporal Correlation Approach for End-to-End Machine Health Perception
- 2019
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In: IEEE Access. - 2169-3536. ; 7, s. 141487-141497
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Journal article (peer-reviewed)abstract
- Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.
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| 2. |
- Zhang, Weiting, et al.
(author)
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DeepHealth : A Self-Attention Based Method for Instant Intelligent Predictive Maintenance in Industrial Internet of Things
- 2021
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In: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 17:8, s. 5461-5473
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Journal article (peer-reviewed)abstract
- With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based "Perception and Prediction" framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.
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| 3. |
- Zhang, Hongguo, et al.
(author)
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Rational design of porous Fex-N@MOF as a highly efficient catalyst for oxygen reduction over a wide pH range
- 2023
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In: Journal of Alloys and Compounds. - : ELSEVIER SCIENCE SA. - 0925-8388 .- 1873-4669. ; 944
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Journal article (peer-reviewed)abstract
- The oxygen reduction reaction (ORR) kinetics are well known to strongly rely on the activives of electro-catalysts. Herein, a Fe-N-doped porous carbon-based electrocatalyst combined with zinc (Zn)-based metal-organic frameworks (MOFs) (Fex-N@MOF) was designed and successfully fabricated via a facile process combined immersion doping and pyrolysis. By controlling the formation of Fe3C, the physical structure of porous carbon was significantly altered, and the active chemical sites of Fe species can be formed to catalyze ORR. The uniform N-doped three-dimensional interpenetrating network structure yielded a high surface area. Both Fe3C and Fe-Nx could offer an abundance of active sites and thus promoted Fe0.05-N@MOF to exhibit high ORR activity in alkaline, neutral and acid electrolytes. Fe0.05-N@MOF showed extraordinary stability and methanol tolerance under a varied pH range conditions, it could be applied as cathode elec-trocatalyst in different fuel cells such as Zn-air fuel cell (ZFC), microbial fuel cells (MFCs), as well as direct methanol fuel cell (DMFC). Fe0.05-N@MOF is a promising material to replace Pt-based electrocatalysts as non-precious metal catalysts.(c) 2023 Elsevier B.V. All rights reserved.
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| 4. |
- Zhang, Weiting, et al.
(author)
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CarNet : A Dual Correlation Method for Health Perception of Rotating Machinery
- 2019
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In: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 19:16, s. 7095-7106
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Journal article (peer-reviewed)abstract
- As a key component of rotating machinery, the health perception of hearings is essential to ensure the safe and reliable operation of industrial equipment. In recent years, research on equipment health perception based on data-driven methods has received extensive attention. Overall, most studies focus on several public datasets to verify the effectiveness of their algorithms. However, the scale of these datasets cannot completely satisfy the representation learning of deep models. Therefore, this paper proposes a novel method, called CarNet, to obtain a more robust model and ensure that the model is sufficiently trained on a limited dataset. Specifically, it is composed of a data augmentation method named equitable sliding stride segmentation (ESSS) and a hybrid-stacked deep model (HSDM). The ESSS not only amplifies the scale of the original dataset but also enables newly generated data with both spatial and temporal correlations. The HSDM can, therefore, extract shallow spatial features and deep temporal information from the strongly correlated 2-dimensional (2-D) sensor array using a CNN and a bi-GRU, respectively. Moreover, the integrated attention mechanism contributes to focusing limited resources on informative areas. The effectiveness of CarNet is evaluated on the CWRU dataset, and an optimal diagnostic accuracy of 99.92% is achieved.
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