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CarNet: A Dual Correlation Method for Health Perception of Rotating Machinery
Beijing Jiaotong Univ, Beijing, Peoples R China.ORCID iD: 0000-0002-7473-2234
Beijing Jiaotong Univ, Beijing, Peoples R China.
Beijing Jiaotong Univ, Beijing, Peoples R China.
Beijing Sheenline Technol, Beijing, Peoples R China.
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2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 16, p. 7095-7106, article id 8695784Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 19, no 16, p. 7095-7106, article id 8695784
Keywords [en]
Health perception, convolutional neural network, gated recurrent unit, attention mechanism, temporal and spatial correlation
Identifiers
URN: urn:nbn:se:miun:diva-36821DOI: 10.1109/JSEN.2019.2912934ISI: 000476795500059Scopus ID: 2-s2.0-85069780589OAI: oai:DiVA.org:miun-36821DiVA, id: diva2:1341847
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-13Bibliographically approved

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Gidlund, Mikael

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