2024
DOI: 10.1108/ilt-04-2023-0086
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Interactive spatiotemporal LSTM approach for enhanced industrial fault diagnosis

Tan Zhang,
Zhanying Huang,
Ming Lu
et al.

Abstract: Purpose Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features. Design/methodology/approach The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotat… Show more

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