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Fault information identification method of industrial production equipment based on industrial internet of things

Yang Yi

International Journal of Manufacturing Technology and Management2026https://doi.org/10.1504/ijmtm.2026.151516article
AJG 1
Weight
0.50

What the paper says

In order to solve the problems of low recognition accuracy and long recognition time existing in the existing fault information recognition methods for industrial production equipment, this paper proposes a fault information recognition method for industrial production equipment based on the industrial internet of things. First, based on the industrial internet of things technology, build an information collection platform for industrial production equipment. Then, based on wavelet coefficients, the equipment signal is pre-processed and the fault features of industrial production equipment are extracted based on sparse expression. Finally, a least squares support vector model is constructed to classify fault signals and achieve recognition of industrial production equipment fault information. Through experiments, it can be seen that the accuracy of using the method proposed in this article for recognition is always above 96%, and the recognition time is always within 7.50 s, which has good recognition effect and efficiency.

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https://doi.org/https://doi.org/10.1504/ijmtm.2026.151516

Or copy a formatted citation

@article{yang2026,
  title        = {{Fault information identification method of industrial production equipment based on industrial internet of things}},
  author       = {Yang Yi},
  journal      = {International Journal of Manufacturing Technology and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijmtm.2026.151516},
}

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Fault information identification method of industrial production equipment based on industrial internet of things

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.