A method for predicting the remaining life of mechanical equipment in production lines based on similarity features

Xiangyang Mei et al.

International Journal of Manufacturing Technology and Management2026https://doi.org/10.1504/ijmtm.2026.151523article
AJG 1
Weight
0.50

What the paper says

In order to shorten the time required for predicting the remaining life of mechanical equipment and reduce prediction errors, this paper proposes a method for predicting the remaining life of mechanical equipment in production lines based on similarity features. Firstly, obtain the vibration signals of mechanical equipment in the production line and extract signal features; then, calculate the similarity characteristics between the degradation indicators of mechanical equipment. Finally, the DTW method is used to measure the similarity of the overall lifespan of mechanical equipment, and the final remaining lifespan of the predicted samples is calculated based on the actual remaining lifespan of each reference sample and corresponding weights, achieving residual lifespan prediction. The results show that the prediction time of our method is only four seconds, and the prediction error does not exceed 8.33%, which verifies the effectiveness of our method.

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https://doi.org/https://doi.org/10.1504/ijmtm.2026.151523

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@article{xiangyang2026,
  title        = {{A method for predicting the remaining life of mechanical equipment in production lines based on similarity features}},
  author       = {Xiangyang Mei et al.},
  journal      = {International Journal of Manufacturing Technology and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijmtm.2026.151523},
}

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A method for predicting the remaining life of mechanical equipment in production lines based on similarity features

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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.