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Fault prediction method of large forging press based on a multi scale and multi model integrated method.

Chao Yuan1,2, Tianmin Zhang3, Yuxi Tang4

  • 1China Academy of Machinery Beijing Research Institute of Mechanical & Electrical Technology Co., LTD, Beijing, China.

Scientific Reports
|August 20, 2025
PubMed
Summary

This study introduces a novel multi-model ensemble approach for predicting faults in large forging presses. While not meeting all industry standards, the proposed method shows superior performance compared to existing models for critical component state prediction.

Keywords:
AutoregressiveFault predictionLarge forging pressLong Short-Term MemorySupport vector regression

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Area of Science:

  • Mechanical Engineering
  • Manufacturing Technology
  • Artificial Intelligence in Industry

Background:

  • Large forging presses are crucial in automotive, shipbuilding, and aerospace manufacturing.
  • Intricate component relationships in these presses make early fault detection challenging.
  • Traditional single-model prediction methods struggle with nonlinear, stochastic fault characteristics.

Purpose of the Study:

  • To develop an advanced fault prediction model for large forging presses.
  • To overcome limitations of traditional single-model approaches in complex industrial settings.
  • To improve the accuracy and generalization capability of fault signal prediction.

Main Methods:

  • Proposed a multi-scale Autoregressive-Support Vector Regression-Long Short-Term Memory (AR-SVR-LSTM) ensemble prediction approach.
  • Integrated AR, SVR, and LSTM models with weight constraints for hybrid prediction.
  • Validated the model using brake oil pressure data from an 80MN electric screw press.

Main Results:

  • The proposed AR-SVR-LSTM ensemble model showed significant advantages over comparative models.
  • The model's accuracy, while not meeting stringent industry standards (MAE < 5%, RMSE < 1%), demonstrated effectiveness.
  • Case study on an 80MN electric screw press confirmed the model's practical applicability.

Conclusions:

  • The multi-model ensemble approach offers a superior alternative for fault prediction in large forging presses.
  • Despite not meeting all industry benchmarks, the model's enhanced performance warrants further investigation.
  • This research contributes to advancing predictive maintenance strategies in heavy-duty manufacturing.