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Related Experiment Videos

Parallel fusion model for complex multi-source vibration time-series prediction.

Wei Huang1,2,3, Jian Xu4

  • 1SINOMACH Academy of Science and Technology Co. Ltd, SINOMACH Research Center of Engineering Vibration Control Technology, Beijing, 100080, China. huangweiac@126.com.

Scientific Reports
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel parallel fusion model for accurate vibration signal prediction, enhancing accuracy by optimizing hyperparameters and integrating advanced decomposition and ensemble methods for complex systems.

Keywords:
Complex multi-source vibrationHyperparameter optimizationKNNMVMDParallel fusion modelParallel weighted fusionTime-series predictionXGBoost

Related Experiment Videos

Area of Science:

  • Engineering
  • Data Science
  • Signal Processing

Background:

  • Complex multi-source vibration signals exhibit non-stationarity and multi-frequency coupling, leading to insufficient prediction accuracy with traditional methods.
  • Existing models struggle with subjective bias in parameter tuning and error propagation in serial prediction structures.

Purpose of the Study:

  • To propose a parallel fusion prediction model for complex multi-source vibration signals.
  • To enhance prediction accuracy by integrating hyperparameter optimization, multivariate variational mode decomposition (MVMD), and a parallel weighted K-Nearest Neighbors (KNN)-eXtreme Gradient Boosting (XGBoost) structure.
  • To provide a robust technical pathway for high-precision time-series prediction in critical engineering applications.

Main Methods:

  • A three-set data separation framework with stepwise grid search for hyperparameter optimization (KNN neighbors, XGBoost depth/learning rate, fusion weights).
  • Multivariate Variational Mode Decomposition (MVMD) to decompose signals into intrinsic mode functions (IMFs), mitigating mode mixing.
  • A parallel weighted KNN-XGBoost model for independent prediction and weighted fusion, avoiding serial error propagation.

Main Results:

  • The proposed MVMD-parallel-KNN-XGBoost model demonstrated optimal balanced performance compared to other benchmark models.
  • Hyperparameter optimization effectively reduced subjective bias and improved model efficiency.
  • MVMD successfully decomposed complex signals while preserving multi-source coupling features.

Conclusions:

  • The developed parallel fusion model offers a significant improvement in predicting complex multi-source vibration signals.
  • This approach provides a reliable technical pathway for high-precision time-series prediction in aerospace, rail transit, and industrial engineering.
  • The integration of optimized parameters, modal separation, and parallel fusion is key to achieving superior prediction accuracy.