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Physical mechanism-corrected degradation trend prediction network under data missing.

Qichao Yang1, Baoping Tang1, Qikang Li1

  • 1State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400030, PR China.

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|April 23, 2024
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Summary
This summary is machine-generated.

This study introduces a novel Data Repair and Dual-data-stream LSTM (DR-DLSTM) network for accurate equipment degradation trend prediction (DTP) with missing data. The DR-DLSTM enhances feature extraction and prediction accuracy by separating trend and periodic components.

Keywords:
Data repairDegradation trend predictionDual-frequency modification unitLatent vectorSignal decomposition

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Area of Science:

  • Engineering
  • Data Science
  • Machine Learning

Background:

  • Accurate degradation trend prediction (DTP) is vital for optimizing equipment operation and maintenance.
  • Missing data presents a significant challenge in achieving reliable DTP.
  • Existing methods often struggle to effectively handle complex data variations.

Purpose of the Study:

  • To introduce a novel network, the Data Repair and Dual-data-stream LSTM (DR-DLSTM), for robust equipment DTP.
  • To address the challenge of missing data in time series prediction for equipment degradation.
  • To improve the accuracy and efficiency of degradation trend prediction models.

Main Methods:

  • Developed a DR-DLSTM framework utilizing convex optimization with polynomial and trigonometric functions for missing data rectification.
  • Implemented a Dual-LSTM block with dual data streams for enhanced feature extraction and correlation of time series components.
  • Integrated physical rule information via Fourier and wavelet transform frequency correction modules for dynamic prediction adjustments.

Main Results:

  • The DR-DLSTM demonstrated superior performance compared to state-of-the-art models across multiple datasets.
  • The model effectively handled missing data, improving the accuracy of degradation trend prediction.
  • Separate and accurate prediction of trend and periodic components was achieved, enhancing model capability.

Conclusions:

  • The proposed DR-DLSTM network offers a significant advancement in equipment degradation trend prediction, particularly in the presence of missing data.
  • The dual-stream LSTM architecture and data repair mechanism contribute to enhanced prediction accuracy and feature extraction.
  • This approach provides a more reliable tool for optimizing equipment maintenance and operational efficiency.