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Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.

Di Wang1, Xiaochen Xian2, Changyue Song3

  • 1department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|April 10, 2024
PubMed
Summary

This study introduces a joint learning model for recognizing failure modes and predicting remaining useful lifetime (RUL) in manufacturing systems. The model integrates failure mode information for more accurate RUL predictions, improving prognostics health management (PHM).

Keywords:
Joint learningfailure mode recognitionfeature extractionremaining useful lifetime prediction

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Accurate prognostics health management (PHM) is crucial for preventing unexpected failures in manufacturing systems.
  • Existing methods often treat failure mode recognition and remaining useful lifetime (RUL) prediction independently, limiting accuracy.
  • RUL prediction is highly dependent on specific failure modes due to distinct sensor signal degradation patterns.

Purpose of the Study:

  • To propose a novel joint learning model for simultaneous failure mode recognition and RUL prediction.
  • To leverage interpretable degradation features extracted from multiple sensor signals for improved PHM.
  • To enhance the accuracy of RUL prediction by incorporating failure mode information.

Main Methods:

  • Developed a joint learning model integrating multiple sensor signals for degradation processes.
  • Extracted interpretable features considering degradation mechanisms as input for a deep neural network.
  • Trained the model using historical unit data, including sensor signals, failure times, and failure modes.

Main Results:

  • The joint learning model successfully performs failure mode recognition and RUL prediction simultaneously.
  • The model effectively characterizes complex relationships between features, RUL, and failure modes.
  • Demonstrated effectiveness through a case study on aircraft gas turbine engine degradation.

Conclusions:

  • Jointly learning failure mode recognition and RUL prediction significantly improves PHM accuracy.
  • The proposed data-driven neural network approach is flexible and applicable to complex manufacturing systems.
  • This method offers a robust solution for predicting unit RUL and identifying failure modes in real-world applications.