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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Research on a Multi-Dimensional Information Fusion Mechanical Wear Fault-Diagnosis Algorithm Based on Data

Qifan Zhou¹, Bosong Chai², Kunwen Ran¹

¹School of Power and Energy, Northwestern Polytechnical University, Xi'an 710129, China.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This study introduces a novel method combining diffusion models and test-time training (TTT) to improve mechanical wear fault diagnosis. The approach achieves over 95% accuracy in identifying six types of aero-engine wear faults, overcoming data limitations.

Keywords:

aero-engine diffusion model fault diagnosis algorithms mechanical wear test-time training

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Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Area of Science:

Mechanical Engineering
Artificial Intelligence
Machine Learning

Background:

Limited data distribution characteristics hinder machine learning for mechanical wear fault diagnosis.
Advanced deep learning models face challenges with small datasets in laboratory settings.

Purpose of the Study:

To develop a robust method for diagnosing and localizing mechanical wear faults using limited data.
To enhance the accuracy of fault diagnosis in aero-engine components.

Main Methods:

Utilized a combination of diffusion models and test-time training (TTT).
Employed a pre-trained decoder for data regeneration into a continuous potential representation.
Implemented TTT with self-supervised loss for synchronous training during testing, leveraging transfer learning.

Main Results:

Achieved a diagnosis accuracy exceeding 95% for six typical aero-engine mechanical wear fault types.
Successfully constructed a high-dimensional mapping between feature parameters and mechanical wear failure modes.
Demonstrated effective data regeneration and model adaptation using TTT.

Conclusions:

The proposed diffusion model and TTT combination effectively overcomes data limitations in mechanical fault diagnosis.
This approach offers a significant advancement in the accuracy and reliability of aero-engine wear fault detection.
The method shows promise for real-world applications requiring precise fault localization with limited data.