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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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Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter.

Yan Zhang¹, Yongbo Zhang^1,2, Jinhui Yu¹

¹School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study introduces a new method for structural health monitoring using the Unscented Kalman Filter (UKF) to identify damage and predict system reliability in real-time.

Keywords:

Unscented Kalman Filter dynamic reliability prediction performance degradation process structural damage identification

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Area of Science:

Structural Engineering
Reliability Engineering
Signal Processing

Background:

Advancements in sensor technology enable structural online monitoring.
Challenges persist in real-time damage diagnosis and dynamic reliability prediction.

Purpose of the Study:

To develop a method for online structural damage identification.
To achieve accurate real-time dynamic reliability predictions for structures.

Main Methods:

Utilized the Unscented Kalman Filter (UKF) for structural damage identification.
Employed the Expectation-Maximization (EM) algorithm for performance model parameter estimation.
Implemented conditional probability for dynamic reliability prediction.

Main Results:

The proposed method accurately identifies structural damage states during operation.
Achieved precise, real-time, and dynamic reliability predictions.
Demonstrated effectiveness in Wiener degradation processes with random effects.

Conclusions:

The UKF-based approach enhances structural health management.
Provides reliable tools for assessing structural integrity and predicting future performance.