Hybrid Modal-Machine Learning Damage Diagnosis

Hybrid Modal-Machine Learning Damage Diagnosis

 

Integrates modal-based and machine learning (ML) based methods to develop a more damage-sensitive, noise robust, and generalised damage diagnosis method

Motivation of This Research

Cold-Start Problem faced by ML methods

Damage events are rare, which causes the cold-start problem for machine learning methods that usually requires several labelled examples of healthy and damaged conditions for training.

The need for a noise robust modal analysis method for damage diagnosis

Conventional modal analysis requires shutdown conditions, which is not practical for modal-based damage diagnosis methods.

Damage sensitive features required for unsupervised methods

Modal parameters, i.e., natural frequencies are commonly used as the damage-sensitive feature for unsupervised methods. However, as modal parameters are not the upstream data (FRFs), they could incur extraction-errors that affect their sensitivity towards damage.

Main Features of the Proposed Approach

Damage-sensitive, noise robust, and solves the cold-start problem

ISMA

Makes the program to be noise robust

PCA-FRF

Used as a damage-sensitive feature for 1st level unsupervised damage presence detection

MSA

Used as a single damage classifier to solve the cold-start issue and auto labels the damage samples

Hybrid ML

Combines unsupervised and supervised methods to diagnose unseen single and multiple damages

Tested on Plate-like and Beam-like Structures

Generalised enough to locate unseen single and multiple damages for both plate and beam-like structures

0 %
Accuracy in detecting damage presence​
0 %
Accuracy of MSA method as a single-damage classifier
0 %
Average accuracy on unseen damages

Research Outputs

  1. Siow, P. Y., Ong, Z. C., Khoo, S. Y., & Lim, K. S. (2021). Damage Sensitive PCA-FRF Feature in Unsupervised Machine Learning for Damage Detection of Plate-Like Structures. International Journal of Structural Stability and Dynamics, 21(2), 29. https://doi.org/10.1142/s0219455421500280

  2. Siow, P. Y., Ong, Z. C., Khoo, S. Y., & Lim, K. S. (in press, 2023). Noise robustness of an operational modal-based structural damage-detection scheme using Impact-Synchronous Modal Analysis. Journal of Zhejiang University-Science A. https://doi.org/10.1631/jzus.A2200620

  3. Siow, P. Y., Ong, Z. C., Khoo, S. Y., Lim, K. S., & Chew, B. T. (2023). Hybrid machine learning with mode shape assessment for damage identification of plates. Smart Structures and Systems. (accepted)

  4. Gordan, M., Siow, P. Y., Deifalla, A. F., Chao, O. Z., Ismail, Z., & Yee, K. S. (2022). Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures. Ieee Access, 10, 84837-84852. https://doi.org/10.1109/access.2022.3197776