Work

Unsupervised Wind Turbine Blade Damage Detection With Memory-Aided Denoising Reconstruction

Year: 2024

Type: article

Abstract: AI-based automated wind turbine blade damage detection has significant economic value. This article proposes a novel memory-aided denoising autoencoder for unsupervised blade damage detection, which detects structural damages with a denoising autoencoder and detects logical damages with a designed memory system. Specifically, by training a denoising autoencoder on blade data with synthesized artificial damage as input and corresponding damage-free data as output, it learns to differentiate between damage and normal domains, thereby detecting structural damage. Furthermore, a memory system encompassing memory reading, writing, and management is designed for the denoising autoencoder, enabling the approach to detect logical damages effectively by leveraging information read from memory. Experimental results on a blade damage dataset, MvTec anomaly detection dataset, and MvTec logical constraints anomaly detection not only show our approach outperforms state-of-the-art methods by 4.7% on logical damages without compromising its performance on structural damages but also verify its generalization and robustness.

Source: IEEE Transactions on Industrial Informatics

DOI: 10.1109/tii.2024.3459612

Authors: Xiaodong Jia, Xiao Chen

Institution: Technical University of Denmark

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $2,495