Acoustical damage detection of wind turbine blade using the improved incremental support vector data description

Abstract

The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built..