Quantitative blade damage detection based on multisource domain and multistage joint transfer

Abstract

For blade damage detection under variable rotational speeds of centrifugal fans, transfer learning under working conditions appears to be more effective than deep learning. However, damage detection models with small samples and single-source domain transfer are still characterized by insufficiencies, such as negative transfer and poor accuracy. Therefore, in this paper, a quantitative blade damage detection method based on multisource domain and multistage joint transfer is proposed. First, an anchor adapter is constructed using linear discriminant analysis projection combined with a feature similarity index metric to acquire the weight matrix of multisource domain-target domain data. Second, a multisource domain feature extractor based on the fusion of vibroacoustic information is established, obtaining the feature set of the target domain data. Then, the feature set is filtered through information gain and max-relevance and min-redundancy to remove the negative transfer features and is combined with an improved supervised locally linear embedding to build a subspace structure preservation model for the alignment between the source and target domain feature distributions. Finally, the classifier is fine-tuned with small sample data for quantitative damage detection of blades with variable rotational speeds. The proposed method is verified using experimental data from two centrifugal fans. The results show that the detection accuracy of the proposed model is significantly higher than that of any comparison model with a single source domain or single stage. Compared with other transfer models, the proposed method is characterized by higher detection accuracy and generalization performance.