A Novel Cross-Domain Intelligent Fault Diagnosis Method Based on Entropy Features and Transfer Learning
Yongbo Li, Yu Ren, Huailiang Zheng, Zichen Deng, Shun Wang
Abstract
Using transfer learning for fault detection and diagnosis has been a hot topic in prognostic and health management (PHM) field. In this paper, a systematic framework is established to solve the cross-domain fault diagnosis for rotary machines of the same type using entropy-based transfer learning. The multi-scale symbolic dynamic entropy (MSDE) is firstly proposed to extract features from vibration signals. Then, transfer learning model is trained to get a mapping matrix, which can preserve the structure properties of prior distribution and minimize the discrepancies between different datasets. The mapped features are called multi-scale transfer symbolic dynamic entropy (MTSDE). Finally, the support vector machine classifier is utilized to accomplish the cross-domain intelligent fault type recognition. To demonstrate the superiority of the proposed MTSDE method, the comparative experiments of nine methods are carried out under different datasets including gearboxes and bearings. Experimental results demonstrate that our proposed MTSDE method performs best in recognizing various fault types comparing with other nine methods. To the best knowledge of the authors, this is the first attempt of using entropy-based transfer learning for cross-domain fault diagnosis of rotating machinery.