Chao Chen

TL;DR: Experimental comparison between transfer learning and traditional machine learning has verified the superiority of the proposed algorithm for bearing fault diagnosis, which improves accuracy and relaxing computational load of the presented approach.

TL;DR: A new DAL, aiming to reduce the discrepancy on extracted features under a least square support vector machine (LSSVM) framework, is studied to exploit SD signals from another working conditions or adjacent mechanical parts to assist and boost target gear fault diagnostic performance in this article.

TL;DR: Experimental results prove that the proposed latent feature-based transfer learning (TL) strategy has a significant advantage over gear fault diagnosis, especially under varying working conditions.

TL;DR: In this article, a bearing fault diagnosis method and system based on improved LSSVM transfer learning is presented. But the method comprises the following steps: processing target data and auxiliary data through employing recurrence quantification analysis, extracting a nonlinear feature and combing the non linear feature with a conventional time domain feature, forming a characteristic vector, and forming a training set; constructing a fault classification model through employing an improved LssVM transferlearning, extracting the nonlinear features of unmarked fault vibration data of a target bearing under a target work condition through the recurrence Quantification analysis

TL;DR: A non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution.