Zheng Chai

TL;DR: An enhanced random forest algorithm with a concurrent analysis of static and dynamic nodes is proposed to address the issue for fault classification and outperforms the traditional learning algorithms with remarkable accuracy and F1 score.

TL;DR: A new method termed the fine-grained adversarial network-based domain adaptation (FANDA) is proposed to address the cross-domain industrial fault diagnosis problem and can reduce the distribution discrepancy of both the global domains and each fault class across the domains automatically.

TL;DR: In this paper, a single-side canonical correlation analysis (SsCCA) is proposed to address the fault detection problem for industrial systems. But, it is not optimal in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal.

TL;DR: A transfer learning method is proposed for detecting sensor faults in dynamic systems with consideration of actuator-performance degradation, whose structure is a federated neural network, which can be regarded as a new unified framework for data-driven parameter identification with adaptive model calibration.

TL;DR: A batch multiclass ObRF (ObRF-BM) algorithm is proposed by using a broad learning system and a multi-to-binary method to obtain an optimal oblique hyperplane in a higher dimensional space and then separate the samples into two supervised clusters at each node, which provides the basis for the following incremental learning strategy.