Kernel adapted extreme learning machine for cross-domain fault diagnosis of aero-engines

In order to bridge the gap between the ideal experimental environment and practical engineering applications, many fault diagnosis studies have focused on transfer learning-based methods. However, the research on cross-domain fault diagnosis of aero-engine is obviously insufficient. Besides, most existing studies only consider the impact of global discrepancies between the source and target domains, while overlooking the contributions of individual samples, and the models proposed by these works usually implement the two key steps of distribution discrepancies alignment and fault identification separately, which may lead to the loss of some critical information. To tackle this issue, a novel weighted kernel adaption extreme learning machine is proposed for cross-domain fault diagnosis of aero-engine. Specifically, the proposed approach integrates white cosine similarity (WCS), projected maximum mean discrepancy (PMMD) and kernel extreme learning machine (KELM), which allows the diagnostic model to consider the contributions of individual samples to the alignment of global distribution discrepancies while minimizing the distribution discrepancies and diagnostic error. Finally, the proposed approach is evaluated by extensive tests, including traditional and cross-domain fault diagnosis experiments, and the results of improved diagnostic performance confirm the effectiveness of the proposed approach.