Industrial process fault diagnosis based on domain adaptive broad echo network

Background: In response to the challenge that traditional fault diagnosis models are difficult to maintain satisfactory accuracy when data distribution changes due to changes in process conditions, a fault diagnosis model of industrial process based on domain adaptive broad echo network (DABEN) is proposed. Methods: The DABEN model first constructs feature nodes through random feature mapping to extract shallow features of process data, and then inputs feature nodes into cascade reservoirs to extract dynamic features of different levels. On this basis, the objective function of DABEN is constructed, which starts from the four goals of minimizing the prediction error, maximum mean discrepancy distribution alignment, manifold regularization and minimizing cross-domain error to ensure that the features are as similar as possible between the source domain and the target domain. Significant Findings: Finally, two simulation cases show that DABEN can achieve good transfer fault diagnosis performance under different data distributions