A General Transfer Framework Based on Industrial Process Fault Diagnosis Under Small Samples

Abstract

The lack of fault samples is a challenging issue for fault diagnosis in the industrial process. It is difficult for conventional fault diagnosis methods to achieve satisfactory results under small samples. This article proposes a general transfer framework with evolutionary capability to address the above issue. First, a general transfer framework is proposed, in which the transfer learning strategy is applied to guarantee the number and diversity of expanded samples and achieve accurate modeling. Second, the adaptive mixup (Admixup) method is presented, which can adaptively expand the fault samples and make the shared distribution smoother to guarantee the stability and accuracy of the fault diagnosis results. Finally, an optimized evolution strategy is designed, in which the transformation matrix is used as an evolutionary channel to reduce the fault diagnosis errors without retraining the framework as fault samples increase. The presented framework can utilize the generalization of small samples and the knowledge of various working condition samples to achieve accurate modeling. The proposed framework is applied to simulated and real industrial processes. Experiment results illustrate that the fault diagnosis model can be effectively established by the proposed framework under small samples, and the proposed framework is evolutionary capable.