Multi-representation symbolic convolutional neural network: a novel multisource cross-domain fault diagnosis method for rotating system

Abstract

Multisource domain adaptation (MDA) methods have been preliminarily applied in cross-domain fault diagnosis of rotating system due to its correlation ability between different but related fields. However, it still remains challenging to learn domain-invariant representations under multisource scenarios. This article proposes a multi-representation symbolic convolutional neural network (MR-SCNN) for multisource cross-domain fault diagnosis of rotating system. The novelty of our work lies in three aspects. First, the proposed method combines symbolic dynamics with CNN to obtain a coarse-grained description of vibration signals, which could eliminate the negative transfer caused by subtle changes in dynamic characteristics among different source domains. Second, considering that most MDA methods ignore the significant limitations brought by statistical properties of the specific working condition, a multi-representation Softmax (MR-Softmax) is developed to learn domain-invariant discriminative representations by allowing the diversity of the predictions of samples with the same label. In addition, an undifferentiated adversarial training strategies is proposed to narrow the domain discrepancies and reasonably assess the residual negative transfer risk of different source domains. Based on the assessment, confidence coeffients are defined and embedded into MR-Softmax to extract and utilize the useful diagnostic knowledge on each source domain. Compared with several state-of-the-art diagnostic models, the experimental results confirm the validation of the proposed MR-SCNN method.