Deep Transfer Network With Adaptive Joint Distribution Adaptation: A New Process Fault Diagnosis Model

Abstract

Distribution discrepancy between training data and testing data caused by varying working conditions limits the wide applications of deep learning-based methods for process fault diagnosis. Generally, this issue is addressed by transfer learning (TL) effectively. However, previous works on TL mainly focus on aligning the marginal distribution only or ignoring the different impacts of the marginal and conditional distributions of the data. Thus, it remains challenging to reduce domain shifts by considering marginal and conditional distributions adaptatively and simultaneously. In this article, a novel deep transfer network (DTN) with adaptive joint distribution adaptation (AJDA) is proposed to solve the problem of process fault diagnosis under varying working conditions. First, an adaptive joint distribution module is proposed to implement domain adaptation both in feature space and label space. AJDA not only aligns the marginal and conditional distribution simultaneously but also quantifies the importance of the two distributions. Moreover, a novel feature generator, self-calibrated-based 1-D convolutional neural network (SC-1DCNN), is developed to effectively learn shared feature representations from the process data. The adversarial training with gradient penalty is adopted to guide SC-1DCNN to provide domain-invariant features between the two domains. The testing results on four experimental cases under varying working conditions, including two simulation cases and two real cases, have demonstrated the effectiveness of AJDA in process fault diagnosis.