Deep Negative Correlation Multisource Domains Adaptation Network for Machinery Fault Diagnosis Under Different Working Conditions

Abstract

Machinery fault diagnosis is crucial to ensure production safety. Deep learning has been widely investigated in intelligent fault diagnosis. However, the mechanical equipment generally runs in a complex and polytropic condition. It is challenging to perform machinery fault diagnosis under polytopic conditions. In this article, a novel multisource domain adaptation method is proposed for machinery fault diagnosis under different working conditions. First, a deep negative correlation multisource domains adaptation network (DNC-MDAN) is proposed, where the information from multisource domains is transferred into target domain based on the multisource feature alignment and adversarial learning. Second, in order to make full of the multisource domains information, an ensemble classifier corresponding to multisource domains is developed, where a DNC learning is performed in the ensemble classifier. Third, an end-to-end feature generator, discrete cosine convolutional block, is proposed in DNC-MDAN, where discrete cosine transform is embedded in the network smoothly for noise reduction. In order to validate the effectiveness of DNC-MDAN for machinery fault diagnosis, three cases are considered in this article. In comparison with other transfer learning methods, DNC-MDAN has a better fault diagnosis performance.