Deep Transfer Learning based Multisource Adaptation Fault Diagnosis Network for Industrial Processes
Zheng Chai, Chunhui Zhao
Abstract
In industrial processes, there are generally multiple data sources generated from different working conditions, which can provide different fault diagnosis knowledge to the target application. In this paper, a multisource adaptation diagnosis network (MADN) method is proposed to transfer the diagnostic knowledge existed in multiple sources to the target. First, a stacked-autoencoder based feature generator is pretrained to extract feature representations from the process data acquired from diverse working conditions. Then, domain discriminators are developed to reduce the distribution discrepancy between the target domain and each of the sources in an adversarial way. The domain discrimination ability, on the other hand, also reveals the different importance of the source domains. Thus, the fault classifiers can be assembled to identify the fault types of the unlabeled target data. The superiority of the proposed method is verified using a real-world process.