Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis

Abstract

In this article, a challenging fault diagnosis task is studied, in which no samples of the target faults are available for the model training. This scenario has hardly been studied in industrial research. But it is a common problem that massive fault samples are not available for the target faults, which limits the successes of conventional data-driven approaches in practical application. Here, we introduce the idea of zero-shot learning into the industry field, and tackle the zero-sample fault diagnosis task by proposing the fault description based attribute transfer method. Specifically, the method learns to determine the fault categories using the human-defined fault descriptions instead of the collected fault samples.The defined description consists of arbitrary attributes of the faults, including the fault positions, the consequences of the fault, and even the cause of the fault, etc. For the attribute knowledge of target faults, they can be prelearned and transferred from some readily available faults occurred in the same process. Afterwards, the target faults can be diagnosed based on the defined fault descriptions without the need for any additional data based training. Besides, the supervised principle component analysis is adopted in our method to extract the attribute related features to offer an effective attribute learning. We analyze and interpret the feasibility of the fault description based method theoretically. Also, the zero-sample fault diagnosis experiments are designed and conducted on the benchmark Tennessee-Eastman process and the real thermal power plant process to validate the effectiveness. The results show that it is indeed possible to diagnose target faults without their samples.