Automatic machinery fault detection via using distributed sensor information

Abstract

It is critical in intelligent manufacturing that monitors devices to sustain normal status. Recently, the artificial intelligence‐powered diagnosis is the key in smart monitoring process and has become a hot topic in the engineering field. In previous diagnostic methods, complete failure samples are the premise to activate the intelligent diagnostic model. However, the actual failures of the mechanical diagnostic are far more than that we can obtain under running state in advance. In order solve this issue, this paper adopts Generative Adversarial Networks (GAN) by using simulated data to obtain complete failure samples, which builds a bridge between artificial intelligent model and mechanical fault diagnostic system. In mechanical diagnostic system, we first use finite element simulation to generate the missing fault samples. The generated and existing fault samples are supplemented each other to construct complete failure samples. The complete failure samples are used as training set to train an intelligent model which is used to predict the status for future status of mechanical system. The status of mechanical system is represented as the signal from distributed sensors. The fault diagnostic system is verified on a public mechanical system dataset and the results demonstrate it is superior to previous ones.