Deep Belief Network-Based Gas Path Fault Diagnosis for Turbofan Engines

Abstract

A gas path fault diagnosis scheme for turborfan engines based on deep belief network (DBN) is presented. The scheme is constructed according to the diagnosis principles of gas path faults and is composed of a turbofan engine reference model and a DBN diagnosis model. The DBN diagnosis model is a stacked network of several restricted Boltzmann machines (RBM) and was trained with the contrastive divergence algorithm and the back propagation algorithm. To optimize the DBN performance, the orthogonal tests L25 (57) were adopted to determine the hyper-parameters, such as learning rate, hidden layer number, hidden layer neuron number, etc. The proposed DBN-based scheme was applied to diagnose the gas path faults of a turbofan engine model and compared with BP-based and SVM-based schemes. The results show that the fault diagnosis accuracy of the DBN-based scheme is as high as 96.59%, and the DBN-based scheme has dramatic performance advantages over the other two schemes.