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.
Highlights
The turbofan engine is the main propulsion system for commercial and military aircrafts
A deep belief network (DBN)-based gas path fault diagnosis scheme for turbofan engines, which is composed of a turbofan engine reference model and a DBN diagnosis model, is proposed in this paper
The DBN diagnosis model is composed of several restricted Boltzmann machines (RBM) and was trained with the contrastive divergence algorithm and the back propagation algorithm
Summary
The turbofan engine is the main propulsion system for commercial and military aircrafts. Thirteen measurable variables are selected for fault diagnosis, including fuel flow Wf, low pressure spool speed nL, high pressure spool speed nH, total pressure after fan P21, total pressure after high pressure compressor P3, total temperature and total pressure after combustion chamber T4, P4, total pressure before high pressure turbine P41, total pressure after high pressure turbine P44, total temperature and total pressure before low pressure turbine T45, P45, total pressure after low pressure turbine P5, and total temperature after nozzle T8 The difference between these sensor signals and the output parameters of the engine reference model is calculated, and the results are used as the input of the DBN diagnosis model.
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