The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network

Abstract

Building electrical system fault diagnosis is the blank in the fault diagnosis field at home and abroad. The main reason is that the building electrical systems have many complex and huge subsystems; meanwhile, it is very hard to establish the mathematical model of system. By using the neural networks which is not depending on the model and using its advantage of convergence speed, the difficulties of building electrical system fault diagnosis can be well solved. This paper puts forward a method of fault diagnosis based on radial basis function neural network (hereinafter referred to as the RBF network) and applied it to building electrical system fault diagnosis. Beijing institute of civil engineering and architecture building intelligent experiment center provides building electrical test platform which can collect actual fault samples for RBF network training. After experiments and verification, RBF network’s accuracy and speed on fault diagnosis of building electrical system is significantly better than BP network. The effective RBF network in building electric system fault diagnosis field will have good engineering application prospect in the future.