针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题.本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率和抗噪声能力更高的卷积神经网络—BICNN(Convolution Neural Network based on Inception),并用BIC-NN卷积神经网络基于数据驱动的方式,对楼体框架模型进行了集成故障诊断研究.集成诊断结果表明BICNN具有更高的识别率和较强的抗噪声能力,而且在训练步数较少的情况下振荡次数少收敛情况良好.因此采取本研究所提出的方法,对框架结构进行故障诊断时具有高诊断率和稳定性,为维护框架结构的稳定运行具有重大安全意义.
Integrated Fault Diagnosis of Frame Structure Based on Neural Network with Inception Model
In view of the bad working environment of frame structure,which is often accompanied by a lot of noise,when using ordi-nary one-dimensional convolution neural network for fault diagnosis of frame structure,it is unable to make effective fault diagno-sis.In this study,the inception module is added to a convolutional neural network with strong anti-noise ability,the recognition rate and anti-noise are obtained.Therefore,a more powerful convolution neural network named BICNN(Convolution Neural Net-work based on Inception)is raised.Based on the data-driven method,BICNN convolution neural network is used to study the inte-grated fault diagnosis of a building frame model.The integrated diagnosis results show that BICNN has higher recognition rate,stronger anti-noise ability,less oscillation times and good convergence under the condition of less training epoch.The method pro-posed in this study has high diagnosis rate and stability in fault diagnosis of frame structure,which is of great safety significance to maintain the stable operation of frame structure.
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