摘要
锅炉作为燃烧的核心设备,其安全运行至关重要,由于锅炉结构复杂,损伤、磨损、酸气腐蚀以及操作不当均会引起故障,为了有效地避免故障,本文将小波变换和神经网络相结合构成小波神经网络用于锅炉故障诊断。实验结果表明,小波神经网络充分继承了小波变换和神经网络的优点,该方法具有良好的故障诊断能力,在故障诊断的准确度上明显地优于BP神经网络。
Boiler as the pivotal equipment of burning,its safe operation is essential,because boiler has complex structure,damage,wear,acid gas corrosion and improper operation will cause malfunctions.In order to avoid failure,this paper combines wavelet transform and neural network to constitute wavelet neural network and applies it to boiler fault diagnosis.Experiment results show that the wavelet neural network fully inherits the advantages of wavelet transform and neural network,this method has better fault diagnostic capabilities,the fault diagnosis accuracy is obvious better than BP neural network.
出处
《计算机与现代化》
2013年第7期109-112,116,共5页
Computer and Modernization
