计算机与现代化 ›› 2013, Vol. 1 ›› Issue (7): 109-112,.doi: 10.3969/j.issn.1006-2475.2013.07.029

• 人工智能 • 上一篇    下一篇

基于小波神经网络的锅炉故障诊断及应用研究

吴国安,刘春生,薛雅丽   

  1. 南京航空航天大学自动化学院,江苏南京210016
  • 收稿日期:2013-01-10 修回日期:1900-01-01 出版日期:2013-07-17 发布日期:2013-07-17

Study of Boiler Fault Diagnosis Based on Wavelet Neural Network and Its Applications

WU Guo-an, LIU Chun-sheng, XUE Ya-li   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2013-01-10 Revised:1900-01-01 Online:2013-07-17 Published:2013-07-17

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。

关键词: 锅炉, 小波神经网络, 故障诊断, BP神经网络

Abstract: 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.

Key words: boiler, wavelet neural network, fault diagnosis, BP neural network