摘要
针对风机常见故障征兆与故障类型之间的非线性映射关系,结合专家知识建立了风机系统故障知识库,提出了基于PNN神经网络的风机故障诊断方法,结果表明该方法能克服BP算法诊断过程中容易陷入局部极小的缺点,并能满足故障诊断的快速性和准确性要求,适用于在线检测,具有实际应用价值。
Based on nonlinear mapping relationship between fault symptom and fan faults, probabilistic neural network （PNN） approach was presented for fault diagnosis. Then fault features were extracted from fan failures and the extracted features were regarded as fault symptom eigenvector. Fault diagnosis model and fault diagnosis algorithm were given using probabilistic neural network. The result shows that probabilistic neural network can overcome the limitation of local imrinitesimal of BP, and can meet the requirement for fast diagnosis rate and high diagnosis precision during fault diagnosis process, so probabilistic neural network can be used in the real time diagnosis. And it shows that the fault diagnosis based on probabilistic neural network is useful.
出处
《煤矿机械》
北大核心
2007年第10期187-189,共3页
Coal Mine Machinery
关键词
PNN神经网络
故障诊断
风机
probabilistic neural network
fault diagnosis
fan
作者简介
李铁军（1978-），辽宁新民人，沈阳化工学院讲师，2003年毕业于东北大学机械设计及理论专业，获工学硕士学位，主要从事机械优化设计和机械故障诊断方面研究，发表论文10余篇，电子信箱：litiejun780920@sina．com．
