摘要
发动机是车辆的核心部件,及时有效地发现并排除故障,对降低维修费用,减少经济损失,增加发动机工作时的可靠性,避免事故发生具有重大的意义。以某型号发动机为研究对象,运用测试技术、信号处理、小波分析、神经网络和模糊控制理论,提出了基于模糊神经网络的智能故障诊断系统。建立了发动机故障信号采集试验台,在试验台上人工模拟3种转速下6种工况,通过加速度传感器采集正常工况和异常工况的振动信号,之后利用小波包技术进行消噪处理,并提取出故障信号的特征值,作为网络训练和测试的样本数据。用样本数据训练和检测自适应模糊神经网络,完成对信号的离线模式识别,之后以测试样本数据实现在线故障诊断,通过仿真分析,取得了很好的诊断效果。与传统的BP神经网络故障诊断方法进行对比,无论在诊断精度上还是学习速度上,模糊神经网络在故障诊断中更具有优势。同时,在专家系统的理论基础上,将模糊神经网络与专家系统进行信息融合,实现数据接口通信,利用网络的自学习能力建立智能故障诊断数据库和诊断规则库,通过程序语言快速高效的设计出智能诊断系统。最后,通过发动机故障诊断实例仿真分析,验证了基于模糊神经网络的智能故障诊断专家系统的可行性。
Engine is a very important part of vehicles. Fault diagnosis and removal of faults for engine timely and effective thus has important significance, which can not only reduce maintenance costs, reduce economic losses, increase the reliability of the engine at work, but also avoid accidents. A model engine was used as an example in this study. Testing techniques, signal processing, wavelet analysis, neural networks and fuzzy control theory were applied. An intelligent fault diagnosis method based on fuzzy neural network was proposed. The paper established a fault signal acquisition engine test stand, and simulated six kinds of artificial conditions under three kinds of speed. An acceleration sensor was used to collect the vibration signals of the normal condition and abnormal conditions. And then wavelet theory was used to denoise the collected vibration signal. The extracted fault characteristic value of the signal was used as network training sample data and testing sample data. The sample data was used to train and test adaptive fuzzy neural network and complete the signal pattern recognition offline. Online fault diagnosis was then realized. Compared with the traditional BP Neural Network diagnostic methods, the fuzzy neural network has more advantages in fault diagnosis, no matter in learning speed or accuracy. At the same time, on the basis of the theory of the expert system, the fuzzy neural network information fusion was combined with the expert system. The data communication interface was implemented. The network self-learning ability was used to establish a database of intelligent fault diagnosis and the rules library of diagnosis. A fast and efficient design intelligent diagnosis system was completed through the programming language. Finally, the engine fault diagnosis example simulation analysis proved that the intelligent fault diagnosis expert system based on fuzzy neural network is feasible. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
出处
《振动与冲击》
EI
CSCD
北大核心
2017年第4期164-171,共8页
Journal of Vibration and Shock
基金
内蒙古自然基金(2012MS0704)
内蒙古高校科研基金重点(NJZZ11070)
关键词
神经网络
模糊理论
专家系统
小波分析
信息融合
智能故障诊断
Control theory
Engines
Expert systems
Failure analysis
Fault detection
Fuzzy control
Fuzzy inference
Fuzzy logic
Information fusion
Losses
Neural networks
Pattern recognition
Program diagnostics
Signal processing
Testing
Wavelet analysis
