模糊神经网络用于齿轮装置故障的逐次诊断法

Diagnosis method for gear equipment by sequential fuzzy neural network

提出了一种基于神经网络和逐次模糊推理理论,构建了逐次的模糊神经网络,对齿轮装置故障进行逐次诊断。该方法能自动精确地识别齿轮装置故障。提出了5个时域中的无量纲特征参量,并应用可能性理论,把由实测数据求得的特征参量的概率密度函数转换为可能性分布函数,可表征特征参量与设备状态间的模糊关系。逐次模糊神经网络能处理特征参量与故障状态的模糊关系,实现对故障的自动诊断。齿轮诊断实例验证了该方法的有效性及可行性。

This paper proposed a new method called a ＂sequential fuzzy neural network＂ to diagnose gear equipment failures automatically and precisely. The symptom parameters in time domain, by which each gear equipment failure can be detected sequentially, were selected according to values calculated from the signals measured in each gear condition. To express the relationship between the gear condition and the symptom parameters, the probability density functions were translated to possibility distribution functions by possibility theory. The diagnostic process can be carried out automatically by a neural network combined with sequential fuzzy inference. Examples of practical diagnosis are shown to verify the efficiency of this method.