智能异构模型在电机故障诊断中的研究

提出以测试电机的三相电流噪声为电机故障特征信号的诊断方法，建立电机电流噪声多元时序模型，将时序模型的多元残差序列化为一元序列作为故障总体检测指标。针对多元时序模型参数Φi的特点，提出了多层NN结构的故障类型识别模型，应用APEX网络提取初始模式向量的分类信息，利用前馈网络建立其识别函数，实践证明该诊断方法是正确的。
关 键 词 故障诊断; 时序模型; 神经网络; 模式向量

Abstract The paper puts forward a set of faults diagnosis methods of testing the noise of the three-phase motor current which shows the characteristics of the motor faults, and multi-varieties time series models of the noise is established, the multi-varieties residual series is changed to the monistical as the fault detection index. In the light of trait of the model parameter Φi, presenting a faults classification recognition model based on the multi-layer NN structure, using APEX network extracts classification Information of the initial pattern vector, making use of feedforward network establishes the classification function. The diagnosis way is correct by practising.
Key words faults diagnosis; time series model; neural network; pattern vector