数据驱动的变速器传感器故障诊断方法

Data-driven Fault Diagnosis Method for Transmission Sensors

摘要:

针对基于模型以及基于规则的故障诊断方法的局限性，运用数据驱动的方法对变速器传感器进行故障诊断。使用逐步回归算法建立传感器模型，将实际传感器输出与传感器模型输出相减得到残差序列；用小波包变换（WPT）对残差序列进行分解，提取节点的香农熵作为特征值；最后，用概率神经网络（PNN）对不同传感器故障的特征值进行识别。使用硬件在环仿真获取车辆行驶过程中的变速器信号对该方法进行验证。结果表明：该方法的诊断正确率达到98.50%，在不同的样本划分情况下诊断正确率变化很小。此外，还对其他多个变速器传感器进行了故障诊断，诊断正确率均在较高值，证明了该方法的普适性。

Abstract:

Aiming at the limitations of model-based and rule-based fault diagnosis methods， a data-driven fault diagnosis method for transmission sensors was proposed. First， a residual sequence was obtained between the output of actual sensor and the output of sensor model established by step-wise regression. Then， the residual sequence was decomposed by wavelet packet transform（WPT）， and the Shannon entropy of each node was calculated as the feature values. Finally， a probabilistic neural network（PNN） was adopted to identify the feature values of different sensor faults. This method is verified by transmission signals from hardware-in-the-loop platform. Results indicate that the method has a diagnostic accuracy of 98.50%， and the diagnostic accuracy varies little under different sample divisions. In addition， the fault diagnoses of two speed sensors were also performed， and the diagnostic accuracy is at a relatively high value， which proves the applicability of the method.