【摘要】 提出了多参数自确认传感器概念,给出了多参数自确认传感器的功能模型。故障诊断单元是实现其多参数自确认功能的重要部分,研究了一种基于偏最小二乘法(PLS)和支持向量机(SVM)的多参数自确认传感器故障诊断方法。利用PLS提取多参数自确认传感器已知工作状态数据的主成分,作为表征多参数自确认传感器各种工作状态的状态特征矩阵,并对其进行特征编码;利用状态特征矩阵作为输入,状态特征编码作为目标训练SVM分类机,得到SVM分类机的参数。在故障诊断单元中,利用PLS在线提取多参数自确认传感器测量数据的状态特征矩阵,输入训练完成的SVM分类机进行分类,最终确认多参数自确认传感器的工作状态。实验结果证明了该方法的有效性。更多还原

【Abstract】 This paper introduced the concept of multi-parameter self-validating sensor,including definition and function model. Fault diagnosis unit plays an important part in incoming the self-validating function model.The multi-parameter self-validating sensor fault diagnosis based on partial least square(PLS) and support vector machines(SVM) was researched.The method with PLS to get the principal components of the sensor’s measuring data as feature matrix and SVM to classify the sensor’s working status was proposed for sensor fault diagnosis.The PLS method was applied for feature extraction to get the feature matrix which denote all kinds of known working status of multi-parameter self-validating senso,and the feature matrix were encoded.The feature matrix as inputs and feature codes as outputs were proposed for training the SVM classifier to get the optimum parameters.In the fault diagnosis unit,it used the PLS method to acquire the on-line feature matrix of multi-parameter self-validating sensor.The on-line feature matrix was supplied to the trained SVM classifier as inputs to validate the working status of multi-parameter self-validating sensor.If the sensor is healthy,the validated outputs are given directly;otherwise the sensor should auto alarm and recover the outputs in a short period of time.Finally,the applicability and effectiveness of the multi-parameter self-validating sensor fault diagnosis based on partial least square(PLS) and support vector machines(SVM) are illustrated by the sensor’s self-validating results.更多还原