基于ReliefF与mRMR耦合特征选择的多联机制冷剂充注量故障诊断
Refrigerant charge fault diagnosis for variable refrigerant flow system based on ReliefF and mRMR coupling feature selection
摘要：
针对制冷剂充注量故障，提出了一种结合ReliefF和mRMR特征选择算法的故障诊断方法。首先，利用ReliefF算法计算出每个特征变量的权重系数W(A)，剔除权重系数低于阈值的特征；然后，利用集成的mRMR算法选出与目标类别具有最大相关性且相互之间具有最小冗余性的特征子集；最后，利用特征提取后的变量建立BP神经网络模型进行故障诊断，并和单一特征选择算法的结果进行对比。结果表明：该方法较好地提高了多联机制冷剂充注量故障诊断模型的诊断精度和效率。
Abstract：
For the refrigerant charge fault, presents a fault detection and diagnosis method combined with ReliefF and mRMR feature selection algorithm. Firstly, calculates the weight coefficient W(A) of each feature variable by the ReliefF algorithm, and removes the features with weight coefficient lower than the threshold. Then, selects the features which have the max-relevance with the target class and min-redundancy with each other by the ensemble mRMR algorithm. Finally, develops a BP neural network model based on the newly selected variables to achieve fault detection and diagnosis, and compares the results with that of single feature selection algorithm. The result indicates that the new method can improve the accuracy and efficiency of the diagnostic model.
Keywords:variablerefrigerantflowairconditioningsystem,refrigerantcharge,faultdiagnosis,ReliefF,maximumrelevanceandminimumredundancy,neuralnetwork