基于主成分分析与支持向量机的热泵系统制冷剂泄漏识别研究

Research on refrigerant leakage identification for heat pump system based on PCA-SVM models

为了研究热泵系统制冷剂泄漏识别的数据挖掘理论方法和实验验证,首先建立空气源热泵系统制冷剂泄漏实验台,进行热泵系统正常工况、干扰工况、泄漏工况的实验参数测试;其次,采用主成分分析法对测试数据进行特征提取处理,采用支持向量机对数据进行分类识别,建立了用于热泵系统的制冷剂泄漏识别的主成分分析-支持向量机模型,在二分类和多分类模式下验证了模型的性能,并研究了泄漏速率和不同故障工况对模型的影响。采用RefliefF特征选择算法对原始特征参数进行筛选,简化了识别模型的特征参数。研究结果表明:对于空气源热泵热水系统,PCA-SVM泄漏识别模型在多种验证集中对泄漏工况的识别准确度达100%,缓慢泄漏的诊断识别性能弱于快速泄漏,同一模型在不同故障诊断识别中性能不同,对系统运行影响轻微的故障诊断识别性能弱于其他故障。RefliefF特征选择方法将原始41个系统特征参数精简至10个特征参数,参数筛选优化后的泄漏识别模型识别精度也维持在较高水平,优化的泄漏识别模型更利于实际应用。

To study the data mining theory method and experimental verification of the refrigerant leakage identification of the heat pump system,firstly establish an air source heat pump system refrigerant leakage test bench to test the experimental parameters of the heat pump system in normal working conditions,interference working conditions,and leakage working conditions.Then,principal component analysis(PCA)was used to process the experimental data,and support vector machine(SVM)was used to classify and identify the data.A leakage identification model based on PCA-SVM was established which verified in both two classification and multi classification model.The leakage rate and the influence of different fault conditions of the model was studied.RefliefF feature selection algorithm is used to screen the original feature parameters which simplify the feature parameters of the identification model.The results show that,for the air source heat pump water heater,the leakage identification model has a high identification of 100%in the leakage mode,and the slow leak diagnosis recognition performance of the weak in rapid leak,the same model in different fault diagnosis recognition performance is different,slight influence on the system's running fault diagnosis recognition performance is weaker than other malfunction.RefliefF feature selection method reduces the original 41 system characteristic parameters to 10 characteristic parameters.The identification accuracy of the leakage identification model after parameter screening and optimization is also maintained at a high level,the optimized leakage identification model is more conducive to practical application.