基于随机森林和支持向量机混合模型的空调故障检测

Mixed model based on support vector machine and random forests air conditioning failure detection

目前,随着社会的发展,空调系统被广泛应用在各种建筑中,而空调的故障检测也被认为是现代建筑面临的主要挑战之一。由于空调运行的数据来自于大量传感器,因此在早期阶段很难被检测发现。尽管近年来使用统计建模和机器学习的方法,被人们广泛用于空调故障检测和诊断中,但故障的早期检测仍然是一项艰巨的任务,同时还面临对故障检测准确性不高的问题。通过结合随机森林和支持向量机的混合分类器,用于对空调系统的故障检测和诊断应用。实验表明,这种随机森林和支持向量机的混合分类模型在故障分类上有着较高的精度,并且对训练样本的要求不高,可以使用较少的训练样本训练并得到较好的准确率。

At present,with the development of society,air conditioning systems are widely used in various buildings,and fault detection of air conditioners is also considered to be one of the main challenges facing modern buildings.Because the data on the operation of the air conditioner comes from a large number of sensors,it is difficult to detect and detect at an early stage.Although in recent years,the use of statistical modeling and machine learning methods has been widely used in air conditioning fault detection and diagnosis.However,early detection is still a difficult task,and it also faces the problem of poor accuracy of fault detection.By combining a hybrid classifier of a random forest and support vector machine are used for fault detection and diagnostic applications of air conditioning systems.Experiments show that this kind of mixed classification model of random forest and support vector machine has high accuracy in fault classification,and the requirements for training samples are not high,and fewer training samples can be used to train and obtain better accuracy.