基于ADASYN-随机森林的智能家电内部电路故障诊断

针对智能家电内部电路故障诊断中存在的数据不平衡和分类器诊断精度低的问题，提出一种基于ADASYN算法过采样和随机森林(random forest,RF)的故障诊断方法。将电流信号进行小波包分解，提取最后一层各节点能量作为特征向量；使用ADASYN算法扩充训练数据集，得到随机森林故障诊断模型并进行测试。实验结果表明：ADASYN-随机森林故障诊断模型对智能家电内部电路故障具有较高的诊断精度，对故障诊断有一定的实用价值和指导意义。

Fault Diagnosis of Internal Circuits of Smart Home Appliances Based on ADASYN-random Forest

Aiming at the problems of data imbalance and low accuracy of classifier in the fault diagnosis of the internal circuit of intelligent appliances, a fault diagnosis method based on ADASYN algorithm over-sampling and random forest is proposed. The current signal is decomposed by wavelet packet, and the energy of each node in the last layer is extracted as the feature vector. The training data set is expanded by ADASYN algorithm, and the random forest fault diagnosis model is obtained and tested. The experimental results show that the ADASYN-random forest fault diagnosis model has high diagnosis accuracy for the internal circuit fault of intelligent household appliances, and has certain practical value and guiding significance for fault diagnosis.