基于改进的QBC和随机森林的管道类别非均衡堵塞故障识别

Blockage recognition method of drainage pipeline learning from unbalanced data based on improved QBC and random forest

针对城市埋地排水管道堵塞故障检测过程中有标签故障样本少,管道运行状态样本集存在类别不均衡及样本标注成本高昂的问题,提出一种基于主动学习的排水管道堵塞故障分类识别方法。该方法采用改进后的委员会样本查询策略通过基于一致熵的委员会样本查询策略建立主动学习模型来实现不均衡样本集的学习。经过充分考虑样本的信息度并挖掘信息度高的未标注样本进行标注后,结合多个随机森林分类器组成委员会对未标注样本进行分类识别。在实验室所采集的管道运行数据集上对委员会样本查询策略中的投票熵、一致熵和随机选择样本查询策略进行对比验证。实验结果表明,采用基于一致熵的委员会查询策略在类别分布均衡初始训练集下有更快的收敛速度和更好的稳定性,在类别非均衡分布的初始训练集下同样具有良好的识别效果。

Aiming at the problems of few labeled fault samples, unbalanced dataset of pipeline operation state data set and high cost of sample labeling in the process of urban buried drainage pipeline blockage fault detection, an classification and recognition method of drainage pipeline blockage fault based on active learning is proposed. This method adopts the improved committee sample query strategy, and established an active learning model based on consensus entropy to realize the learning of unbalanced data set. After fully considering the uncertainty of the samples and mining the most informative of unlabeled samples for labeling, the committee composed of several random forest classifiers was used to classify and identify the unlabeled samples. The vote entropy, uniform entropy and randomly selected sample query strategy are compared and verified on the pipeline operation data set collected by the laboratory. The experimental results show that the committee query strategy based on consensus entropy has faster convergence speed and better stability under the initial training set of class distribution equilibrium, and also has good recognition effect under the initial training set with unbalanced distribution of categories.