排水管道堵塞故障的声诊断方法研究

Research on acoustic diagnosis method of blockage in drainage pipeline

针对现有城市排水管道堵塞程度难以辨识的问题,提出一种基于多特征融合与随机森林的排水管道堵塞程度辨识方法.首先对排水管道中采集到的声响应信号进行分帧处理建立信号帧集合.其次,提取各个信号帧的A计权总声压级、能量熵、分形盒维数指标构建特征向量集合;引入距离可分性判据,以达到对特征向量集合去冗余并提高特征区分度的目的;并利用类内类间散布矩阵的迹作为权重实现特征的加权融合.最后,对加权融合的特征向量集合使用随机森林进行故障辨识.实验结果表明,基于距离可分性判据的多特征融合特征向量集合可取得更高的管道堵塞故障识别率;同时,随机森林的堵塞辨识模型与SVM辨识模型比较,随机森林辨识模型有较高的准确率和较快的辨识速度.经验证,本方法不仅能有效地识别不同程度的管道堵塞故障和重复堵塞情况,而且能够排除管道配件比如三通件对故障识别的影响.

In order to solve the problem of partial blockage detection of urban drainage pipeline and the difficulty in identifying the degree of blocking,a method based on multi-feature extraction,combined with random forest for blockage recognition is proposed in this paper.Firstly,the acoustic response signals of the drainage pipeline are divided into frames as feature set.Then the characteristics of A weighted total sound pressure level,energy entropy and fractal box dimension of the feature components are extracted respectively,so the classification feature sets can be constructed. In addition,the distance separability criterion is introduced into feature selection to remove redundancy and enhance the discrimination of the classification feature sets.The weighted fusion of the three features is achieved by using the trace of the scatter matrix in the class as the weight.Finally,random forest identification model is established from the classification feature sets based on the weighted fusion.The results demonstrate that multi-feature fusion has a higher recognition rate based on the distance separability criterion of the classification feature set.Meanwhile,random forest identification model has higher accuracy and faster recognition rate compared with SVM recognition model.Furthermore,the method can not only effectively identify different degrees of pipe blockage and multiple blockage,but also can eliminate the impact of the pipe accessories such as lateral connection for fault identification.