基于CEEMD和RNN-LSTM的饲喂管道堵塞故障识别  

Fault identification of feeding pipeline blockage based on CEEMD and RNN-LSTM

作　　者：刘涛[1] 陈龙 宗哲英[1] LIU Tao;CHEN Long;ZONG Zheying(College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Hohhot O10000,China)

机构地区：[1]内蒙古农业大学机电工程学院,呼和浩特010000

出　　处：《黑龙江畜牧兽医》2022年第24期39-43,136,共6页Heilongjiang Animal Science And veterinary Medicine

基　　金：内蒙古自治区高等学校科学研究项目(NJZZ23041)。

摘　　要：为了解决稀料饲喂系统使用过程中管道堵塞程度难以识别的问题,试验采用一种基于完全经验模态分解(complete ensemble empirical mode decomposition,CEEMD)的改进循环神经网络(recurrent neural network,RNN)-长短期记忆模型(long and short-term memory,LSTM)算法来实现对饲喂管道堵塞状态的检测,即对采集到的饲喂管道中的声音反馈信号进行CEEMD分解,得到本征模态函数(intrinsic mode function,IMF)分量,从IMF分量中提取能量占比和近似熵作为特征向量构建特征集合M1;根据皮尔逊相关系数和能量占比的特性选取特征中相关性强的和包含信息量多的IMF分量重新构建特征集合M2;再利用BP神经网络(back-propagation network)和RNN-LSTM算法模型分别对三通件管道、无堵塞管道、轻度堵塞管道、中度堵塞管道、重度堵塞管道5种工况进行分类识别。结果表明:单一特征的识别准确率低于多特征识别准确率,经过特征筛选后的识别准确率高于未筛选的;在相同试验条件下,RNN-LSTM算法对饲喂管道堵塞状态识别准确率高于BP神经网络。说明RNN-LSTM算法模型能有效识别饲喂管道内的不同程度堵塞状况,可实现对管道堵塞情况的预测,具有一定的实际应用价值。In order to solve the problem of identifying the different degrees of pipeline blockage during the use of the thin feeding system,an improved recurrent neural network-Long and short-term memory(LSTM)algorithm based on complete ensemble empirical mode decomposition(CEEMD)was used to detect the blockage status of feeding pipes,that is,CEEMD was used to decompose the collected sound feedback signals in feeding pipes to obtain the intrinsic mode function(IMF)component.The energy proportion and approximate entropy were extracted from IMF components as feature vectors for constructing feature set M1.According to the characteristics of Pearson correlation coefficient and energy proportion,the IMF components with strong correlation and more information were selected to reconstruct the feature set M2.Then back-propagation network and RNN-LSTM algorithm model were used to classify and identify five working conditions of three-way pipeline,non-blocked pipeline,mildly blocked pipeline,moderately blocked pipeline and severely blocked pipeline.The results showed that the recognition accuracy of single feature was lower than that of multiple features,and the recognition accuracy after feature screening was higher than that without screening.Under the same conditions,the recognition accuracy of RNN-LSTM algorithm was significantly higher than that of back-propagation network in identifying the blockage status of feeding pipeline.The results indicated that RNN-LSTM algorithm model could effectively identify the different degrees of blockage in feeding pipeline,and could realize the prediction of pipeline blockage,which had certain practical application value.

关 键 词：饲喂管道 CEEMD RNN-LSTM 特征向量 能量占比 近似熵 分类识别 

分 类 号：S815.4[农业科学—畜牧学]