VP型宽频带倾斜仪故障信号的BBA-SOM智能诊断  

Determination of Troubles for VP-type Broadband Inclinometer Using CEEMD Multi-scale Approximate Entropy and BBA-SOM Model

作　　者：马武刚[1,2] 庞聪 龚燕民[3] 刘晓磊 MA Wu-gang;PANG Cong;GONG Yan-min;LIU Xiao-lei(Institute of Seismology,CEA,Wuhan 430071,China;Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station,Wuhan 430071,China;Yixian Seismic Station,Hebei Earthquake Agency,Yixian 074200,China)

机构地区：[1]中国地震局地震研究所,武汉430071 [2]武汉引力与固体潮国家野外科学观测研究站,武汉430071 [3]河北省地震局易县地震台,易县074200

出　　处：《科学技术与工程》2023年第14期6012-6017,共6页Science Technology and Engineering

基　　金：中国地震局地震研究所和应急管理部国家自然灾害防治研究院基本科研业务费专项(IS202226321;IS202236328);武汉引力与固体潮国家野外科学观测研究站开放基金(WHYWZ202107);湖北省自然科学基金(2019CFB768);地震科技星火计划攻关项目(XH15030);中国地震局监测、预报、科研三结合课题(3JH-202201024)。

摘　　要：针对现有VP型倾斜仪故障诊断主要依靠人工经验和诊断流程较为复杂的问题,提出以互补集合经验模态分解(complete ensemble empirical mode decomposition,CEEMD)多尺度近似熵和二进制蝙蝠算法(binary bat algorithm,BBA)优化SOM神经网络参数的VP型倾斜仪故障诊断新方。首先,将归一化后的仪器故障信号进行CEEMD分解,对6阶本征模态函数(intrinsic mode function,IMF)求取多尺度近似熵值;然后将网络输入法按比例分为训练集和测试集,以训练集的识别率为适应度函数,应用二进制蝙蝠算法(binary bat algorithm,BBA)优化SOM神经网络的竞争层维数和网络训练次数;最后应用上述得到的BBA-SOM网络模型对倾斜仪故障特征数据进行辨识。实验表明:CEEMD多尺度近似熵判据对倾斜仪故障特征的区分效果符合预期;相对于朴素贝叶斯、AdaBoost集成学习与LDA等学习模型,BBA-SOM模型可以准确进行故障诊断;该方法对实现VP型倾斜仪故障的自动诊断有重要现实意义。Aiming at the problem that the fault diagnosis of the existing VP-type inclinometer mainly relies on manual experience and the diagnosis process is relatively complex,a complementary ensemble empirical mode decomposition(CEEMD)multi-scale approximate entropy and binary bat algorithm(BBA)optimization were proposed.A new method for fault diagnosis of VP-type inclinometers based on SOM neural network parameters.First,the normalized instrument fault signal was decomposed by CEEMD,and the multi-scale approximate entropy value was obtained for the sixth-order eigenmode function(IMF).Then the network input is divided into training set and test set proportionally to train.The recognition rate of the set is a fitness function,and the binary bat algorithm(BBA)was used to optimize the competition layer dimension and network training times of the SOM neural network.Finally,the BBA-SOM network model obtained above was used to identify the fault characteristic data of the inclinometer.Experiments show that the CEEMD multi-scale approximate entropy criterion can distinguish the fault features of the inclinometer as expected.Compared with the learning models such as Naive Bayes,AdaBoost ensemble learning and LDA,the BBA-SOM model can accurately diagnose faults.The automatic diagnosis of VP-type inclinometer fault has important practical significance.

关 键 词：VP宽频带倾斜仪 故障诊断 互补集合经验模态分解 二进制蝙蝠算法 自组织特征映射神经网络 

分 类 号：TH761[机械工程—仪器科学与技术] TP301.6[机械工程—精密仪器及机械]