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工程设计学报
Chin J Eng Design  2023, Vol. 30 Issue (1): 109-116    DOI: 10.3785/j.issn.1006-754X.2023.00.002
Whole Machine and System Design     
Design of cable tunnel fault warning system based on MSSA-SVM
Chao JI1(),Liang WANG1,Xiao-jing WANG2,Xiao-bing LI2,Wen CAO1
1.School of Electronic Information, Xi'an Polytechnic University, Xi'an 710600, China
2.Xi'an Jinpower Electrical Co. , Ltd. , Xi'an 710075, China
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Abstract  

In order to realize online monitoring and fault alarm of cable tunnel environment and improve the intelligent level of cable tunnel monitoring system, a fault warning system based on multi-feature sparrow search algorithm (MSSA) optimized support vector machines (SVM) was proposed. Firstly, the fault data set was preprocessed normalized; secondly, a multi-class SVM model was established, and MSSA was used to optimize the parameters of the SVM, so as to establish the MSSA-SVM model. The trained MSSA-SVM model was embedded in the database server of the fault warning system, and the real-time collected data was monitored and diagnosed online, and the alarm was given in time; finally, the effectiveness of MSSA-SVM model was verified by experiments, and it was compared with sparrow search algorithm (SSA), grey wolf optimization (GWO) and particle swarm optimization (PSO). The experimental results showed that MSSA-SVM model has the highest fault recognition accuracy, and its recognition accuracy could reach 95%. The research result provides a reference for effectively improving the intelligence and accuracy of online monitoring of cable tunnels.

Key words： cable tunnel      monitoring system      support vector machine      fault diagnosis      multi-feature sparrow search algorithm     
Received: 21 March 2022      Published: 06 March 2023
CLC:  TM 712  
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Chao JI
Liang WANG
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Xiao-bing LI
Wen CAO
Cite this article:

Chao JI,Liang WANG,Xiao-jing WANG,Xiao-bing LI,Wen CAO. Design of cable tunnel fault warning system based on MSSA-SVM. Chin J Eng Design, 2023, 30(1): 109-116.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2023.00.002     OR     https://www.zjujournals.com/gcsjxb/Y2023/V30/I1/109


基于MSSA-SVM的电缆隧道故障预警系统设计

为了实现电缆隧道环境的在线监测和故障报警，提高电缆隧道监测系统的智能化水平，提出了一种基于多特征麻雀搜索算法（multi-feature modified sparrow search algorithm, MSSA）优化支持向量机（support vector machines, SVM）的故障预警系统。首先，对故障数据集进行归一化预处理；其次，建立多分类SVM模型，用MSSA对SVM进行参数寻优，从而建立MSSA-SVM模型，并将训练好的MSSA-SVM模型嵌入故障预警系统的数据库服务器中，对实时采集的数据进行在线监测、诊断，并及时报警；最后，通过实验验证了MSSA-SVM模型的有效性，并将其与麻雀搜索算法（sparrow search algorithm, SSA）、灰狼优化算法（grey wolf optimization, GWO）和粒子群算法（particle swarm optimization, PSO）进行对照实验，实验结果表明，MSSA-SVM模型的故障识别准确率最高，其识别准确率可达95%。研究结果为有效提高电缆隧道在线监测的智能性和准确性提供了参考。


关键词： 电缆隧道,  监测系统,  支持向量机,  故障诊断,  多特征麻雀搜索算法 
Fig.1 Framework of online monitoring system for cable tunnel
Fig.2 Functional modular design of monitoring center
Fig.3 Fault warning process
Fig.4 Optimization process based on MSSA-SVM
Fig.5 Device of cable tunnel fault warning experiment
Fig.6 Data acquisition control terminal
故障标签故障类型训练样本数量/组测试样本数量/组
1水灾3510
2火灾3510
3有毒气4010
4内部潮湿4515
5氧气不足4515
Table 1 Distribution of fault samples
Fig.7 Fitness curve of MSSA-SVM model
Fig.8 Fault recognition results of MSSA-SVM model
Fig.9 Fault recognition results of the SSA-SVM, GWO-SVM and PSO-SVM models
Fig.10 Comparison of fault recognition accuracy and running time of each model
Fig.11 Comparison of recognition accuracy of various kinds of faults
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