粒子群优化的SVM提升钢丝绳故障诊断

黄帅; 吴娟; 李琳琳; 李鑫鑫

doi:10.13433/j.cnki.1003-8728.20190121

粒子群优化的SVM提升钢丝绳故障诊断

doi: 10.13433/j.cnki.1003-8728.20190121

黄帅^1,2,3,,
吴娟^1,2,3, ,,
李琳琳^1,2,3,
李鑫鑫^1,2,3

1.
太原理工大学机械与运载工程学院, 太原 030024
2.
矿山流体控制国家地方联合工程实验室, 太原 030024
3.
山西省矿山流体控制工程技术研究中心, 太原 030024

基金项目:

国家重点研发计划重点专项项目 2016YFC0600908

详细信息

作者简介:
黄帅(1993-), 硕士研究生, 研究方向为机电液一体化, 19935112407@163.com

通讯作者:
吴娟, 教授, 博士, wujuanz@163.com

中图分类号: TD532
计量
- 文章访问数: 172
- HTML全文浏览量: 49
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2019-01-11
- 刊出日期: 2020-02-05

Fault Detection of Hoisting Wireope with SVM Optimized by PSO

Huang Shuai^{1,2,3
,},
Wu Juan^{1,2,3
, ,},
Li Linlin^1,2,3,
Li Xinxin^1,2,3

1.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
2.
National-local Joint Laboratory of Mining Fluid Control Engineering, Taiyuan 030024, China
3.
Shanxi Research Center of Mining Fluid Control Engineering, Taiyuan, 030024, China

摘要

摘要: 为了加强多绳摩擦提升系统的提升钢丝绳故障与提升钢丝绳张力之间的联系，提出了基于支持向量机（SVM）以及最小二乘支持向量机（LSSVM）的诊断模型。在MATLAB中应用粒子群优化算法（PSO）对模型参数进行优化，得到具有最优参数的支持向量机诊断模型；在某矿的提升系统进行三种故障及正常状态试验，利用东方所INV3060S采集仪获得的钢丝绳故障及正常状态数据对PSO-SVM以及PSO-LSSVM进行训练以及预测，结果显示PSO-SVM的运算结果的误差及均方误差较小，PSO-LSSVM的运算速度较快，且两种算法都能有较好的故障诊断能力。
- 故障诊断 /
- 钢丝绳 /
- 多绳摩擦提升系统 /
- 支持向量机(SVM) /
- 最小二乘支持向量机(LSSVM) /
- 粒子群优化算法(PSO) /
- MATLAB /
Abstract: To strengthen the relationship between the hoisting wirerope fault and the hoisting wirerope tension of multi-rope friction lifting system, a fault detection model based on support vector machine(SVM) and least squares support vector machine(LSSVM) was proposed. The particle swarm optimization algorithm (PSO) was used to optimize the model parameters to obtain the support vector machine diagnostic model with optimal parameters in MATLAB. Three kinds of faults and normal state tests were carried out in the lifting system of a mine. The PSO-SVM and PSO-LSSVM were trained and predicted by the wirerope fault data. The results showed the error and the mean square error of the PSO-SVM is smaller, and the PSO-LSSVM is faster, and both algorithms have good detection capabilities.
- fault detection /
- multi-rope friction lifting system /
- support vector machine (SVM) /
- particle swarm optimization (PSO) /
- MATLAB /
- algorithms /
- fault diagnostic model /
- test

HTML全文

图 1 PSO-SVM流程图

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图 2 PSO-SVM算法的适应度曲线

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图 3 PSO-LSSVM算法的适应度曲线

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图 4 PSO-SVM法和PSO-LSSVM法预测结果

下载: 全尺寸图片幻灯片

表 1 张力差数据库

数据序号	钢丝绳序号				故障类别
数据序号	1	2	3	4	故障类别
1	0.439	0.453	0.155	0.201	1
2	0.804	0.437	0.418	0.182	2
3	0.749	0.510	0.342	0.234	3
4	0.973	0.518	0.627	0.227	4
5	0.430	0.435	0.153	0.191	1
6	0.742	0.453	0.421	0.192	2
7	0.801	0.404	0.309	0.186	3
8	0.942	0.421	0.583	0.169	4
9	0.460	0.464	0.168	0.202	1
10	0.739	0.453	0.411	0.192	2
11	0.807	0.554	0.364	0.251	3
12	0.946	0.604	0.531	0.115	4
13	0.450	0.438	0.152	0.192	1
14	0.741	0.352	0.382	0.146	2
15	0.929	0.541	0.354	0.247	3
16	0.930	0.588	0.591	0.133	4
17	0.466	0.463	0.161	0.202	1
18	0.731	0.445	0.427	0.183	2
19	0.810	0.527	0.382	0.217	3
20	0.965	0.609	0.644	0.311	4
21	0.523	0.489	0.281	0.221	1
22	0.809	0.455	0.389	0.256	2
23	0.859	0.632	0.399	0.302	3
24	0.998	0.686	0.596	0.299	4
注:故障类别1、2、3、4分别代表了钢丝绳的正常、卡罐、过卷以及打滑状态。

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表 2 运行结果比较

算法	最大预测误差绝对值	均方误差	运行时间/s
PSO-SVM	0.031 55	6.904 0×10^-4	3.420 404
PSO-LSSVM	0.414 574	0.022 0	0.246 795

下载: 导出CSV

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