|本期目录/Table of Contents|

[1]张阔,李国勇,韩方阵.故障树法和改进PSO-PNN网络的电梯故障诊断模型[J].中国安全生产科学技术,2017,13(9):175-179.[doi:10.11731/j.issn.1673-193x.2017.09.028]
　ZHANG Kuo,LI Guoyong,HAN Fangzhen.Diagnosis model of elevator fault based on fault tree analysis and improved PSO-PNN network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(9):175-179.[doi:10.11731/j.issn.1673-193x.2017.09.028]
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故障树法和改进PSO-PNN网络的电梯故障诊断模型
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
13
期数:
2017年9期
页码:
175-179
栏目:
现代职业安全卫生管理与技术
出版日期:
2017-09-30

文章信息/Info

Title:
Diagnosis model of elevator fault based on fault tree analysis and improved PSO-PNN network
文章编号:
1673-193X(2017)-09-0175-05
作者:
张阔; 李国勇; 韩方阵
（太原理工大学 信息工程学院,山西 太原 030024）
Author(s):
ZHANG Kuo;  LI Guoyong;  HAN Fangzhen
(College of Information Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, China)
关键词:
故障树分析法; 粒子群算法; 概率神经网络; 平滑因子; 惯性权重; 电梯故障诊断
Keywords:
fault tree analysis;  particle swarm optimization (PSO);  probabilistic neural network (PNN);  smoothing factor;  inertia weight;  elevator fault diagnosis
分类号:
X913.4；TP277
DOI:
10.11731/j.issn.1673-193x.2017.09.028
文献标志码:
A
摘要:
针对电梯故障问题，提出一种将故障树分析法、改进的粒子群优化算法和概率神经网络相结合的方法用于电梯的故障诊断。以电梯的安全回路系统为例，用故障树法对回路进行分析，获得训练样本与故障类型；使用粒子群算法对概率神经网络的平滑因子进行优化，在优化过程中，针对粒子群算法存在易陷入局部最优的缺陷，提出对惯性权重的改进策略；采用相对误差对诊断效果做出评估，并与传统的概率神经网络和基本粒子群算法优化的概率神经网络在各种故障类型输出和最大相对误差等方面进行比较，结果表明：该模型能够有效诊断电梯故障。
Abstract:
Aiming at the problem that the elevator fault occur frequently, a new method combining fault tree analysis, improved particle swarm optimization (PSO) and probabilistic neural network (PNN) was proposed to diagnose the elevator fault. Taking the safety circuit system of elevator as example, the circuit was analyzed by the fault tree analysis, and the training samples and the fault types were obtained. The PSO algorithm was used to optimize the smoothing factor of PNN. In the optimization process, the improvement strategy of inertia weight was put forward according to the weakness of PSO which is easy to fall into the local optimum. The relative error was applied to evaluate the diagnosis effect, then the output of various fault types and the maximum relative error of this method were compared with the traditional PNN and the PNN optimized by the basic PSO. The results showed that this model can diagnose the elevator fault effectively.

参考文献/References:

［1］张莉，武昌俊，余丙荣，等．基于遗传小波神经网络的电梯故障诊断分析［J］．北华大学学报(自然科学版)，2012，13（2）：236-240． ZHANG Li, WU Changjun,Yu Bingrong,et al. Elevator fault diagnosis based on genetic wavelet neural network［J］. Journal of Beihua University (Natural), 2012,13(2): 236-240.
［2］蒋华安. 西门子可编程控制器在电梯远程监控系统中的应用［J］. 国内外机电一体化技术, 2002(6):47-48. JIANG Huaan. Application of programmable logic controller in elevator remote monitoring system［J］. Mechatronics Technology at Home and Abroad, 2002(6): 47-48.
［3］杨洋．电梯控制系统故障诊断专家系统的研究［D］．杭州：浙江工业大学，2003．
［4］冯鑫，夏洪山，冯月贵，等．基于遗传神经网络的电梯故障诊断技术研究［J］．起重运输机械，2017（1）：28-32． FENG Xin,XIA Hongshan,FENG Yuegui, et al.Study on elevator fault diagnosis technology based on genetic neural network［J］. Hoisting and Conveying Machinery, 2017(1): 28-32.
［5］宗群，李光宇，郭萌．基于故障树的电梯故障诊断专家系统设计［J］．控制工程，2013，20（2）：305-308． ZONG Qun,LI Guangyu,GUO Meng.Design of elevator fault diagnosis expert system based on fault tree［J］. Control Engineering, 2013,20(2): 305-308.
［6］李元贵，乐洋．专家系统在电梯故障诊断中的应用［J］．河海大学常州分校学报，2006，20（2）：50-52． LI Yuangui,LE Yang. Application of expert system in elevator fault diagnosis［J］. Journal of Hehai University(Changzhou Campus), 2006, 20(2): 50-52.
［7］杜碧华，张思宇，杨顺坤，等．基于故障树分析电梯门系统故障［J］．设备管理与维修，2015（3）：27-28． DU Bihua,ZHANG Siyu, YANG Shunkun,et al. Fault tree analysis of elevator door system［J］. Equipment Management and Maintenance, 2015(3): 27-28.
［8］彭钟华．基于遗传算法优化PNN的短期负荷预测［J］．电气开关，2017，55（1）：49-51, 56． PENG Zhonghua.Short term load forecasting based on genetic algorithm optimization［J］. PNN Electric Switch, 2017,55(1): 49-51,56.
［9］韩月娇. 粒子群算法的改进及其在BP神经网络中的应用［D］. 南昌航空大学, 2012. HAN Yuejiao. Improvement of particle swarm optimization and its application in BP neural networks ［D］. Nanchang University of Aeronautics, 2012.
［10］郭文忠. 离散粒子群优化算法及其应用［M］.北京：清华大学出版社, 2012.
［11］王小川，史峰，郁磊．MATLAB神经网络43个案例分析［M］．北京：北京航空航天大学出版社，2013．
［12］李佳琪, 王晓红, 孙军. 粒子群算法参数选择对动压滑动轴承优化设计的影响［J］. 机械设计, 2014(3):55-59. LI Jiaqi,WANG Xiaohong,SUN Jun. Influence of parameter selection of particle swarm optimization algorithm on optimal design of hydrodynamic journal bearings［J］. Mechanical Design, 2014(3): 55-59.
［13］叶小岭，施珮，匡亮．基于粒子群优化BP神经网络的台风灾损预测模型研究［J］．灾害学，2013，28（4）：11-15, 21． YE Xiaoling,SHI Pei, KUANG Liang. The typhoon disaster damage particle swarm optimization BP neural network prediction model of［J］. Based on Disaster Science, 2013, 28(4): 11-15,21.
［14］王炳萱，李国勇，王艳晖．基于LM-PSO算法和BP神经网络的非线性预测控制［J］．太原理工大学学报，2016，47（2）：207-211． WANG Bingxuan, LI Guoyong, WANG Yanhui. Nonlinear predictive control based on LM-PSO algorithm and BP neural network［J］. Journal of Taiyuan University of Technology,2016,47(2):207-211.

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备注/Memo

备注/Memo:
国家自然科学基金项目（51075291）
更新日期/Last Update: 2017-10-12