智能故障诊断技术在中频冶炼中的应用
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摘要
中频冶炼技术近年来作为一门新兴技术,在淬火、熔炼、透热工业领域中得到广泛运用。随着现代集成化感应加热技术日益趋于成熟,中频冶炼自动化水平的不断提高,中频冶炼系统的可靠、稳定、安全运行也越来越重要。
本文主要是通过对中频冶炼系统主电路电压信号进行监测从而实现对变频器短路故障进行智能故障诊断的研究。发生短路故障的变频器电压信号是非平稳随机信号,以往的傅里叶变换难以满足故障特征向量提取要求,并且建立变频器故障数学模型比较复杂,基于以上原因本文提出了基于小波包变换和PSO神经网络的中频冶炼故障诊断方法,通过小波包提取故障特征向量,利用PSO神经网络进行故障识别,并输出系统相应故障类型。
该故障诊断方法首先是通过利用MATLAB软件对中频冶炼主电路进行建模、仿真,模拟了主电路中晶闸管开路故障,获得了相应的故障波形。其次,当一个系统或电路发生故障时,测试点测得的电压或电流与正常情况下相同频带内能量值是不一样的,根据能量守恒定律,某一频带能量低,另一频带能量值可能就高,信号每个频带的能量值是体现故障状态一个有效信息。本文使用的小波包算法就是对信号进行系数分解,得到一组能量形式的故障特征向量;最后,本文主要对变频器三大类短路故障状态进行测试:无故障,一只晶闸管短路故障、两只晶闸管短路故障,设计了相应的故障信号采集系统和试验方案,利用已经得到的特征向量和三大类故障状态设计并训练PSO神经网络。通过MATLAB平台实现相应的算法。
本文主要是通过仿真实验验证相应故障诊断算法的准确性和有效性。通过仿真模拟故障信号,再对信号分别进行采集和测试,使用小波包算法对故障信号进行特征提取得到故障特征向量;利用已知故障类型的样本对PSO神经网络进行训练,然后用测试信号对训练好的神经网络进行测试。试验结果显示输出故障类型与实际测试信号所对应的故障状态一致。结果证明了基于小波包变换和PSO神经网络智能故障诊断方法对中频冶炼这样随机性和模糊性很强的系统切实可行,收敛速度快准确度高,减少了企业的经济损失,具有很大的运用价值。
In recent years, intermediate frequency smelting technology as an emerging technology has been widely used in quenching, melting, heat penetrating industrial areas. As the automation levels rising, reliable, stable and safe operation is becoming more and more important to intermediate frequency smelting system.
The fault diagnosis of intermediate frequency smelting system is researched by monitoring main circuit voltage signals. The short-circuited fault signals of converter are random and unstable. Fourier Transform can’t meet the unsteady signal feature vector extraction requirements. At the same time, establishing a mathematical model of inverter fault is more complex. For this reason, the fault diagnosis method of inverter based on wavelet packet analysis and PSO neural network is put forward in this paper. Wavelet packet is used to extract the feature vector from fault signals. The PSO neural network is used to identify the feature vector and output the corresponding fault type of the system.
First, the method of intermediate frequency smelting fault diagnosis obtains corresponding fault waveform through MATLAB software to build a simulation model.
Secondly, when circuit in fault conditions, energy value of the voltage test point is different in the same band. This is the so-called law of conservation of energy. If a band energy is low ,another band energy may be high. Anyhow, various changes of band energy embody the fault characteristics. The algorithm of wavelet packet strikes the energy of frequency band and gains the open circuit fault characteristics from various changes in each energy frequency band. Finally, this paper focuses on testing five types of short-circuit fault state of the converter: no-power tube failure, single power tube failure, two power tube failure in the same bridge arm, two power tube failure in the same half bridge arm, cross two power tube failure. After finishing feature extraction, PSO neural network is established by setting initial value and training neural network. The algorithms in this paper are realized with MATLAB software.
The simulation experiment verifies the accuracy and validity of the fault diagnosis algorithm. The samples and testing signals are collected separately by simulating. Fault feature vectors are obtained by wavelet packet analysis which are off-line analysis. Neural network based on PSO is trained by sample eigenvectors, and tested by testing eigenvectors after the successful training. Test results show that the output fault type accords with the corresponding actual fault state of the test signals. The simulation experiment results prove that PSO neural network intelligent fault diagnosis method is practicable and has the advantage of fast convergence and high accuracy
本文主要是通过对中频冶炼系统主电路电压信号进行监测从而实现对变频器短路故障进行智能故障诊断的研究。发生短路故障的变频器电压信号是非平稳随机信号,以往的傅里叶变换难以满足故障特征向量提取要求,并且建立变频器故障数学模型比较复杂,基于以上原因本文提出了基于小波包变换和PSO神经网络的中频冶炼故障诊断方法,通过小波包提取故障特征向量,利用PSO神经网络进行故障识别,并输出系统相应故障类型。
该故障诊断方法首先是通过利用MATLAB软件对中频冶炼主电路进行建模、仿真,模拟了主电路中晶闸管开路故障,获得了相应的故障波形。其次,当一个系统或电路发生故障时,测试点测得的电压或电流与正常情况下相同频带内能量值是不一样的,根据能量守恒定律,某一频带能量低,另一频带能量值可能就高,信号每个频带的能量值是体现故障状态一个有效信息。本文使用的小波包算法就是对信号进行系数分解,得到一组能量形式的故障特征向量;最后,本文主要对变频器三大类短路故障状态进行测试:无故障,一只晶闸管短路故障、两只晶闸管短路故障,设计了相应的故障信号采集系统和试验方案,利用已经得到的特征向量和三大类故障状态设计并训练PSO神经网络。通过MATLAB平台实现相应的算法。
本文主要是通过仿真实验验证相应故障诊断算法的准确性和有效性。通过仿真模拟故障信号,再对信号分别进行采集和测试,使用小波包算法对故障信号进行特征提取得到故障特征向量;利用已知故障类型的样本对PSO神经网络进行训练,然后用测试信号对训练好的神经网络进行测试。试验结果显示输出故障类型与实际测试信号所对应的故障状态一致。结果证明了基于小波包变换和PSO神经网络智能故障诊断方法对中频冶炼这样随机性和模糊性很强的系统切实可行,收敛速度快准确度高,减少了企业的经济损失,具有很大的运用价值。
In recent years, intermediate frequency smelting technology as an emerging technology has been widely used in quenching, melting, heat penetrating industrial areas. As the automation levels rising, reliable, stable and safe operation is becoming more and more important to intermediate frequency smelting system.
The fault diagnosis of intermediate frequency smelting system is researched by monitoring main circuit voltage signals. The short-circuited fault signals of converter are random and unstable. Fourier Transform can’t meet the unsteady signal feature vector extraction requirements. At the same time, establishing a mathematical model of inverter fault is more complex. For this reason, the fault diagnosis method of inverter based on wavelet packet analysis and PSO neural network is put forward in this paper. Wavelet packet is used to extract the feature vector from fault signals. The PSO neural network is used to identify the feature vector and output the corresponding fault type of the system.
First, the method of intermediate frequency smelting fault diagnosis obtains corresponding fault waveform through MATLAB software to build a simulation model.
Secondly, when circuit in fault conditions, energy value of the voltage test point is different in the same band. This is the so-called law of conservation of energy. If a band energy is low ,another band energy may be high. Anyhow, various changes of band energy embody the fault characteristics. The algorithm of wavelet packet strikes the energy of frequency band and gains the open circuit fault characteristics from various changes in each energy frequency band. Finally, this paper focuses on testing five types of short-circuit fault state of the converter: no-power tube failure, single power tube failure, two power tube failure in the same bridge arm, two power tube failure in the same half bridge arm, cross two power tube failure. After finishing feature extraction, PSO neural network is established by setting initial value and training neural network. The algorithms in this paper are realized with MATLAB software.
The simulation experiment verifies the accuracy and validity of the fault diagnosis algorithm. The samples and testing signals are collected separately by simulating. Fault feature vectors are obtained by wavelet packet analysis which are off-line analysis. Neural network based on PSO is trained by sample eigenvectors, and tested by testing eigenvectors after the successful training. Test results show that the output fault type accords with the corresponding actual fault state of the test signals. The simulation experiment results prove that PSO neural network intelligent fault diagnosis method is practicable and has the advantage of fast convergence and high accuracy
引文
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[23]吴小华,史忠科.三相SPWM逆变电源故障检测与诊断的仿真研究.系统仿真学报, 2004, 16(7): 21-24
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[29]李爱红.小波包神经网络在数控机床故障诊断中的应用.矿煤机械, 2010, 31(12): 211-213
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[2]马天兵,杜菲.变频器故障诊断技术及其发展趋势.工矿自动化, 2007, 32(3): 32-34
[3] M.Y LI.,R CHEN.On-line fault diagnosis system for high-power controlled rectifier based on improved frequency analysis method.Proceedings of the CSEE,2004,24(10):162-167(in Chinese)
[4]王阳,刘红彦.频谱分析在齿轮故障诊断中的应用.石油化工设备, 2010, 3(6): 31-33
[5]申大勇.频谱分析法在齿轮箱故障诊断中的应用.石油化工设备, 2010, 13(6): 34-35
[6]许允之.基于频谱分析的电动机故障诊断试验台研究.试验技术与管理, 2010, 27(8): 81-83
[7]何宗旺.振动频谱分析在滚动轴承故障诊断中的应用.现代零部件, 2010, s1(l): 50-53
[8]催景泰.小波分析导论.西安:西安交通大学出版社, 1997: 36-39
[9]虞和济.基于神经网络的故障智能诊断.北京:冶金工业出版社, 2000: 56-72
[10]陈如清.大功率电力电子装置在线故障诊断. [西安理工大学硕士论文], 2004: 5-8
[11] Z.J.WANG, N.HAN,J.WANG,et al.The Study on the Fuzzy Control Algorithm for Main Steam Pressure Control of Circulated Fluidized Bed Boiler.ICEEAC,Zi Bo,2010
[12]李森林,邓小武.基于二参数的BP神经网络算法改进与应用.河北科技大学学报, 2010, 31(5) :447-450
[13]张丽平.粒子群优化算法的理论及实践. [浙江大学博士论文], 2005: 5-7
[14] ANTHONY CARLISLE,GERRY DOZIER. An off-The-Shelf PSO. Proceedings of the 2001 Workshop on Particle Swarm Optimization,2001,12(5):l-6
[15] Q.R.LIU, Z.J.WANG ,N.HAN,et al.The Research of Measurement System Based on A New True Effective Value.International Conference on Electrical and Control Engineering,Wu Han,2010
[16] CLERC M AND KENNEDY J. The Particle Swarm-explosion,Stability and Convergence in a Multidimensional Complex Space. IEEE Transaction Evoluate Computation 2002, 6 (2):58-73
[17] ZAVALA A E M,AGUIRRE A H,DIHARCE E R V. Partiele Evolutionary Swarm Optimization Algorithm(PESO).Sixth Mexican International Conference on Computer science,2005,26(9):282-259
[18]蔡金锭,涂娟.电力电子电路故障的遗传进化神经网络诊断.高电压技术,2004,30(9):3-5
[19]郭国旗,韩田辉,邓宏运.中频感应炉控制系统和铸钢的快速熔炼技术.铸造设备研究, 2006,30(5): 43-48
[20]张智星. MATLAB程序设计与应用.北京:清华大学出版社, 2002: 26-32
[21]郑连清,邹涛,娄洪立.电力电子主电路故障诊断方法研究.高电压技术,2006,32(3):84-87
[22]刘庆珍,蔡金锭,王少芳.基于粗糙集一神经网络系统的电力电子电路故障诊断.电力自动化设备, 2004, 24(4): 45-48
[23]吴小华,史忠科.三相SPWM逆变电源故障检测与诊断的仿真研究.系统仿真学报, 2004, 16(7): 21-24
[24] H MA,LEE Y S.Fault diagnosis of power electronic circuits based on neural network and waveform analysis.The IEEE 1999 International Conference on Power Electronics and Drive Systems,Hong Kong,1999
[25]秦前清,杨宗凯.实用小波分析.西安电子科技大学出版社, 1998: 46-55
[26] Q.j.Guo,H.b.Yu,A.d.Xu. Hybrid PSO Based Wavelet Neural Networks for Intelligent Fault Diagnosis.Advances in Neural Networks,2005,34(98):521-530
[27]王军锋,张维强,宋国乡.模拟电路故障诊断的多小波神经网络算法.电工技术学报, 2006, 21(l):33-36
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