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摘 要
I
混凝土搅拌站螺旋输送电机故障智能诊断研究
摘 要
继《中华人民共和国环境保护税法》等相关法规推出后,许多小型混凝土厂商和大
量的简易混凝土生产设备被逐渐淘汰。规模更大、自动化和集成化程度更高的混凝土搅
拌站已经成为主流的发展方向。螺旋输送电机作为搅拌站最重要的物料输送设备之一,
由于其工作环境恶劣、工作强度大,可靠性和稳定性很难得到保障。
本文采用试验法对螺旋输送电机的智能诊断方法进行研究。首先根据其实际工作需
求,运用 MATLAB/Simulink 建立了电机矢量控制模型。然后在此基础上对电机的主要
故障形式进行仿真分析,并根据电机故障信号的特点,提出利用小波分解法提取电机的
故障特征参数。接着采用 PCA(主元分析)法选取电机的主要故障特征参数,确定故障
样本。最后结合 BP 神经网络和遗传算法的优点,建立了 AGA-BP 神经网络故障诊断模
型,并利用采集的样本数据进行试验验证。试验结果显示该模型的诊断精度为 73.3%,
表明本文设计的诊断模型具有良好的故障诊断能力。通过本研究可以为将来混凝土搅拌
站智能化控制系统的设计提供有效的技术支撑。
关键词:混凝土搅拌站;螺旋输送电机;智能诊断;MATLAB/Simulink 仿真;PCA; AGA-
BP 神经网络
华北水利水电大学硕士学位论文
II
ABSTRACT
III
THE STUDY OF INTELLIGENT DIAGNOSIS
OF SPIRAL TRANSPORT MOTOR IN
CONCRETE MIXING STATION
ABSTRACT
After the introduction of the environmental protection tax law of the people's Republic of
China and other relevant laws and regulations, many small concrete manufacturers and a large
number of simple concrete production equipment has been phased out. The concrete mixing
plant with larger scale, higher automation and integration has become the mainstream
development direction. As one of the most important material conveying equipments in the
mixing station, the spiral conveyor is difficult to guarantee the reliability and stability, because
of its poor working environment and high working intensity.
In this paper, the intelligent diagnosis method of spiral conveyor is studied by test method.
Firstly, according to its actual working needs, the motor vector control model is established by
using MATLAB/Simulink. Nextly, the main fault forms of the motor are simulated and analyzed.
According to the characteristics of the motor fault signal, the wavelet decomposition method is
proposed to extract the fault characteristic parameters of the motor. Then PCA (principal
component analysis) method is used to select the main fault characteristic parameters of the
motor and determine the fault samples. Finally, combining the advantages of BP neural network
and genetic algorithm, the AGA-BP neural network fault diagnosis model is established, and
the sample data collected are used to verify the test. The test results show that the diagnosis
accuracy of the model is 73.3%, which indicates that the model designed in this paper has good
fault diagnosis ability. Through this study, it provides effective technical support for the design
of intelligent control system of concrete mixing station in the future.
KEY WORDS: Concrete batching plant; Spiral conveyor; Intelligent diagnosis;
MATLAB/Simulink simulation; PCA; AGA-BP Neural Network
华北水利水电大学硕士学位论文
IV
目 录
V
目 录
摘 要 ................................................................................................................................... I
ABSTRACT ..................................................................................................................... III
目 录 ............................................................................................................................... V
1 绪论 ................................................................................................................................ 1
1.1 研究背景与意义 ....................................................................................................... 1
1.2 国内外研究现状 ....................................................................................................... 1
1.2.1 混凝土搅拌站发展现状 .................................................................................... 1
1.2.2 智能故障诊断技术研究现状 ............................................................................ 2
1.2.3 电机故障诊断技术研究现状 ............................................................................ 3
1.2.4 小波理论在电机故障诊断领域的应用现状 .................................................... 4
1.3 研究内容 ................................................................................................................... 5
1.4 技术路线 ................................................................................................................... 5
2 螺旋输送电机故障信号采集 ........................................................................................ 7
2.1 电机仿真模型设计 ................................................................................................... 7
2.1.1 MATLAB/Simulink 功能简介 ............................................................................ 7
2.1.2 电机控制方案的确定 ........................................................................................ 7
2.1.3 电机矢量控制模型设计 .................................................................................... 9
2.2 电机故障模式分析 ................................................................................................. 14
2.3 电机故障信号采集 ................................................................................................. 16
2.3.1 电机正常运行状态仿真 .................................................................................. 16
2.3.2 电源欠压故障仿真 .......................................................................................... 18
2.3.3 定子短路故障仿真 .......................................................................................... 19
2.3.4 电机过载(堵转)仿真 .................................................................................. 20
2.3.5 逆变器故障仿真 .............................................................................................. 22
2.4 本章小结 ................................................................................................................. 25
3 基于小波分解的电机故障特征提取 .......................................................................... 27
3.1 小波变换理论分析 ................................................................................................. 27
3.1.1 小波定义 .......................................................................................................... 27
3.1.2 小波变换原理 .................................................................................................. 28
3.2 小波函数的选择 ..................................................................................................... 28
3.2.1 小波基的选择标准 .......................................................................................... 29
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