【作者】 赵晓东；

【导师】 李志刚；

【作者基本信息】 河北工业大学 ， 电机与电器， 2011， 博士

【摘要】 电机运行的正常与否直接影响着工矿企业以及日常生活的正常运转。如果电机质量不高,频繁出现故障,不仅会影响生产的运行,还会影响经济的快速发展,造成严重的经济损失和恶劣的社会影响。电机转子是电机中的运动部件,在运行过程中受到轴向电磁力、离心力和热弯曲扰度力等的作用,因此在电机的运行过程中转子故障是主要的故障。本文从几个方面入手,研究了电机转子在制造以及使用过程中的检测方法及故障诊断技术,本文的研究成果一方面可以为提高电机出厂质量,延长电机的使用寿命,另一方面可以分析电机的运行状态,在故障的前期进行预警,防止因突发故障给生产生活带来巨大损失。转子制作过程受到诸多因素的影响,其中线圈抽头至换向片的虚焊问题尤为严重且较为普遍,由此引发的质量隐患通常在电机的生产过程中难以发现,更多的表现在装机出厂交付使用后随时可能发生的局部发热、断线等故障导致的电机烧毁。本文在建立电机转子电路模型及数学模型的基础上,研究了电机转子片间电阻、跨间电阻、焊接电阻的测量及计算方法,设计了电机转子直流电阻测量电路,设计了电机转子诊断智能测试系统。电机出厂之前对转子进行全面的电气性能检测与分析,可以防止不良产品流入市场,提高电机出厂质量,延长电机的使用寿命。电机转子绕组匝间短路故障是电机常见故障之一,本文在分析电机转子绕组匝间短路故障机理的基础上,设计了基于DSP的高速数据处理系统,采集了气隙电动势信号。利用小波变换,去除该信号中的主磁通干扰信号,分别将正常信号和故障信号进行4层小波包变换,提取重构后的16个能量特征信息并进行归一化处理,作为神经网络的输入向量,为建立人工智能的故障检测提供数据支持,应用小波包分解提取后的能量信息值作为神经网络的输入向量,故障类型作为网络的输出向量,建立了电机故障诊断系统的诊断过程,通过分别利用不同的数据进行网络训练和测试,实现了电机转子绕组匝间短路的故障诊断。转子断条故障即转子导条断裂是电动机常见故障之一。本文在分析电动机转子断条故障的诊断机理基础上,使用小波包分析电机转子断条仿真信号,确定断条的故障特征信号,结合FFT变换对重构的结点系数进行频谱分析,得到故障信号的特征频率,然后运用小波分析确定电机转子断条故障发生的具体时刻,最后经过试验证明了该方法的可行性。更多还原

【Abstract】 The quality of motor directly influence the competition position of motor manufacturing enterprise in market, but armature is the main component of motor, and its faults are the main faults in the process of motor running. If the motor is often damaged and destructed, the system will not work in order and get in trouble. It will also affect the rapid economic development, causing serious economic losses and adverse social impact. Rotor is the moving parts of the motor, motor often suffered axial electromagnetic force, centrifugal force and hot bending flexibility in the running process, so the rotor fault is the major fault. The detection method and fault diagnosis technology of rotor is researched by several aspects in this article; the research results can improve the quality of the motor and extend the life of the motor. The motor can be analyzed operating state in the pre-fault early warning.Rotors are subjected many factors in manufacturing process, including the coil tap to the conversion to film a particularly serious problem, which is uually caused quality problems in the production process and it is difficult to find. This testing system could experimentize and determinate to the basic electrical performance of motor armature, for instance, welding resistance, bar to bar resistance, leap resistance, industrial frequency withstand voltage, inter-turn withstand voltage, dc voltage withstand and so on. The paper designs the rotor DC resistance measuring circuit and diagnostic intelligence test rotor system. Rotor will have a full electrical performance test before the motor factory and prevent defective products from entering the market; it will improve the quality of the motor factory and extend the motor life.The rotor winding inter turn short circuit fault is one of the common faults, which is of destruction to the machines. The paper analyze the rotor winding inter turn short circuit fault mechanism and design high speed data processing system based on DSP. The system can collect the air gap EMF signal. Wavelet transform can remove the main magnetic flux interference signals from the air gap EMF signal, the normal and fault signals have 4 layers wavelet packet transform to extract the reconstructed energy feature information. After the wavelet packet transform, we get 16 energy feature information as the neural network the input vector for the establishment of artificial intelligence to provide data to support fault detection. Wavelet packet decomposition of the energy information extracted value as neural network input vector, fault type as the network output vector, and then establish a motor fault diagnosis system. In the diagnostic process, different data were used to train and test the network to achieve the rotor winding inter turn short circuit fault diagnosis.Rotor fault analysis, that is the motor rotor bar fault, is one of the common faults. This paper analyzes broken bar of the motor fault diagnosis mechanism, which is based on wavelet packet analysis. Use the wavelet packet to simulate of the signal rotor broken bars and determine the fault feature information.Use the nodes of FFT transform coefficients to reconstruct the spectrum analysis, then we can get the characteristic frequency of fault signal. Use wavelet analysis to determine fault specific time with broken bar of induction motor. The simulated experimental results show that the diagnosis method, a quicker diagnosis and a higher accuracy, is feasible.更多还原