非侵入性的电动机的转子条的故障诊断问题属于难问题

1 IntroductionThe problem of non-invasive diagnosis of the faults of rotor bars in the induction motor belongs to difficult problems 5, 7. Motor with this type of damage is still functioning but such work causes increased currents at both sides of the broken bar. This causes the damage of the next bars. The result of this avalanche process is the damage of all bars, and finally the machine stops functioning. Thus,the most important problem is to catch the moment when only one or at most few bars are broken (the beginning of the avalanche process). There has been a lot of research reported over the past years devoted to the development of various steady state condition monitoring techniques. Most of them use Fourier transformation of the stator current in a steady state 4, 14, 19. Some apply more sophisticated method of wavelet analysis of stator current in transient state 18. There are also solutions relying on the analysis of the magnetic field space vector orientation 13. All these methods of current preprocessing are combined with different tools of analysis of the results of this preprocessing stage, forming the final classification stage. Among them,we can mention the statistical approach to classification 4, 6, 8, the artificial neural networks 5, 9, 17 or support vector machine 1, 3, 10. They are responsible for the automatic recognition of fault. The observation of motors working in the normal state or at faulty conditions of the bars allows to point out some typical symptoms indicating the bar faults 46. To the most important belong changes of the harmonic spectrum in the phase currents and voltages, change of the shaft flux, increased vibration and noise of the machine 4, 7, 8, 17. These symptoms let us create a diagnostic model of the machine, responsible for early estimation of the technical bar fault of the motor.The important point in this task is to develop the mechanism of association of the changes of the observed harmonic spectrum with the actual condition of the rotorS. Osowski (&)Warsaw University of Technology, Warsaw, Polande-mail: .plS. OsowskiMilitary University of Technology, Warsaw, PolandJ. KurekUniversity of Life Sciences, Warsaw, Polande-mail: .pl123Neural Comput & Applic (2010) 19:557564DOI 10.1007/s00521-009-0316-5bars. Different approaches to this problem have been developed in the past. In 16, the unsupervised neural network using clusterization technique has been used. In 6, the Bayes decision theory and Bayes minimum error classifier have been applied. Some papers present the application of the multi-layer perceptron, performing the role of classifier 9, 17. Recently, there is a lot of papers proving the superiority of support vector machine (SVM) in the classification tasks 12, including diagnosis of the bars in the machine 1, 3, 10. This paper will develop the fault detection model based on the application of SVM. In distinction to the other solutions, we propose to use special type of SVM, called single-class SVM. This type of classifier is ideally suited for the fault detection, since in the learning stage, it needs only the data belonging to one class, corresponding to the normal operation of machine (so called healthy bars). This is quite important, since in the industrial practice, most machines under operation are in normal state of bars. Thanks to this, there will be no problems with learning data acquisition. In the process of discovering the potential fault in an online operation, the trained single-class SVM compares the actual input signals with the learned prototype, corresponding to the healthy bars. When this difference is beyond the learned tolerance limit, the classifier treats it as a fault.Two solutions of the diagnostic system have been elaborated and presented in this work. The first detection system discovers only the fact of fault occurrence. The second one (complex diagnosis) is able to find how many bars have been damaged. The important point in diagnosis is the definition of diagnostic features, on the basis of which the classifier will be able to recognize the fault. In this work, we have defined special features relying on the FFT analysis of the registered instantaneous forms of the phase current, voltage and shaft field in a steady state. As a recognizing and classifying tool, we have used the Gaussian kernel support vector machine, known from its very good generalization ability 15. Two kinds of SVM networks have been used. For the fault detection, we apply the single-class SVM. The use of this type of classifier for the bar fault detection was never reported before. In the case of complex diagnosis, we have applied the ordinary two-class SVM, combined with a sophisticated procedure of feature selection.Thanks to such solution, we were able to recognize many different types of faults, never considered in the previous publications. The numerical experiments using Matlab 11 have been performed at the measurements done on a specially prepared machine platform, enabling to emulate the typical faults of the of rotor bars. The results presentedin the paper show high efficiency of the proposed approach.朗读显示对应的拉丁字符的拼音字典译文：非侵入性的电动机的转子条的故障诊断问题属于难问题，与此类型的损坏电动机机仍然有作用，但是这些工作引起了两个电流双方的转子条断。这将导致下一个转子条的损害。这雪崩过程的结果是损害所有的转子条，最后机器停止运转。因此，最重要的问题是找到只有一个或最多几个转子条坏了的时候（雪崩过程初期阶段）。 过去几年已有大量的研究报告致力于各种稳态状态监测技术的发展。他们大多使用傅立叶 定子使电流处于稳定状态 。一些应用于更复杂的小波分析定子瞬时状态的方法。 也有依靠分析磁场空间矢量的方向的解决方案。所有这些当前的预处理方法与不同预处理结果的分析工具阶段相结合，形成最终的分类阶段。其中我们可以提及的统计分类方法 ，人工神经网络或提供的向量机。他们负责自动识别的故障。对电机在正常状态下的工作的观察或允许指出一些转子条的故障条件表明转子条的典型故障症状。到最重要的是属于谐波频谱的变化相电流和电压，改变轴的通量， 机器的振动和噪声的增加。 这些症状让我们创建一个诊断模型机，负责早期评估故障的电机的转子条。 在这项任务的重要的一点是发展机制所观察到的变化的协会。在过去已经发展到这个问题的不同方法。无监督神经网络，利用集群化的技术已被使用。决策理论和贝叶斯最小错误分类已应用。有些论文目前多层感知器的应用程序，执行分类的作用。最近，有很多文献证明了支持向量机的优势在分类任务，包括诊断在机器里的转子，本文将在故障检测模型的基础上发展，应用SVM。在区别其他的解决方案时，我们建议使用特殊SVM的类型，被称为单类SVM。这种类型的分类是非常适合于故障检测的，因为在学习阶段，只需要属于数据一类，相应的正常运行的机器（即所谓的“正常”转子条）。这是相当重要的，因为在工业实践中，大多数机器转子条正常状态下运行。这一点，对学习数据采集有没有问题。在网上发现潜在的故障的过程行动中，单类SVM比较了解原型的实际输入信号，对应“正常”的转子条。当这种差异学会了限制之外的分类，将其视为故障。 两种解决方案已经阐述了诊断系统 ，并提出了这项工作。先检测系统发现故障发生的事实。第二个复杂的诊断是找到多少 转子条已被损坏。在诊断的重要点诊断功能的定义的基础上将能够识别的故障分类。在这项工作中，我们已经定义了依赖的特殊功能FFT分析的注册瞬时形式处于稳定状态，相电流，电压和轴领域。作为一个识别和分类的工具，我们已经使用了高斯核支持向量机，从很好的泛化能力到两种SVM的网络已被使用。对于故障检测，我们采用的单类SVM。使用这类型的分类，转子条故障检测从来没有报道过。在复杂的诊断的情况下，我们已申请，结合普通的两个类SVM 特征选择的一个复杂的过程。 这样的解决方案，我们能够认识到很多 不同类型的故障，从来没有考虑在前面的出版物。使用Matlab的数值试验上所做的测量已完成专门准备的机台，效仿转子的典型故障。结果高效率的文件显示了建议方法。实验说明对实验数据进行收集，使用实时测量系统，在研究所的实验室建华沙理工大学电机。通过使用数据，已经完成所有登记手续采集卡USB - 6251的采样频率等于10千赫。要执行非常具体的测量，我们必须使用专门准备的异步电动机配额外的头环和螺丝连接到每个鼠笼条（图1）。这个建筑的变化使我们能够模拟在异步电动机断条。在我们的例子中，归纳出电机有33个转子条鼠笼。标称Sg132M - 6B - S电机参数以下内容：额定电压：UN = 3 9 400伏，频率：F = 50赫兹，额定功率：PN = 5.5千瓦，额定电流：= 12.1，COS / N = 0.83，效率：GN = 79，速度：NN = 895转/分。机器的诊断测量工作在不断变化的负载从标称的一半面值和几乎对称的3相供电系统（真正的工业体系）。所有已登记数据已经规范化。正常化机器的大小和功率（和一些数据，程度也负载）上的没有影响诊断系统