笼型感应电机转子断条故障诊断方法

依据多回路理论,本文建立了笼型感应电机正常及断条故障时的有限元模型.应用场路耦合有限元方法对电机转子断条故障前后的定子起动电流波形及气隙磁密基波、三次谐波进行仿真分析,并且仿真结果与利用探测线圈技术和示波器测得的数据进行了比较.另外,应用谐波分析程序得到电流及电压三次谐波随故障演化而变化的规律,从而提取故障特性量.理论研究与实验结果的比较证实了这些方法是切实可行的.     

Estimation of frequency components in stator current for the detection of broken rotor bars in induction machines

The limitation of data window length in induction machine broken rotor bar diagnostics is a real challenge in practice. Sideband frequencies which are used as broken rotor bar indicators are very close to the fundamental frequency and have low magnitude. Traditional spectral analysis approach such as Discrete Fourier Transform (DFT) can be inaccurate in these conditions due to its inherent drawbacks such as the requirement of long data window for high resolution and the side lobe leakage in frequency domain. In this paper, a high-resolution spectral analysis technique, Prony Analysis (PA), is proposed for broken rotor bar detection in induction machines. The method is described and demonstrated in detail, validated by experimental data, and compared with DFT. Results clearly indicate the advantages of PA over DFT in terms of maintaining a high resolution with a much shorter window and a better frequency estimate accuracy with the same window length.     

Detection of stator winding faults in induction motors using three-phase current monitoring

The objective of this paper is to propose a new method for the detection of inter-turn short circuits in the stator windings of induction motors. In the previous reported methods, the supply voltage unbalance was the major difficulty, and this was solved mostly based on the sequence component impedance or current which are difficult to implement. Some other methods essentially are included in the offline methods. The proposed method is based on the motor current signature analysis and utilizes three phase current spectra to overcome the mentioned problem. Simulation results indicate that under healthy conditions, the rotor slot harmonics have the same magnitude in three phase currents, while under even 1 turn (0.3%) short circuit condition they differ from each other. Although the magnitude of these harmonics depends on the level of unbalanced voltage, they have the same magnitude in three phases in these conditions. Experiments performed under various load, fault, and supply voltage conditions validate the simulation results and demonstrate the effectiveness of the proposed technique. It is shown that the detection of resistive slight short circuits, without sensitivity to supply voltage unbalance is possible.     

一种感应电机参数自适应转子磁链观测器的设计

为了解决“感应电机运行过程中由电机参数变化引起的转子磁场定向不准确”的问题,根据龙贝格观测器原理提出了一种新颖的参数自适应转子磁链观测器。首先论述了观测器的设计原理,针对磁链误差无法获得这一问题提出了一种有效的解决方案,引入了新变量,然后根据Lyapunov稳定性原理设计小参数自适应律。最后分别在Matlab和DSP2812平台上对该算法的参数收敛性以及定子电阻误差对观测器的影响进行了验证。仿真和实验结果表明,该方法不但可以对转子磁链加以观测,还可以同时在线调整并辨识转子电阻以及转子时间常数,对定子电阻具有很强的鲁棒性。     

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a Wigner-Ville distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool - the discrete wavelet transform (DWT) - applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena.     

Particle and Kalman filtering for state estimation and control of DC motors

State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor’s state vector, but at the same time it required higher computational effort.     

自适应滤波算法在导弹动力系统故障诊断中的应用

基于时间序列的AR模型方法在用于液体导弹动力系统稳态工作段故障检测时遇到了两难题。其一,由于系统差异,在以试车时的动力系统状态的均值作为实际运行时动力系统状态均值时,得到的时间序列必定不再是零均值;其二,导弹动力系统工作环境不同时刻不尽相同,表现在AR模型中其观测噪声是时变的。在实践中,我们采用了基于扩展Kalm an滤波的自适应算法以及带有时变噪声统计的自适应滤波算法很好地解决了相关问题。     

Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations.     

A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor

This paper proposes a new induction motor broken bar fault extent diagnostic approach under varying load conditions based on wavelet coefficients of stator current in a specific frequency band. In this paper, winding function approach (WFA) is used to develop a mathematical model to provide indication references for parameters under different load levels and different fault cases. It is shown that rise of number of broken bars and load levels increases amplitude of the particular side band components of the stator currents in faulty case. Stator current, rotor speed and torque are used to demonstrate the relationship between these parameters and broken rotor bar severity. An induction motor with 1, 2 and 3 broken bars and the motor with 3 broken bars in experiment at no-load, 50% and 100% load are investigated. A novel criterion is then developed to assess rotor fault severity based on the stator current and the rotor speed. Simulations and experimental results confirm the validity of the proposed approach.     

Impulse noise cancellation in image data using a two-output nonlinear filter

The quality of image data is often degraded by impulse noise caused by noisy sensors and/or transmission errors. To address this issue, a two-output nonlinear filtering architecture is presented. The proposed approach is based on the subsequent activation of two recursive filtering algorithms that operate on different subsets of input data. As a result, two pixel values are updated at each processing step producing a very effective cancellation of noise pulses. Impulse noise removal is based on rank ordered filtering. A nonlinear mechanism for error correction is also provided in order to avoid detail blur. Validation of the method is carried out by evaluating the quality of the filtered data with respect to two conflicting performance indexes: effectiveness of noise cancellation and accuracy of detail preservation. Results of computer simulations show that the proposed approach performs significantly better than well-known nonlinear methods in the literature including state-of-the-art operators.     

Validation of thermal and electrical model for induction motors using fiber Bragg gratings

This works presents the simulation and validation of the thermal, electrical and mechanical models of a three-phase induction motor (TIM). Fiber Bragg grating (FBG) sensors are used to measure stator temperature and validate the thermal model. The knowledge of the relationship between losses and temperature variation in the TIM makes a simulation of the motor possible. To determine losses in the TIM an equivalent electrical circuit in arbitrary reference frame is used, which combines a traditional model with the more usual modeling of losses in the stator iron. The thermal study of the motor is performed using an equivalent thermal circuit formed by thermal capacitances and thermal conductivities that are separately considered for the stator and rotor. The losses calculated with the electrical and mechanical models are the input parameters for the thermal model. The simulation of the electrical model produces an error of approximately 4.2% when determining the Joule effect losses in the motor when compared to the experimentally obtained results. The simulation of the mechanical model presents an error of 0.2% for the losses due to friction and ventilation. The stator and rotor temperature, obtained with the thermal model, presented a high correlation with the measured values. The thermal model presents a maximum error of 0.75 °C when one compares them to the average experimental values of temperature in the stator during the temperature transient behavior. When the temperature in the stator reaches steady state, the experimental and simulated results converge to the same values. The use of FBGs to measure temperature in the machine allowed a thermal model to be developed, which also uses the mechanical losses of the machine and is the main contribution of this work.     

Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor

Time-domain vibration signals are measured in all horizontal, axial, and vertical directions for induction motor mechanical fault diagnostics. Many features are extracted from these signals. The problem is how to find the good features among the feature set in order to receive reliable classifications. Based on specific distance criteria, a genetic algorithm (GA) is introduced to reduce the number of features by selecting optimized ones for fault classification purpose. A decision tree and multi-class support vector machine are used to illustrate the potentiality and efficiency of this selection method. Comparisons show that the diagnostic systems after selecting specific features perform better than the original system.     

Detection of stator winding faults in induction machines using flux and vibration analysis

This work aims at presenting the detection and diagnosis of electrical faults in the stator winding of three-phase induction motors using magnetic flux and vibration analysis techniques. A relationship was established between the main electrical faults (inter-turn short circuits and unbalanced voltage supplies) and the signals of magnetic flux and vibration, in order to identify the characteristic frequencies of those faults. The experimental results showed the efficiency of the conjugation of these techniques for detection, diagnosis and monitoring tasks. The results were undoubtedly impressive and can be adapted and used in real predictive maintenance programs in industries.     

基于EKF的异步电机转速和负载转矩估计

合理选择电机的容量具有重要的意义,电机的容量可根据电机的转速和负载转矩确定,将电机的转速和负载转矩同时作为系统的状态,提出了一种基于EKF同时估计异步电机转速和负载转矩的方法,建立了包含异步电机转速和负载转矩状态的系统模型,基于该模型用EKF实现了同时估计异步电机转速和负载转矩,仿真和实验验证了所提方法能以较高的精度同时估计出电机的转速和负载转矩.     

An improved model-based predictive control of vehicle trajectory by using nonlinear function

A new model-based predictive control algorithm for vehicle trajectory control is proposed by using vehicle velocity and sideslip angle. Based on the error function combined with vehicle velocity and side slip of a bicycle model, a predictive control method has been proven to be useful on low velocity. Thus, it could be applied for an autonomous vehicle without a driver. Although an autonomous robot is not necessary to be driven with a high velocity, a commercial vehicle has to be driven at high velocity. Thus the previous predictive control formulation is not enough for a commercial driving system. This study is proposed to enhance the capacity of the predictive controller for rather high speed vehicles. This paper was presented at the 4th Asian Conference on Multibody Dynamics(ACMD2008), Jeju, Korea, August 20–23, 2008. Mr. Jeong-Han Lee is pursuing a Ph.D. degree in Mechanical Engineering at Pusan National University under the supervision of professor Wan-Suk Yoo. His research interests are focused on the area of adaptive control using multibody dynamics. Dr. Wan-Suk Yoo received his Ph.D. degree in 1985 from the University of Iowa. In 1994, he became a full professor at the Pusan National University, and he was selected an ASME fellow. He is serving as a vicepresident of the KSME.     

滑动轴承转子系统非线性动力学稳定性研究

采用多尺度法研究了滑动轴承刚性平衡转子系统临界点的稳定性。研究发现,滑动轴承在其线性失稳转速上发生Hopf分岔,系统是发生超临界Hopf分岔还是亚临界Hopf分岔取决于系统参数的设计。给出了通用的公式和程序,为滑动轴承的非线性动力学设计及性能预测提供了有利的工具。     

基于Kalman滤波和短时自相关变换的电压暂降检测

为了在含有谐波和噪声的电网中准确检测电压暂降,将卡尔曼滤波器和短时自相关变换结合起来,对相邻工频周期的采样信号做差,再进行短时自相关变换,准确检测到了电压扰动,采用卡尔曼滤波器对基波幅值和相位进行了估计;采用离线计算增益矩阵K的方式减小了卡尔曼递推算法的运算量,并在稳态时通过采用定常增益矩阵K达到了抑制滤波发散的目的,同时减小了所需的存储空间;M atlab仿真实验证实了此方法的有效性。     

Bearing fault detection using wavelet packet transform of induction motor stator current

Induction motor vibrations, caused by bearing defects, result in the modulation of the stator current. In this research, bearing defect is detected using the stator current analysis via Meyer wavelet in the wavelet packet structure, with energy comparison as the fault index. The advantage of this method is in the detection of incipient faults. The presented method is evaluated using experimental signals. Sets of data are gathered before and after using defective bearings. Compared to conventional methods, the superiority of the proposed method is shown in the success of fault detection.     

A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art

Preventing induction motors (IMs) from failure and shutdown is important to maintain functionality of many critical loads in industry and commerce. This paper provides a comprehensive review of fault detection and diagnosis (FDD) methods targeting all the four major types of faults in IMs. Popular FDD methods published up to 2010 are briefly introduced, while the focus of the review is laid on the state-of-the-art FDD techniques after 2010, i.e. in 2011–2015 and some in 2016. Different FDD methods are introduced and classified into four categories depending on their application domains, instead of on fault types like in many other reviews, to better reveal hidden connections and similarities of different FDD methods. Detailed comparisons of the reviewed papers after 2010 are given in tables for fast referring. Finally, a dedicated discussion session is provided, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs, to inspire novel research ideas and new research possibilities.     

Isolation and identification of dry bearing faults in induction machine using wavelet transform

Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations that can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. The presence of dry bearing fault, which is caused by the lack of lubricant, increases the level of random vibrations as compared to those obtained in healthy bearing machine. If these random vibrations could be isolated from the measured signal, useful information about bearing health may be obtained. Therefore, in this paper, signals (three line to line voltages, three currents, two vibration signals, four temperatures and one speed signal) obtained from the monitoring system are treated and analyzed using wavelet transform to correlate it to the dry bearing faults in induction machine. In this study, on-line analysis of the acquired signals has been performed using C⁺⁺, while MATLAB has been used to perform the off-line analysis.