基于Lyapunov理论的异步电机定子绕组匝间短路故障检测系统设计

为实现对定子绕组匝间短路故障行为的精准识别，确保异步电机的稳定运行状态，设计了基于Lyapunov理论的异步电机定子绕组匝间短路故障检测系统。在DSP外围电量回路中，设置ARM处理器与步进电机驱动模块，采用模数转换单元结构，调节电量互感装置的实时运行状态，实现对异步电机定子绕组匝间短路故障检测系统硬件设计。根据Lyapunov函数定义条件，确定Nussbaum增益参数取值范围，在此基础上，定义Lyapunov算法模型，再通过计算故障预测特征的方法，求解定子绕组的短路故障电压方程与匝间电感参数，对定子绕组匝间短路故障特征进行分析，实现异步电机定子绕组匝间短路故障检测。实验结果表明，所设计系统可以有效控制定子绕组匝间短路故障电流和电压检测结果与标准检测结果之间的差值水平，能够精准识别定子绕组匝间短路故障行为，保障异步电机的稳定运行状态。     

Detection of stator winding fault in induction motor using fuzzy logic

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. The fuzzy system is based on knowledge expressed in rules and membership functions, which describe the behaviour of the stator winding. The finite element method (FEM) is utilised to generate virtual data that support the construction of the membership functions and give the possibility to online test the proposed system. The layout has been implemented in MATLAB/SIMULINK, with both data from a FEM motor simulation program and real measurements. The proposed method is simple and has the ability to work with variable speed drives. The fuzzy system is able to identify the motor stator condition with high accuracy. This work is an example of the fusion between soft and hard computing.     

Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.     

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37?kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.     

Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network

Monitoring system for induction motor is widely developed to detect the incipient fault. Such system is desirable to detect the fault at the running condition to avoid the motor stop running suddenly. In this paper, a new method for detection system is proposed that emphasizes the fault occurrences as temporary short circuit in induction motor winding. The investigation of fault detection is focused on the transient phenomena during starting and ending points of temporary short circuit. The proposed system utilizes the wavelet transform for processing the motor current signal. Energy level of high frequency signal from wavelet transform is used as the input variable of neural network which works as detection system. Three types of neural networks are developed and evaluated including feed forward neural network (FFNN), Elman neural network (ELMNN) and radial basis functions neural network (RBFNN). The results show that ELMNN is the most simply and accurate system that can recognize all of unseen data test. Laboratory based experimental setup is performed to provide real-time measurement data for this research.     

基于幅值恢复算法的谐波分析实验

本文的研究目的就是针对三相异步电机的运行状态的监测以期对电机早期故障能够及时发现,减小故障严重后造成的巨大损失。本文采用分析定子电流的方式对运行中的电机进行现场监测,为解决故障频率与电网频率接近,且电机轻微故障时,定子电流中故障特征分量幅值过小的问题,使用一种新的谐波分析方法——幅值恢复算法,将该算法结合Fourier频谱分析,可对电机轻微故障和微弱的谐波成分做出有效的分析。     

基于RBF神经网络的电机故障诊断的研究

在对国内外感应电动机故障诊断技术发展与研究的基础上,提出了从定子电流人手,利用径向基（RBF）神经网络算法来监测感应电动机工作状态,从而实现对电动机较为常见的电气故障和机械故障的综合检测。Matlab仿真结果表明RBF算法有效地实现了对电机故障诊断的研究。     

Fuzzy logic-based induction motor protection system

The protection is very important to detect abnormal motor running conditions such as over current, over voltage, overload, over temperature, and so on. When a failure is sensed by the protection system, a time delay should be specified to trip the motor. In the classical systems, motors are stopped with the time delay, which is adjusted constantly without considering the fault level. This paper presents a fuzzy logic-based protection system covering six different fault parameters for induction motors. This paper focuses on a new time-delay calculation for stopping induction motor and improves the overall detection performance. The time delay is computed by fuzzy logic method according to various fault parameters when one of the failures occurs on the motor. This system is successfully tested in real-time faults on the motor, and it shows that it provides sensitive protection by fuzzy rules.     

A novel multi-agent approach to identify faults in line connected three-phase induction motors

Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2 hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.     

定子电阻辨识用于异步电机温度监测的研究

定子绕组温度监测是电机安全运行的重要保障.利用定子电阻值与温度的密切关系,提出了一种定子电阻估计方法以反映绕组温度的变化.在电流定向的坐标系中,异步电机数学模型中与定子电阻相关的部分得到简化.基于异步电机稳态模型,得到了独立于定、转子电阻和转速的稳态磁链估计,然后利用稳态磁链建立了定子电阻在线整定律.该整定律只需量测定子电流电压,简单易行.所提出的定子温度监测方法可以作为电机过热保护的一种辅助措施.     

Diagnosis of broken rotor bars in induction motors based on harmonic analysis of fault components using modified adaptive notch filter and discrete wavelet transform

This paper proposes a method for fast and accurate detection of broken rotor bars (BRBs) in a three-phase squirrel cage induction motor. The fundamental component of the stator current signal is extracted using a linear time-invariant filter. The resultant residual signal which contains the harmonic components of the current is then used to detect the BRBs, by means of discrete wavelet transform (DWT). Since in experiment it is not possible to break the rotor bars while the motor is under load, finite element method and MATLAB/Simulink are employed to accurately demonstrate the behavior of the running machine as the BRB happens. To get more accuracy, differential evolution (DE) optimization algorithm is used to obtain the corresponding fault impedance for the rotor external circuit of the MATLAB model. Detail coefficients (DCs) of the wavelet decomposition are employed as the new fault indicators. Simulation results show that using DCs of the harmonic component signal rather than the actual current signal, leads to more distinctive fault signatures in the wavelet decomposition. The obtained results suggest that the proposed fault detection scheme can be employed as a highly reliable technique for diagnosing rotor bar failures in running machines.     

Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.     

Precise wavelet for current signature in 3 IM

Induction motors, which are used worldwide as the “workhorse” in industrial applications, are intermittently subjected to faults, mainly the stator faults. In this paper, fault diagnostics of induction motor using current signature analysis, with wavelet transform, is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, feature selection and classification. The feature extraction is done by wavelet transforms, using different wavelets which allow the use of long time intervals where there is precise low-frequency information, and shorter regions where there is precise high-frequency information. The extracted features are classified using the new generation pattern classification technique of Support Vector Machine (SVM) identification. Then the relative capability of the different wavelets, in performing the stator winding fault identification is analyzed and the best wavelet is selected.     

基于融合分析的感应电机轴承故障检测方法

根据感应电机轴承发生故障时的振动信号特性以及定子电流特性，求出三相电流的Park矢量模信号，并将其与电机滚动轴承振动信号经解调处理后的包络信号进行融合分析。可以从振动信号与电流信号的融合谱图中有效地提取轴承故障特征信息，并将其作为故障识别的依据。实验结果表明，本文检测方法具有较高的信噪比，提高了诊断的可靠性。     

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier–Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.     

Dynamic Routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor

Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multi-source information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance.     

Recursive identification of induction motor parameters

The use of linear parameter estimation techniques to determine the rotor resistance, self-inductance of the rotor winding, as well as the stator leakage inductance of a three-phase induction machine is investigated in this paper. In order to obtain results with maximum accuracy, some specific procedures to reduce the effect of the operating conditions on the quality of the estimates are investigated. For analytical identification, a model is developed from the steady-state equations of induction motor dynamics. The identification procedure, based on a simple algorithm derived from least squares techniques, uses only the information of stator currents and voltages and rotor angular speed as input–output data. The computer simulation as well as the experimental results are used to anchor the main conclusions issued from this study and to demonstrate the practical use of the identification method.     

Research on motor control and simulation based on PID and Internet of Things system

A motor application consists of a Single Phase Induction Motor, and it has single-phase winding, which is winding on the stator of the motor and a winding placed on the rotor. A pulsating magnetic field is produced when a phase supply energizes the single-phase induction motor's stator winding shown below. In the existing method, DTC (Direct Torque Control) is based on converting alternating source and fed to the desired source motor low efficiency and high speed. So in this proposed method, Induction Motor Using Proportional-Integral-Derivative (PID) and IOT (Internet of Thing) for Inverter based achieving an efficiency output voltage or with the lowest amount of ripple content, the high switching frequency. Proportional Integral Derivative (PID) is an emerging technique for controlling PWM (Pulse Width Modulation) Inverter-Fed Induction Motor (IM) drives. The precise and quick inspection of the IM (Induction Motor) flux and torque without calling for complex control algorithms. In principle, moreover, it requires only the knowledge of the stator resistance. The application reviews an IM (Induction Motor) essential operation and a PWM (Pulse Width Modulation) inverter using the space vector theory. The IOT (Internet of Thing) control is a monitoring application of a Checking and controlling compliance and boundary is essential in many applications. The reliable functionality Policies available for equivalents and handles Observing different boundaries and three control.     

基于物联网的分布式电动机故障诊断与保护系统研究

针对传统的电动机保护装置无法实现早期故障诊断、不具备联网功能的问题,提出了一种基于物联网和支持向量机算法的分布式电动机故障诊断与保护系统的设计方案。该系统的下位机利用对称分量法将采集到的电动机定子电流进行分解,根据电流分量值判断故障类型来实现电动机的现场保护,并将定子电流数据通过ZigBee技术发送至嵌入式网关,通过GPRS网络实时上传给上位机;上位机通过小波包分解提取故障特征向量,采用支持向量机对电动机故障进行分类,实现故障早期诊断和预测。实际运行结果表明,该系统能准确诊断电动机故障并实施有效的综合保护。     

A nonlinear tracking control for sensorless induction motors

We propose a novel tracking control for induction motors in which only stator currents are used for feedback. Local exponential rotor speed and flux modulus tracking are achieved for any constant reference value and for restricted time-varying reference signals; any known motor parameters values (including constant load torque) and any initial condition, including rotor speed and fluxes, belonging to an explicitly computed domain of attraction are allowed.