Condition monitoring of speed controlled induction motors using wavelet packets and discriminant analysis

This work presents an intelligent method for the condition monitoring of induction motors supplied with adjustable speed drives (ASD). Most of the previous work in this area concentrated on the fault detection and classification of induction motors supplied directly from an a.c. line. However, ASD driven induction motors are widely used in industrial processes and therefore obtaining an intelligent tool for the condition monitoring of these motors is necessary in terms of preventive maintenance and reducing down time due to motor faults. Here 3-phase supply side current of the ASD driving an induction motor is used to extract statistical features of wavelet packet decomposition coefficients within a frequency range of interest. This way, the information regarding the output frequency of the ASD and hence the motor speed is not required. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with various synthetic faults were used for condition detection and classification. Extracted features obtained from decomposition coefficients of different wavelet filter types for all motors were employed in three different and popular classifiers. The proposed method and the performance of the features used for fault detection and classification are examined at various motor loads and speed levels, and it is shown that a successful condition monitoring system for induction motors supplied with ASDs is developed. The effect of selected filter type in wavelet decomposition to the condition monitoring process is analyzed and presented.     

Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis

The motor is the workhorse of industry. The issues of preventive and condition-based maintenance, on-line monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detection signal features.This work was presented in part at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004This work has been supported by “Research Center for Future Logistics Information Technology” hosted by the Ministry of Education in Korea.     

应用模糊逻辑实现异步电机的效率最优控制

异步电机在满载时一般能以较高的效率运行，在轻载时的效率却大大的减低了。而生产中的异步电机大多数工作在轻载情况下，极大的浪费了能源。该文提出了一种基于模糊理论的异步电机效率最优控制方法，在电机轻载稳态运行时通过模糊控制器逐步减少励磁电流，通过对磁通的在线搜索找到电机的最小输入功率运行点，从而使电机以最优效率运行。该方法能在保证电机转速不变的情况下明显提高电机轻载时的效率。通过建立调速系统模型并进行仿真分析证明了这种方法的正确性。     

Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system

This paper introduces a system for fault detection and classification in AC motors based on soft computing. The kernel of the system is a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART-based), that permits the detection of a fault if it is in progress and its classification, with very low detection and diagnosis times that allow decisions to be made, avoiding definitive damage or failure when possible. The system has been tested on an AC motor in which 15 nondestructive fault types were generated, achieving a high level of detection and classification. The knowledge stored in the neuro-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability and without incoherency amongst the extracted rules.     

三相感应电动机的在线故障检测与诊断问题研究

本文建立了三相感应电动机的通用故障模型，提出了一种检测，分离，并估计出其转子与定子故障的新方法，最后，应用一个４４７０千瓦的大型电动机的实际参数作了仿真研究，验证了本文方法的有效性。     

Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.     

Linearizing control of induction motor based on networked control systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.     

三相感应电动机的在线故障检测与诊断问题研究

本文建立了三相感应电动机的通用故障模型，提出了一种检测、分离、并估计出其转子与定子故障的新方法．最后，应用一个４４７０千瓦的大型电动机的实际参数作了仿真研究，验证了本文方法的有效性．     

An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform

This paper describes an application about detection of bearing defects in inverter fed induction motors, using Concordia transform approach based algorithm. After introduction, brief information is given about bearing structure and type of bearing failures. Next section, Concordia transform theory is mentioned then, RBF neural network structure is summarized. After that, test system information is specified. This paper indicates that Concordia transform approach is a reliable tool to detect bearing faults in inverter fed small induction motors. The generality of the proposed methodology has been experimentally tested on a 1 HP squirrel-cage induction motor. At the end of the paper, an ANN algorithm is proposed that could detect the bearing faults automatically. The obtained results have 93.75% accuracy. This study suggests that proposed Concordia transform based fault detection algorithm could be integrated in an induction motor driver so, bearing condition of the induction motor could be observed while motor is working and bearing faults could be detect before they become serious.     

基于BP网的交流电机调速系统智能机内检测技术研究

本文介绍了一种采用了智能机内检测技术的交流电机调速系统，基于BP网的机内检测技术提高了故障诊断的精确性．缩短了诊断时间，可对电机提供可靠而有效的保护。     

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.     

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.     

Dynamic equivalent method of interconnected power systems with consideration of motor loads

The existing equivalent methods usually only deal with static load models and neglect the dynamic characteristics of loads such as induction motors.This paper presents a dynamic equivalent method which considers motor dynamics.At first,the clustering criterion of motor loads is given.The motors with similar dynamic characteristics are classified into one group.Then,reduction of motors in the same group is carried out.Finally,parameters of the equivalent motor are calculated and the equivalent system is thus...     

基于定子电流监控的轴承故障在线监测

随着自动化技术的增长,对感应电动机内部滚动轴承的状态监控得到了飞速发展.很多优秀的技术手段被运用到轴承故障的在线监测上,然而还存在着两点不足:1)提取的故障信号不够准确;2)无法满足在线的需求.本文在基于定子电流监测的基础上,提出了一种新型的轴承故障在线诊断方法.为了能够从电流频谱中提取更加准确的信息,作者将改进了的时域平均方法(time domain average method,TDA)运用到故障信号的隔离中.另一方面,极限学习机(extreme learning machine,ELM)在分类问题上表现出良好的泛化性能,它快速的训练速度能够保证在线故障监测的实施.文章最后考虑了3种电机运行状态,仿真结果均证明了此方法的有效性和稳定性.     

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。     

Vibration signal analysis for electrical fault detection of induction machine using neural networks

Fault detection is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three-phase induction motors originate from an electrical source. Vibration signal analysis is found to be sensitive to electrical faults. However, conventional methods require detailed information on motor design characteristics and cannot be applied effectively to vibration diagnosis because of their nonadaptability and the random nature of the vibration signals. This paper presents the development of an online electrical fault detection system that uses neural network modeling of induction motor in vibration spectra. The short-time Fourier transform is used to process the quasi-steady vibration signals for continuous spectra so that the neural network model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Experimental observations show that a robust and automatic electrical fault detection system is produced whose effectiveness is demonstrated while minimizing the triggering of false alarms due to power supply imbalance.     

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.     

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.     

Cascade fuzzy variable structure control of induction motor based on the approach of fuzzy modelling of Ben-Ghalia

In this article, a control design concept using fuzzy sets for an induction motor is presented. The aim of the proposed modelling approach is to provide a fuzzy set-based representation of the cascade sliding mode control of an induction motor fed by PWM voltage source inverter, which operates in a fixed reference frame. For this purpose, a new decoupled and reduced model is first proposed. Then, a set of simple surfaces and associated control laws are synthesised. A piecewise smooth control function with a threshold is adopted. However, the magnitude of this function depends closely on the upper bound of uncertainties, which include parameter variations and external disturbances. This bound is difficult to obtain prior to motor operation. To solve this problem, a fuzzy modelling approach is presented to improve the design and tuning of a fuzzy logic controller using variable structure control theory. The robust fuzzy control design is made feasible without resorting to model simplification or imposing restrictive conditions on the system uncertainty. The fuzzy controller is designed in order to improve the control performances and to reduce the control energy and the chattering phenomenon. Simulation results reveal some very interesting features and show that the proposed fuzzy sliding mode controller could be considered as an alternative to the conventional sliding mode control of induction motors.     

A modified direct torque control with fault tolerance

The use of inverters in induction motor control has reduced classical motor faults, such as broken rotor bars or windings short-circuit, besides improving control performance. The control becomes faster and more precise, reducing peaks in current and torque, so that the motor can have a softer operation. On the other hand, new elements are included in the system and it will be necessary to take into account their faults. These elements are sensors and power electronic devices that since a control point of view are the system sensors and actuators. Fault tolerance tries to maintain the system under control in case a fault appears in the system. If this is not possible, it takes the system to a safe operational point. In this paper a fault-tolerant control for induction motors is designed. Based on a direct torque control, new control strategies have been added in case current sensor and power switch faults are detected. The challenge is to overcome these faults without any physical redundancy of sensors or power switches as other authors propose. With the proposed control, it will be possible to guarantee the motor operation in the whole speed-torque range with one or none current sensors instead of the two usually used, though the performance will be slightly worsened. In case of inverter faults, the operation range will be restricted but the performance with respect to the fault situation is improved.