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.     

Sliding-mode control of a six-phase series/parallel connected two induction motors drive

In this paper, a parallel configuration is proposed for two quasi six-phase induction motors (QIMs) to feed them from a single six-phase voltage source inverter (VSI). A direct torque control (DTC) based on input–output feedback linearization (IOFL) combined with sliding mode (SM) control is used for each QIM in stationary reference frame. In addition, an adaptive scheme is employed to solve the motor resistances mismatching problem. The effectiveness and capability of the proposed method are shown by practical results obtained for two QIMs in series/parallel connections supplied from a single VSI. The decoupling control of QIMs and the feasibility of their torque and flux control are investigated. Moreover, a complete comparison between series and parallel connections of two QIMs is given.     

Motor square current signature analysis for induction motor rotor diagnosis

Induction machines play an important role in today’s industry. Thus, preventive maintenance combined with fault diagnosis techniques have become an essential issue. One of the most used techniques for the diagnosis of faults in the induction machine is motor current signature analysis (MCSA). This approach presents some limitations for induction motor rotor diagnosis, particularly for small faults. In this paper, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented. The proposed technique is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed. This technique allows more information to be obtained from a motor with a rotor fault than the classical MCSA approach. Simulation and experimental results are presented in order to confirm the theoretical assumptions. This methodology has also been tested for the identification of two distinct faults (broken bars and rotor eccentricity).     

基于MCSA和SVM的异步电机转子故障诊断

本文提出一种基于电机电流信号频谱分析和支持向量机的异步电机转子故障诊断方法，该方法可以利用支持向量机对电机电流频谱信号的特征信息和故障模式进行关联。对电机定子电流采样后，其信号经FFT变换后提取故障特征量作为支持向量机的输入，基于1对1算法构造了感应电机转子故障多类分类器。实验结果表明，该方法具有很好的分类和泛化能力，可以提高电机故障诊断的准确性。     

异步电动机故障诊断的小波变换等高图方法

用Morlet小波作为小波基，对异步电动机鼠笼转子故障时的定子电流信号进行多尺度分析，将获得的小波变换系数用等高图表示，从中能清楚地识别出异步电机鼠笼转子不同断条的故障。较基于傅立叶变换的故障诊断，该方法对异步电动机故障的辩识能力有显著提高。     

Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The fault diagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, fault diagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the fault diagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for fault diagnosis of water hydraulic motor.     

基于神经网络的电动机逆变驱动系统故障诊断

针对电动机变频调速系统中逆变器开关元件故障类型多，传统故障诊断方法难以实现故障分离等情况，本文提出了一种基于神经网络的电动机变频调速系统故障诊断方法。通过对逆变器输出信号的谱分析可以获得对故障敏感的故障特征量，将这些故障特征量输入神经网络后，由网络输出层的结点输出可以判断故障类型，从而实现故障分离。研究结果表明，该方法可有效实现开关元件断路、短路故障，为进一步实现逆变器容错驱动奠定了理论基础。     

基于观测器的感应电机故障检测方法及应用

定子绕组和转子绕组的故障是导致感应电机失效的主要原因之一,实时监测电机运行状况不仅可以提高电机运行的可靠性,而且可以避免不必要的经济损失,因此及时而有效地检测感应电机绕组故障是完全有必要的.首先对感应电机定子绕组和转子绕组的故障特性进行分析,并对其故障进行了建模,然后利用感应电机d-q坐标系的状态空间数学模型,提出了一种鲁棒观测器的设计方法,该方法不仅对未知输入扰动具有良好的鲁棒性,而且对绕组故障具有很高的灵敏度,最后对模拟的绕组故障进行了实验,结果证实该方法是正确有效的.同时该方法的提出对实际工程应用也具有一定的参考价值.     

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.     

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks

The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method.     

Comparative investigation of diagnosis media for induction machine mechanical unbalance fault

For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial vibration and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute diagnosis technique is proposed. In addition, the load torque effect on the detection efficiency of these diagnosis media is discussed and a comparative investigation is performed. The decisive factor of comparison is the fault sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical vibration practice.     

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.     

电液伺服阀力矩马达故障模型及其仿真

本文引入了故障仿真的方法,通过建立故障模型,对电液伺服阀中的力矩马达故障进行了仿真分析,给出了几种故障特征及其区别方法,分析结果为电磁力矩马达及至电液伺服阀故障诊断,提供了特性依据。     

Fault diagnosis based on Walsh transform and support vector machine

Recognition of shaft orbit plays an important role in the fault diagnosis. In this paper, a novel recognition method for the shaft orbit based on Walsh transform and support vector machine is proposed. In the method, distance vector between the point on the shaft orbit and its center is first calculated. Then, the distance vector is transformed by Walsh matrix, and the Walsh spectrum obtained has property of invariance to rotation, scaling and translation. In the end, the Walsh spectrum, viewed as the feature of shaft orbit, is trained and tested by means of support vector machine. In addition, a comparison with the previous methods is performed, and experimental results are encouraging, which fully demonstrates the effectiveness and superiority of the proposed approach.     

An availability of MEMS-based accelerometers and current sensors in machinery fault diagnosis

In recent years, micro-electromechanical systems (MEMS)-based sensors have shown huge attraction in machinery fault diagnosis due to their low power consumption, low cost, small size, mobility, and flexibility. Hence, this paper presents a comprehensive fault diagnosis scheme using MEMS-based accelerometers and current sensors to identify several induction motor failures. In this paper, we first verify the reliability of these MEMS-based sensors via frequency analysis for vibration and current signals captured by them. Likewise, this paper validates their suitability for machinery fault diagnosis. To do this, we configure a 147-dimensional feature vector using statistical values (i.e., 21 statistical values × 7 MEMS-based accelerometers and current sensors), analyze fault signatures by employing a kernel principal component analysis, and pinpoint types of induction motor failures with one-against-all multi-class support vector machines (OAA MCSVMs), a random forest (RF), and a fuzzy k-nearest neighbor (Fk-NN). Experimental results indicate that the presented fault diagnosis approach using MEMS-based accelerometers and current sensors yields 100%, 86%, and 80% of classification accuracy with OAA MCSVMs, the RF, and the Fk-NN, respectively. Accordingly, MEMS-based sensors are enough for substituting commercial accelerometers and current sensors that are used for fault diagnosis. Specifically, MEMS-based accelerometers are far more effective for preserving intrinsic information about various induction motor failures than MEMS-based current sensors, offering at least 38% performance improvement in classification accuracy.     

Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

Partial rub and looseness are common faults in rotating machinery because of the clearance between the rotor and the stator. These problems cause malfunctions in rotating machinery and create strange vibrations coming from impact and friction. However, non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency information is needed for identifying these signals. For this purpose, a newly developed time-frequency analysis method, HHT (Hilbert-Huang Transform), is applied to the signals of partial rub and looseness from the experiment using RK-4 rotor kit. Conventional signal processing methods such as FFT, STFT and CWT were compared to verify the effectiveness of fault diagnosis using HHT. The results showed that the impact signals were generated regularly when partial rub occurred, but the intermittent impact and friction signals were generated irregularly when looseness occurred. The time and frequency information was represented exactly by using HHT in both cases, which makes clear fault diagnosis between partial rub and looseness. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin     

Fault diagnosis and accommodation of a three-tank system based on analytical redundancy

This paper investigates the application of a fault diagnosis and accommodation method to a real system composed of three tanks. The performance of a closed-loop system can be altered by the occurrence of faults which can, in some circumstances, cause serious damage on the system. The research goal is to prevent the system deterioration by developing a controller that has some capabilities to compensate for faults, that is, the fault accommodation or fault-tolerant control. In this paper, a two-step scheme composed of a fault detection, isolation and estimation module, and a control compensation module is presented. The main contribution is to develop a unique structured residual generator able to isolate and estimate both sensor and actuator faults. This estimation is of paramount importance to compensate for these faults and to preserve the system performances. The application of this method to the three-tank system gives encouraging results which are presented and commented on in case of various kinds of faults.     

Fault diagnosis for a class of nonlinear systems via ESO

In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

Diagnostics of stator faults of the single-phase induction motor using thermal images,MoASoS and selected classifiers

Single-phase induction motors are used in the industry commonly. Induction motors are not expensive, so it is a reason to use them. Diagnostics of faults is very important. It prevents financial loss and unplanned downtimes causes by faults. In this paper the authors described fault diagnostic techniques of the single-phase induction motor. Presented techniques were based on the analysis of thermal images of electric motor. The authors measured and analysed 3 states of the single-phase induction motor. In this paper an original method of the feature extraction of thermal images called MoASoS (Method of Area Selection of States) was presented. The proposed method - MoASoS and an image histogram were used to form feature vectors. Classification of the obtained vectors was performed by NN (Nearest Neighbour classifier) and Gaussian Mixture Models (GMM). The described fault diagnostic techniques are useful for reliability of the single-phase induction motors and other rotating electrical machines such as: three-phase induction motors, synchronous motors, DC motors.     

Detection of broken rotor bar faults in induction motor at low load using neural network

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions.