Identifying three-phase induction motor faults using artificial neural networks

This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults.     

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。     

无刷直流电机逆变器故障诊断仿真研究

无刷直流电机变频调速系统中,逆变器的功率半导体器件及其控制电路是最易发生故障的薄弱环节。故障特征的提取是逆变器故障诊断的关键。逆变器的输出电压直接反映它的工作状态,是最为敏感的特征量,利用加窗傅里叶变换提取其特征频谱,可实现逆变器的故障诊断。仿真结果验证了该方法的有效性。     

An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space

Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset.     

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).     

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.     

MODEL-BASED FAULT DIAGNOSIS OF INDUCTION MOTORS USING NON-STATIONARY SIGNAL SEGMENTATION

Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based fault diagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on an new motor, significantly reducing the system development time.     

基于小波包能量谱和ELM的光伏逆变器多故障在线诊断

以三电平光伏逆变器为研究对象,提出一种多故障模式快速诊断新方法。首先,利用小波包分解提取出三电平逆变器的桥臂电压和上、下管电压信号的能量谱特征向量,并利用主成分分析降维后获取故障特征向量;然后,基于极端学习机诊断模型分离出单器件及多器件开路等多种故障模式。实验结果表明,相比于传统BP神经网络、最小二乘支持向量机故障诊断方法,该方法检测信号易获取,抗干扰性强,诊断速度快、精度高,减小了诊断成本和复杂性,适用于在线诊断。     

Random forests classifier for machine fault diagnosis

This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and reliability of RF-based diagnosis method.     

Implementation of ANN-based Selective Harmonic Elimination PWM using Hybrid Genetic Algorithm-based optimization

A PWM technique with Selective Harmonic Elimination (SHE) is used to control fundamental harmonic and eliminate harmonics of chosen lower-order in voltage source inverters (VSI). Therefore, this PWM technique requires the determination of the optimum switching angles by solving the nonlinear equation set. The determined angles are recorded on a look-up table to generate PWM signals in real-time systems. The paper proposes two Artificial Neural Networks (ANN) based solution for determining angles and generating PWM signals. ANN generates optimum switching angels for all modulation index between 0 and 1.20 because of it has learning capability differently from the look-up table. Primarily, the optimum 11-switching angles for three-phase two-level inverter are determined by using offline Hybrid Genetic Algorithm (HGA). The first ANN was trained by the data obtained from HGA to calculate the switching angles without using a look-up table. Second ANN was trained by using these switching angles to generate PWM signals. The ANN-based SHEPWM was designed to obtain inverter output voltage which has a bipolar waveform with quarter-wave symmetry. The algorithm of ANN-based SHEPWM is performed by using TMS320F28335 Digital Signal Processor (DSP). The experimental results related to dc-link voltage, inverter output voltage and load current are measured for different M_a by using scope and power quality analyzer. The waveform of inverter output voltage are also analyzed with FFT for an induction motor load. The low-order harmonics are successfully eliminated by proposed ANN based SHEPWM.     

基于Park矢量的电动机轴承故障检测方法

提出了一种基于Park矢量的改进聚类处理算法,该方法通过辨识感应电动机三相定子电流中的故障信息来识别轴承故障。为了验证该方法的有效性,在电动机轴承上预设了故障,通过数字信号处理器采集数据,并利用上述方法进行处理。结果表明,所提出的方法可有效识别电动机的轴承故障。     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

Induction machine condition monitoring using notch-filtered motor current

This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. 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 different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of “healthy motors” on the feature space.     

Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current

The main objective of this paper is to diagnose the presence of combined faults in induction machines. For this purpose, a methodology based on the application of the Discrete Wavelet Transform (DWT) to the stator startup current is used. This approach was applied in previous works with success to the diagnosis of rotor asymmetries and mixed eccentricities in motors with different sizes and conditions. However, as most of the diagnosis methods hitherto developed, the application of the proposed approach was circumscribed to situations in which a single fault was present in the machine. In addition, the influence of other phenomena such as load torque oscillations or voltage fluctuations was studied, but without considering the combination of these phenomena and the fault in the machine. This work is intended, first, to apply the proposed transient-based methodology to several cases in which different faults (rotor asymmetries, mixed eccentricities and inter-turn and inter-coil stator short-circuits) are simultaneously present in the machine and, second, to apply it to cases regarding faults combined with other phenomena making difficult the diagnosis, such as load torque oscillations. Interesting considerations regarding the preponderance of the effects of some of the faults are also done in the paper. The application of the methodology is focused on induction machines with stator parallel branches; in this sense, the suitability of the use either of the phase current or of the branch current for the diagnosis of each particular fault is analysed. The results look promising with regard to the validity of the methodology for the reliable discrimination of simultaneous electromechanical faults and the diagnosis of faults combined with other phenomena.     

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.     

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。     

断续空间矢量调制策略下的逆变器开关损耗研究

为减少异步电动机的开关损耗,以逆变器—异步电动机系统的数学模型为基础,采用三种断续空间矢量调制策略—DPWM1,DPWM2,DPWM3仿真和计算逆变器开关损耗,同时与目前广泛使用的SPWM调制策略下的逆变器开关损耗进行比较。仿真结果表明,较之SPWM调制策略,采用断续空间矢量调制策略可以极大的减少异步电动机系统中逆变器的开关损耗。     

基于小波BP神经网络的城轨列车辅助逆变系统故障诊断

在研究城轨列车辅助逆变系统故障诊断模型的基础上,提出一种基于小波包和神经网络相结合的故障诊断方法.该方法首先对采集到的电压信号进行小波降噪,再经过小波包分解和重构,构造特征向量,以此为故障样本对BP（Back propagation）神经网络进行训练,实现智能化故障诊断.实验结果表明：该方法能够很好地诊断出城轨列车辅助逆变系统的故障类型,这为辅助逆变系统的故障诊断和故障动态监测提供了新的参考,具有一定的工程应用价值.     

基于直流比值法的低压电缆故障检测装置设计

为了保证低压电缆运行的安全性和稳定性,提出基于直流比值法的低压电缆故障检测装置设计方法,该方法在检测装置的硬件设计中设计了信号参考与探测通道、电源模块、信号采集模块和磁电处理电路,将AD605线性放大器设置在磁电处理电路中,以此提高信号的信噪比。在软件设计中利用直流比值法,将投切电阻接入装置母线中,通过电压和漏电流的变化量比值,完成低压电缆故障的检测,实现低压电缆故障检测装置的设计。实验结果表明,所提方法可准确完成低压电缆故障的识别与定位。     

Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis

In this paper, a multiple model (MM)-based detection and estimation scheme for gas turbine sensor and gas path fault diagnosis is proposed, which overcomes the coupling effects between sensor faults and gas path faults, and simultaneously realizes an accurate diagnosis of sensor and gas path faults. First, an adaptive fault detection and isolation (FDI) framework based on the MM method was established to detect and isolate sensor faults and gas path faults. Then, a fault amplitude estimation method was proposed according to the FDI results, and a fault validation method based on the Chi-square test was proposed to confirm the actual fault. Finally, hardware in the loop (HIL) simulation platform was established to validate the effectiveness of the proposed method. Several simulation case studies were conducted based on a two-shaft marine gas turbine with common gas path faults and sensor faults. The simulation results show that the proposed method can accurately diagnose the fault and estimate the corresponding fault amplitude when both the sensor fault and the gas path fault coincide.