Synchrosqueezing transform-based methodology for broken rotor bars detection in induction motors

Due to the relevance and severity of damages caused in induction motors by broken rotor bars (BRBs), the development and application of new detection methods that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. This paper presents a methodology based on the Synchrosqueezing transform for detection of BRBs during the startup transient. In order to validate the proposal, a synthetic signal with different noise levels and real current signals of an induction motor are analyzed. Three severities of damage are considered: half broken, one broken, and two broken rotor bars. For automatic diagnosis, a threshold-based stage using the Pearson product-moment correlation coefficient is presented. Both synthetic and real experiment results demonstrate the proposal effectiveness.     

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.     

Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor

This paper presents a new approach to detect the location of multiple broken rotor bars (MBRBs) in induction motor (IM) drive, running under no load and full load conditions using direct in and variable frequency drives. This technique is based on earlier work of location detection of one broken rotor bar. The techniques are tested for various fault severity levels so the detection of the exact location of the fault at early stage helps to reach sufficient time maintenance. In this paper, the authors used Hilbert Transform to extract the fault signature from the stator current envelope which is the low frequency component. Then statistical analysis is applied which produce a formula that is used to get the exact location of the fault in IM rotor.     

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.     

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.     

Adaptive Sliding Kalman Filter using Nonparametric Change Point Detection

This paper aims to develop an Adaptive Sliding Kalman Filter (ASKF) by fusing the concept of change detection in a data stream, adapting noise covariance matrices and the Sliding Kalman filter (SKF). Adaptive Kalman filtering (AKF) scheme modifies the noise covariance matrix (Q and R) value based on a window of past innovation sequence whereas SKF is a window based filtering technique which uses past information to obtain the present state estimate. However, the length of the window chosen for SKF and AKF is arbitrary and a scheme has been devised here to adapt this window length according to the data stream statistics. The change detection scheme chosen here does not make any assumption on the data distribution and is sequential in nature, such that a change is triggered whenever the underlying statistics of data crosses a pre-determined threshold. The key contribution of this work is toward the formulation of a mechanism by which the window length is made adaptive such that whenever a change is detected, the window length for SKF and AKF is curtailed and restarted in an oscillatory windowing fashion. The suggested filter is robust against temporary uncertainties and appropriate for reliable estimation of signals that may arise in many engineering areas. Real world experimental results demonstrate better estimation accuracy of the proposed method than that of others.     

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.     

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known.     

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.     

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.     

Incorporating delayed measurements in an improved high-degree cubature Kalman filter for the nonlinear state estimation of chemical processes

The on-line estimation of process quality variables has a large impact on the advanced monitoring and control techniques of chemical processes. The present study offers an improved high-degree cubature Kalman filter (HCKF) to solve the nonlinear state estimation problem of high-dimensional chemical processes. We substituted the Cholesky decomposition in the HCKF filter with a diagonalization transformation of the matrix. In addition, we enhanced numerical stability and estimation accuracy. On this basis, we present one nonlinear state estimation method based on the sample-state augmentation and improved HCKF to handle issues with delayed measurements. Finally, we used the nonlinear state estimation experiments for the polymerization process to validate the proposed method. The numerical results indicated the achievement of state estimation with higher accuracy and better stability following the effective utilization of the delayed measurements for nonlinear chemical processes.     

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.