Online Diagnosis of Induction Motors Using MCSA

In this paper, an online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed. MCSA is a method for motor diagnosis with stator-current signals. The proposed system diagnoses induction motors having four types of faults such as breakage of rotor bars and end rings, short-circuit of stator windings, bearing cracks, and air-gap eccentricity. Although MCSA is one of the most powerful online methods for diagnosing motor faults, it has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced signal-and-data-processing algorithms are proposed. They are composed of an optimal-slip-estimation algorithm, a proper-sample-selection algorithm, and a frequency auto search algorithm for achieving MCSA efficiently. The proposed system is able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results obtained on 3.7-kW and 30-kW three-phase squirrel-cage induction motors and voltage-source inverters with a vector-control technique are discussed     

Current signature analysis of induction motor mechanical faults by wavelet packet decomposition

This paper presents a novel approach to induction motor current signature analysis based on wavelet packet decomposition (WPD) of the stator current. The novelty of the proposed method lies in the fact that by using WPD method the inherent nonstationary nature of stator current can be accurately considered. The key characteristics of the proposed method are its ability to provide feature representations of multiple frequency resolutions for faulty modes, ability to clearly differentiate between healthy and faulty conditions, and its applicability to nonstationary signals. Successful implementation of the system for two types of faults, i.e., rotor bar breakage and air-gap eccentricity is demonstrated here. The results are validated based on both simulation and experiments of a 5-hp induction motor.     

Differential Diagnosis Based on Multivariable Monitoring to Assess Induction Machine Rotor Conditions

Multivariable supervision systems for online monitoring of induction motors allow large versatility and diagnosis robustness. As regards rotor faults, the diagnostic procedure based on sideband current components may fail due to the presence of interbar currents that reduce the degree of rotor asymmetry and, thus, the amplitude of these spectral components. On the other hand, the interbar currents produce core vibrations in the axial direction; these vibrations can be detected using a suitable vibration sensor. In this paper, a differential fault analysis based on traditional motor current signature analysis (MCSA) and on radial and axial vibration monitoring is proposed to discern cases in which the presence of interbar currents decreases the sensitivity of MCSA. The features of stator currents and of radial and axial core vibration signals are investigated in order to increase the reliability of the diagnostic system. Moreover, to explore the possibility of obtaining further information, stray flux signals are taken into account.      

A review of induction motors signature analysis as a medium forfaults detection

This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without “state-of-the-art” terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing     

On-Line Monitoring of Mechanical Faults in Variable-Speed Induction Motor Drives Using the Wigner Distribution

This paper deals with the detection of mechanical load faults in induction motors during speed transients. The detection strategy is based on stator current analysis. Mechanical load faults generally lead to load torque oscillations at specific frequencies related to the mechanical rotor speed. The torque oscillations produce a characteristic sinusoidal phase modulation of the stator current. Speed transients result in time-varying supply frequencies that prevent the use of classical, Fourier transform-based spectral estimation. This paper proposes the use of a time-frequency distribution, the Wigner Distribution, for stator current analysis. Fault indicators are extracted from the distribution for on-line condition monitoring. The proposed methods are implemented on a low-cost digital signal processor. Experimental results in a steady-state and during transients with load torque oscillations and load imbalance are presented.     

Influence of the motor load inertia and torque in the fault diagnosis of rotors in induction machines

During the last decades, non-invasive techniques have been proposed to carry out the fault diagnosis in electric machines. When these techniques are employed in the fault detection of the induction machine's rotors, they exhibit a strong dependence on factors such as the motor load inertia or the opposed torque. In order to develop automatic diagnostic systems or the diagnosis assistance over the rotor state, such as expert systems or knowledge based systems, it is necessary to have available further information to weigh up the influence of these factors. This work presents a study done, based on induction motor's mathematical models, about the incidence of the motor inertia and the opposed torque in some non-invasive fault detection technique employed more frequently. The model takes into account in an independent way each of the rotor bars and then allows to represent different faulty situations. These techniques with non-invasive features do not require sensors directly over the motor and thus allow a diagnosis even on-line when the machine is running. Particularly the study of the following techniques was approached: power spectral analysis, torque spectral analysis, stator current spectral analysis and Park's current vector behaviour. Therefore the diagnostic may be based in the measurement of motor's external variables such as applied voltages and stator currents.     

The detection of inter-turn short circuits in the stator windingsof operating motors

This paper develops a winding-function-based method for modeling polyphase cage induction motors with inter-turn short circuits in the machine stator winding. Analytical consideration which sheds light on some components of the stator current spectra of both healthy and faulty machines is developed. It is shown that, as a result of the nature of the cage rotor, no new frequency components of the line current spectra can appear as a consequence of the fault. Only a rise in some of the frequency components which already exist in the line current spectra of a healthy machine can be observed. An experimental setup comprising a 3 kW delta-connected motor loaded by a generator was used to validate this approach. The experimental results obtained clearly validate the analytical and simulation results     

Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor

The positive features of neural networks and fuzzy logic are combined together for the detection of stator inter-turn insulation and bearing wear faults in single-phase induction motor. The adaptive neural fuzzy inference systems (ANFISs) are developed for the detection of these two faults. These faults are created experimentally on a single-phase induction motor in the laboratory. The experimental data is generated for the five measurable parameters, viz, motor intakes current, speed, winding temperature, bearing temperature, and the noise of the machine. Earlier, the ANFIS fault detectors are trained for the two input parameters, i.e., speed and current, and the performance is tested. Later, the three remaining parameters are added and the five input ANFIS fault detector is trained and tested. It observed from the simulation results that the five input parameter system predicts more accurate results     

A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power Factor

Fault detection and diagnosis of asynchronous machine has become a central problem in industry over the past decade. A solution to tackle this problem is to use stator current for a condition monitoring, referred to as motor current signature analysis. This paper argues that bearing faults would have a negligible effect on motor currents and instead argues that the more likely reason why the faults can be detected in currents is because they entail a fluctuating resistive torque which acts immediately, in contrast to the radial displacement which takes time to integrate to a perceptible displacement even in response to a step change in velocity. In this context, we propose a new method for detecting bearing defects based on the exploitation of the instantaneous power factor that varies according to torque oscillations. Experimental results show the good performances of the proposed method which will be compared with the instantaneous power method to highlight the feasibility and advantages of this method.      

Microprocessor-Based Field-Oriented Control of A CSI-Fed Induction Motor Drive

This paper describes the method of field orientation of the stator current vector with respect to the stator, mutual, and rotor flux vectors, for the control of an induction motor fed from a current source inverter (CSI). A control scheme using this principle is described for orienting the stator current with respect to the rotor flux, as this gives natural decoupling between the current coordinates. A dedicated micro-computer system developed for implementing this scheme has been described. The experimental results are also presented.     

An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor

This paper presents a neural approach to detect and locate automatically an interturn short-circuit fault in the stator windings of the induction machine. The fault detection and location are achieved by a feedforward multilayer-perceptron neural network (NN) trained by back propagation. The location process is based on monitoring the three-phase shifts between the line current and the phase voltage of the machine. The required data for training and testing the NN are experimentally generated from a three-phase induction motor with different interturn short-circuit faults. Simulation, as well as experimental, results are presented in this paper to demonstrate the effectiveness of the used method.      

A new stator resistance tuning method for stator-flux-orientedvector-controlled induction motor drive

Field-oriented-controlled induction motor drives have been widely used over the last several years. Conventional direct stator-flux-oriented control schemes have the disadvantage of poor performance in the low-speed operating area when the stator flux is calculated using the voltage model, due to the stator resistance uncertainties and variations. In this paper, a new closed-loop stator-flux estimation method for a stator-flux-oriented vector-controlled induction motor drive is presented in which the stator resistance value is updated during operation. This method is based on a simple algorithm capable of running in a low-cost microcontroller, which is derived from the dynamic model of the induction machine. The effects of stator resistance detuning, especially in the low-speed operating region, are investigated and simulation results are shown. The motor drive system as well as the control logic and the resistance estimator are simulated and characteristic simulation results are derived. In addition, the proposed control scheme is experimentally implemented and some characteristic experimental results are shown. The simulation as well as the experimental results reveal that the proposed method is able to obtain precise flux and torque control, even for very low operating frequencies     

Analysis of Performance of Adjustable-Speed Drives during Voltage Sags

The induction motors are the most common electric machines on industrial systems and with extended applications when adjustable speed drives (ASD) are used. The speed drives are based on power electronic devices and therefore they are highly sensitive to electric disturbances such as voltage sags, interruption, etc. Voltage sags has become one of most common power quality problems in the electrical systems, producing negative effects mainly in loads with power electronic technology. In this paper, the analysis of the effects produced by voltage sags in the ASD and the induction motor are presented. The electric system used for the analysis is conformed by an induction motor, an AC drive with V/Hz control scheme and a step down transformer connected in Yd. The voltage sags were produced by faults in the electric system with a time duration of 6 cycles (0.1 s). The whole electric system was modeled and simulated in Matlab/Simulink environment. The operating conditions of the induction motor was 80% of nominal speed and full load. The obtained results show high sensitivity of the drive, mainly to the dc-link voltage drop, resulting in a motor speed drop and overcurrents in the drive feeders at the ending sag. The adopted parameters used as a limit for the speed drive disruption were 5% of variation in the motor speed and 1.5 p.u. for the peak current. The most severe effects occur with sags type A and G due to three-phase and two-phase to ground faults respectively. The effect of these sags produced a dc-link voltage drop higher than 30% and therefore the drive disruption as a result of the operating limits exceeded. With voltage sags type C and D, caused by single-phase to ground and two-phase faults respectively, the effects produced in the drive and the motor are negligible.     

vector-controlled PWM current-source-inverter-fed induction motor drive with a new stator current control method

In this paper, the control of the pulsewidth-modulated current-source-inverter-fed induction motor drive is discussed. The vector control system of the induction motor is realized in a rotor-flux-oriented reference frame, where only the measured angular rotor speed and the dc-link current are needed for motor control. A new damping method for stator current oscillations is introduced. The method operates in an open-loop manner and is very suitable for microcontroller implementation, since the calculation power demand is low. Also, the stator current phase error caused by the load filter is compensated without measurement of any electrical variable. With the proposed control methods the motor current sensors can be totally eliminated since the stator current measurements are not needed either for protection in the current-source-inverter-fed drives. The proposed control methods are realized using a single-chip Motorola MC68HC916Y1 microcontroller. The experimental tests show excellent performance in both steady-state and transient conditions.     

Intelligent fault diagnosis system of induction motor based on transient current signal

This paper presents a method for induction motor fault diagnosis based on transient signal using component analysis and support vector machine (SVM). The start-up transient current signal is selected as features source for fault diagnosis. Preprocessing of transient current signal is performed using smoothing and discrete wavelet transform to highlight the salient features of faults. In this work, independent component analysis, principal component analysis and their kernel are performed to reduce the dimension of features and to extract the optimal features for classification process. In this work, the influence of the number of component analysis towards diagnosis accuracy is also studied. SVM multi-class classification using one against all strategy is selected for classification tool due to good generalization properties. Performance of the system is validated by applying the system to induction motor faults diagnosis. According to the result, the system has potential to serve an intelligent fault diagnosis system in real application.     

Digital control of induction motor current with deadbeat responseusing predictive state observer

For a high-power induction motor drive, the switching frequency of the inverter cannot become higher than one kilohertz, and such a switching frequency produces a large current ripple, which then produces torque ripple. To minimize the current ripple, a method based on deadbeat control theory for current regulation is proposed. The pulsewidth modulation (PWM) pattern is determined at every sampling instant based on stator current measurements, motor speed, current references, and rotor flux vector, which is predicted by a state observer with variable poles selection, so that the stator currents are controlled to be exactly equal to the reference currents at every sampling instant. The proposed method consists of two parts: (1) derivation of a deadbeat control and (2) construction of a state observer that predicts the rotor flux and the stator currents in the next sampling instant. This paper describes a theoretical analysis, computer simulations and experimental results     

A comparison of external and internal methods of signal spectral analysis for broken rotor bars detection in induction motors

Like all mechanical devices, motors are subject to failures, which can sometimes lead to the shutting down of an entire industrial process. This paper looks at failure predictions in three-phase line-operated induction machines through spectral analysis or electric and electromagnetic signals. Fault characteristics frequencies generated in the estimated and the measured signal spectrum, as a result of mechanical abnormalities such as broken rotor bars, are analyzed. Spectral analyses of simple stator current, of the current's Park vector modulus, and or total and partial instantaneous electric powers are considered as external diagnosis. Internal methods of diagnosis are usually based on a mathematical model of the motor. This requires knowledge of the motor's electrical parameters, which are affected by a number of physical phenomena such as temperature variations, skin effects, core losses, and saturation. As internal diagnosis, we examine different approaches to the spectral analysis of electromagnetic torque computed by stator and rotor flux estimation. To this end, the open loop method, the Luenberger observer and the Kalman filter are employed. Finally, experimental results enable us to draw up a table of comparison of internal and external methods in the detection of rotor imperfections, using two criteria under different load levels.     

Monitoring of induction motor load by neural network techniques

This paper deals with the electric tracing of the load variation of an induction machine supplied by the mains. A load problem, like a torque dip, affects the machine supply current and consequently it should be possible to use the current pattern to detect features of the torque pattern, using the machine itself as a torque sensor. But current signature depends on many phenomena and misunderstandings are possible. At first the effect of different load anomalies on current spectrum, in comparison with other machine problems like rotor asymmetries, are investigated. Reference is made to low frequency torque disturbances, which cause a quasistationary machine behavior. Simplified relationships, validated by simulation results and by experimental results, are developed to address the current spectrum features. In order to detect on-line anomalies, a current signature extraction is performed by the time-frequency spectrum approach. This method allows the detection of random faults as well. Finally it is shown that a neural network approach can help the torque pattern recognition, improving the interpretation of machine anomalies effects     

A new scheme for speed-sensorless control of induction motor

Various control algorithms have been proposed for the speed-sensorless control of an induction motor. These sensorless algorithms are mainly based on the speed feedback with the flux and speed estimations. This paper proposes a new scheme for the speed-sensorless control of an induction motor. The proposed scheme is based on the current estimation without the flux and speed estimations, in which the controlled stator voltage is applied to the induction motor so that the difference between stator currents of the mathematical model and motor may be forced to decay to zero. The performance of the proposed scheme is verified through simulation and experiment.     

A Current Source Inverter Fed Induction Motor Drive with Power Factor Angle Control Using Microprocessor

This paper describes a method of adjusting the stator power factor angle for the control of an induction motor fed from a current source inverter (CSI) based on the concept of space vectors (or park vectors). It is shown that under steady state, if the torque angle is kept constant over the entire operating range, it has the advantage of keeping the slip frequency constant. This can be utilized to dispose of the speed feedback and simplify the control scheme for the drive, such that the stator voltage integral zero crossings alone can be used as a feedback for deciding the triggering instants of the CSI thyristors under stable operation of the system. A closed-loop control strategy is developed for the drive based on this principle, using a microprocessor-based control system and is implemented on a laboratory prototype CSI fed induction motor drive.