Interturn Fault Detection in Induction Machines Based on High-Frequency Injection

This paper proposes an online improved methodology to detect the inter-turn faults in the star-connected induction machines. The proposed methodology is based on the magnitude of fundamental component in the voltage across the star-points of the machine and the source. Theoretical analysis using the proposed analytical model reveal the influence of the total number of turns/phase-winding on the low-severity inter-turn fault detection using the voltage across the star-points of the machine and the source. The theoretical analysis has been validated experimentally for a 2hp and 0.5hp IMs with 132 and 46 turns per phase respectively. An improved fault indicator to detect the inter-turn faults in different induction machines with various total number of turns per phase has been proposed and evaluated under various loading conditions.

Fault detection in induction machines has become a topic of great interest for researchers due to the growing utility these are giving to today’s industry. Real time machine learning methods are recently proposed in this area to improve the precision of fault detection. In this paper, a novel methodology based on texture feature estimation using an artificial neural network is proposed to be used for fault detection of induction machines. The method is tested for a typical fault as rotor phase opening of a wound rotor induction machine. For this, the stator current in time domain is used to detect the fault based on the proposed method. The experimental results are shown for several loads and speeds conditions to prove the effectiveness of the method.

This paper presents a method based on fusion technique applied to signatures obtained from external stray flux to detect an inter turn short circuits in induction machines. This technique uses the belief functions framework to represent and merge the information about short circuits obtained from sensors placed around the machine to be diagnosed. The influence of the sensors positions around the machine to detect faults is studied. This fusion technique leads to a new diagnosis method, which only uses the information captured from the stray magnetic field around the machine, having then the advantage of being non-invasive. Six external flux sensors placed on a belt fixed around the machine provide information used for the diagnostic technique. These signatures are obtained by experimental tests using a rewound induction machine that allows one to create inter-turn short circuit faults with different severity levels.

This study proposes a method for stator fault detection of inverter-fed induction machine in closed-loop control. The sideband response of inverter switching harmonics in common-mode voltage (CMV) is employed to detect the stator inter-turn fault, thus no additional injected signal is necessary. However, the PWM nature harmonics introduce undesired CMV signals, depending on the inherent imbalance or resistance asymmetry. For this reason, the CMV amplitude is not suitable to indicate the fault directly. In this study, the behaviour of switching harmonic is analysed first, and then a simple but efficient stator fault model is demonstrated. To suppress the adverse interference from inverter, an inter-turn fault indicator is proposed by utilising both of the switching sidebands. This method can obtain fault location and severity with low sensitivity to steady operating condition and stator imbalance. The experimental results validate the effectiveness of the proposed method.

This paper presents a novel method for detecting stator winding inter-turn short circuit fault in a three-phase induction motor by vibration signature analysis using non-electrical contact type microelectromechanical systems (MEMS) accelerometer. The present work detects inter-turn short circuit by vibration analysis in a three-phase induction motor running under different load conditions. Theoretical analysis of stator winding under inter-turn short circuit condition has been presented. Experimental investigations have been carried out to detect inter-turn short circuit fault by vibration signature analysis using a MEMS accelerometer with three-phase induction motor operating under different conditions. The spectral analysis of motor vibration has been carried out using radix-2 decimation in time fast Fourier transform algorithm. The appearance of particular characteristic frequency components in vibration spectrum indicates stator winding inter-turn short circuit faults. The experimental results are found to closely match with the theoretical values, indicating that MEMS accelerometers can be successfully employed for the detection of stator winding inter-turn short circuit fault in induction motors.

Stator and rotor winding damages in rotating machines are result of electrical, mechanical, and thermal stress. Online magnetic field monitoring via permanently installed measuring coils inside air gap is a well-established methodology which enables winding fault detection. The paper deals with a new method for detection of stator and rotor winding inter-turn short circuits of synchronous machines and slip rings induction machines, as well as rupture of rotor bars and cage ring of induction machines. The method novelty is based on differential measurement of magnetic field by using two serial connected measuring coils. They are installed on the places (stator or rotor teeth) in the machine which have, by absolute value, equal magnetic vector potential. The distance between the measuring coils is n·tp, where tp is a pole pitch, and n = 1, 2, 3, 4,... is a multiple of the pole pitch. Measuring the coil-induced voltage enables us to detect stator and rotor winding faults, which means that measured voltage is approximately zero without fault and increases in the presence of fault. Analysis of the measuring signal allows us to detect and locate fault. With this new method it is possible with high sensitivity to determine winding fault, which enables more reliable fault detection. For example, in comparison with the motor current signature analysis method (the most widely used method for motor faults detection), this new method gives 200 times higher sensitivity to fault occurrence. Also, by using the DMFM method, faults can be detected in the time domain and there is no need for spectral or other complex signal analysis. This is very important because the measuring equipment used for machine fault detection can be simple and more economically acceptable. The DMFM method enables fault detection for even small machines with small expense in a very effective way. The only downside of the DMFM method is the fact that machine should be disassembled in order to install measuring coils. This problem is solved during the machine overhaul or during the manufacturing of the machine, when sensors can be easily implemented in the machine. For machines with large air gap, measuring coils can be installed without a machine disassembly. For the purpose of the method testing, numerous finite-element (FE) simulations on the 2- and 3-D machine models have been carried out to verify the method. Powerful numerical tools generate realistic results with properly selected starting and boundary conditions. By FEM models, actual machines with embedded measuring coils where created and simulated. The voltage induced inside the measuring coils is calculated for different machine states, load point and with and without a fault (broken rotor bar or inter-turn short circuit). Also, this method was experimentally validated via series of laboratory tests performed on the real electric machines specially designed for fault study (broken rotor bars, broken ring and inter-turn short circuits in a stator and rotor winding). Additionally, this method is applied on more than 20 real machines in industry. Due to the large amount of measured data, in this paper, it will be presented only one measurement performed on an induction motor on which we have detected one broken rotor bar. The thickness of the measuring coil designed in the printed circuit board technique is 0.3 mm. The number of turns is from 3 to 10. This new method and performed FEM calculations together with the experimental measurements improve fault detection portfolio knowledge that can be used in monitoring and diagnostics of rotating machines. Furthermore, this patent-pending method is already implemented in three innovative products placed on market (expert monitoring systems), so this method is fully confirmed in practice.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

A strategy for interturn short-circuit fault detection in Permanent Magnet Synchronous Machines (PMSM) is presented. Fault detection is performed by a Residual Voltage (RV) obtained from the difference between the midpoint voltages measured on the semi-windings of each stator phase. To evaluate the performance of the proposed strategy a PMSM model with interturn short-circuit fault and partitioned stator phase windings (semi-windings) is presented. Unlike other strategies that analyze the stator fault on the complete phase windings, the RV obtained from the PMSM dynamic model considering semi-windings allows to detect the incipient stator fault on any of the phase windings. The RV monitoring indicates the occurrence of a fault in stator windings, even for unbalanced supply voltages or asymmetric load conditions. Simulation results to evaluate the performance of the proposed strategy based on the RV signal are presented.

Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from MMSPP, established by support vector data description with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of ITSC fault severity.

The aim of this paper is to present a new method for accurate three-phase induction machine fault detection using a simple torque excitation technique with the electrical drive. Since it is well recognize that side band harmonics around the fundamental are good candidates for electrical fault detection in three-phase induction machine, the magnitude evaluation of these harmonics is a tricky problem especially with non-sinusoidal power supply. The idea is to excite more these harmonics without high torque quality deterioration and to evaluate their magnitude for rotor fault detection and localization. The technique is presented and applied on a 11 kW-230V/400V-50 Hz-4 poles three-phase squirrel-cage induction machine.

Electrical machinery is becoming increasingly prevalent throughout modern society, making it imperative to enhance its reliability. These machines are susceptible to various electrical and mechanical faults and must be monitored continuously to ensure safe operation. The presence of any potential fault can result in unplanned downtime, requiring prompt assessment of any abnormal condition in rotating electrical machinery. It is widely acknowledged that the most common fault in these machines is the occurrence of ITSC in the stator. This type of malfunction accounts for 40% of the failures in the induction machine. The ITSC occurrence has hazardous effects on the machine and the associated electrical equipment. Therefore, early detection, along with an accurate isolation and severity estimation of these faults under all operational conditions, enhances the reliability and safety of industrial facilities. This article proposes an online method for detecting and isolating ITSC faults based on estimating the percentage of short-circuited turns in the stator winding. The estimation uses the Extended Kalman Filter, suitable for the nonlinear nature of asynchronous machine models. We conducted a numerical simulation in the MATLAB/SIMULINK environment to evaluate the effectiveness and robustness of the proposed method. The simulation results demonstrate the efficiency of proposed scheme and ensure the detection and the isolation of the ITSC within the induction motor.

This article proposes and demonstrates a fault-detection method to locate interturn short circuits (ITSCs) in the stator windings of a brushless doubly fed induction machine (BDFIM). The detection of ITSC is important in machine health prognostics as ITSC is an early stage fault that may lead to other faults such as coil-to-coil and coil-to-ground. BDFIM consists of power and control windings in the stator that are magnetically coupled through a nested-loop rotor winding. Existing ITSC detection algorithms use rotor slot harmonics in stator current spectra as fault indicators for only conventional doubly fed induction machines. However, these algorithms cannot be applied to BDFIM due to its different stator/rotor winding structure. This article primarily proposes a new analytical formulation for the nested-loop rotor slot harmonics (NRSHs) as ITSC fault indicators in BDFIMs. Using NRSHs, a detection algorithm is also proposed to detect and locate ITSC in power/control windings of BDFIMs. The proposed algorithm is verified based on numerical analysis of a BDFIM using the finite-element method. The accuracy of the proposed fault-detection algorithm is also experimentally investigated and demonstrated using a BDFIM test rig. Both numerical and experimental test results confirm effectiveness of the proposed ITSC fault-detection algorithm.

Motor current signature analysis has been successfully used in induction machines for fault diagnosis. The method however does not always achieve good results when the load torque is not constant. This paper proposes a new approach to motor fault detection, by wavelet decomposition analysis. Theoretical development and experimental results are presented to support the research.

Stator winding inter-turn short circuit (ITSC) accounts for 21 % of faults in Permanent Magnet Synchronous Motors (PMSM). One of the biggest challenges in the area of inter-turn fault is to detect the fault having less than 10% of total turns in a phase. In this paper, an attempt is made to analyse stator inter-turn fault in Surface Mounted Permanent Magnet Synchronous Motor (SPMSM) using Finite Element Method (FEM). A 0.55 kW SPMSM model is obtained from ANSYS Maxwell finite element tool and it is linked to ANSYS Simplorer by transient - transient link to supply power and apply load to the motor. Three phase voltage source inverter with Space Vector Pulse Width Modulation (SVPWM) control technique is created in MATLAB Simulink and coupled with Simplorer to control the machine. Analysis is carried out for different inter-turn fault severities such as 15T (7.2%), 30T (14.4%) and 45T (21.6%) respectively. Various machine parameters such as phase current, torque, speed, flux distribution over the machine and radial air gap flux density are analysed and compared with faulty machines. It has been observed that flux density distribution is less in the stator teeth and core region of faulty coil and the rms value of winding current and radial air gap flux density increases proportionally with severity of ITSC fault. Empirical Wavelet Transform (EWT) is applied to analyse the stator current of SPMSM for various inter-turn severities. It is observed that peak magnitude of the EWT decomposed components increases when there is increase in severity of fault.

Interturn short-circuit faults are the leading cause of power transformer's failure. If not detected at the incipient stage, these faults usually develop into more severe faults that would result in irreversible damage to the transformer, unexpected outages, and consequential losses. This paper presents a transformer interturn fault detection system (TIFDS) for the detection of an interturn fault at the inception stage in power transformers. TIFDS is immune to supply voltage imbalance, constructional asymmetries, and instrumental errors. It also takes care of unequal load distribution. The TIFDS algorithm makes the transformer symmetrical in its entire three phases and sets primary-side neutral current to zero value. This is achieved by computing and applying various correction coefficients. However, under the interturn fault condition, TIFDS characterizes the behavior of neutral current. Simulation and experimentation studies are carried out on the 400/110-V, 3 $\times$ 2.5 kVA power transformer. Presented test results demonstrate the effectiveness of the proposed system.