Faults In Three-phase Induction Motors Research Articles

Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain

Three-phase induction motors are widely used in different applications in the industry due to their robustness, low cost, and reliability. Untimely identification and correct diagnosis of incipient faults reduce cost and improve the maintenance management of these machines. This paper explores a new method for robust classification of rotor failures in three-phase induction motors (MITs) connected directly to the electrical network, operating in a steady-state, under unbalanced voltages and load conditions. Through an innovative methodology, an analysis of the electrical current signals from 1 hp and 2 hp motors in the frequency domain was performed. Such analysis was applied in constructing input matrices for a Multilayer Perceptron Neural Network (MLPNN) to detect faults. Furthermore, this methodology proved to be robust because the samples of the failing and healthy motors include voltage unbalance conditions in the electrical supply and a significant variation in the load applied to the motor shaft. Such load variation was used for the detection of failures of 1, 2, and 4 broken bars consecutively on the rotor and in the condition of 2 broken bars and 2 other broken bars diametrically opposite. The results were promising and were obtained using 847 real samples from an experimental bench used to construct the neural model and its respective validation.

Application of Frequency Response Analysis Method to Detect Short-Circuit Faults in Three-Phase Induction Motors

The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.

Sensorless FOC Strategy for Current Sensor Faults in Three-Phase Induction Motor Drives

Current sensors are required in Field-Oriented Control (FOC) strategies of Three-Phase Induction Motor (TPIM) drives. Nevertheless, the current sensors are subject to different electrical/mechanical faults which reduce the safety and dependability of the drive system. Single phase current sensor Fault-Tolerant Control (FTC) for sensorless TPIM drives using flux observer and Extended Kalman Filter (EKF) is proposed in this research. In the suggested FTC scheme, current sensor fault detection is based on axes transformation, a logic circuit is served as the fault isolation and reconstruction of faulted currents are achieved through flux observer and EKF. The presented FTC system is capable of detecting and localizing the current sensor fault and switching the drive system to tolerant FOC mode without the rotor speed measurement. The effectiveness of the suggested FTC system is confirmed by experiments on a 0.75kW TPIM drive platform.

Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor

Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.

Electrical fault detection in three-phase induction motor using deep network-based features of thermograms

In this paper, an automatic method is proposed for detecting the operating faults in three-phase induction motors based on thermal images. If these faults are not detected or fixed on time, they can lead to permanent motor failure. This is why non-invasive and non-destructive experiments are significantly considered. In this paper, first, the region of interest is detected in the thermograms using SIFT-based key-points matching. Then, these images are transformed into representative feature vectors based on a pre-trained convolutional neural network. Then, the training vector samples are clustered into cold and hot clusters by K-means. For each cluster, an SVM-based classifier is trained. The test feature vector samples are clustered and mapped into classes using the corresponding trained SVM-based classifiers. Evaluating the proposed method on the datasets including real thermal images, shows that this algorithm can detect 100% of the faults of the induction motor.

Axial Stray Flux Sensor Proposal for Three-Phase Induction Motor Fault Monitoring by Means of Orbital Analysis

This paper proposes a new form of stray flux sensor, placed on the inner side of the machine rear end plate, in order to monitor the presence and evolution of some most common faults in three-phase induction motors. The fault indicator is based on analysis of orbits built from time flux signals collected by the sensor. The effectiveness of this methodology was checked considering three types of faults under three different load conditions: incipient inter-turn short circuits, unbalanced voltage supplies and misalignment, all with 0%, 50% and 100% loads. The experimental results showed a viability of the technique for diagnosing and monitoring of induction motors. This can be adapted and used in predictive maintenance in industry.

A new methodology for detection of bearings faults in three-phase induction motor

This study shows a methodology for the detection, classification, and location of bearings that presented ball faults, cage faults, and outer race faults. For this study, a three-phase induction motor was used, in which the stator current and voltage signals were measured. By calculating the total harmonic distortion and using the Stockwell Transform, different characteristics were obtained in the electrical signals that allowed defining fault conditions in the bearing, classification of the type of fault, and the location of the defective bearing (fan side or load side). By calculating the difference between the total harmonic distortion of the current and voltage signal, it is possible to identify a threshold value of 0.004 that separates a healthy condition and a fault condition. The joint use of the Stockwell Transform and the Fisher Scoring Algorithm allows us to classify the fault conditions with an average precision of 92.5%. The location of a bearing with defects on the load side generates a greater amplitude in the signal compared to those located on the fan side. This behavior allows establishing a threshold value of 1.6 for ball faults and 0.001 for cage faults and outer race. Due to the results obtained, the algorithm proposed in the study is considered to be a tool with a high degree of reliability for the diagnosis of bearings in induction motors.

Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. 
 Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform

Non-Intrusive Methods to Detect Air-Gap Eccentricity Faults in Three-Phase Induction Motor

The focus of this paper is model-based behavioural analysis for the diagnosis of the air-gap eccentricity fault condition in the three-phase induction machine. To facilitate reliable and sensitive diagnosis, an online condition monitoring system has to depend on multiple signals and signatures. Air-gap mixed eccentricity fault signatures derived from three non-invasive methods such as stator line current, instantaneous power, and estimated air-gap torque are presented. Modified winding function theory and multiple coupled circuit approaches are used to model the machine. The sidebands present around the fundamental frequency in the spectrum of stator current, and around double the supply frequency in the spectrum of instantaneous power indicate the presence of air-gap mixed eccentricity. The low frequencies which exist near the DC component in the spectra of the air-gap torque and instantaneous power also indicate the same. These specific components were also observed in the high-frequency spectra around the principal slot harmonics. The modelling approach, the torque estimation approach, the simulation results at different severity and load conditions, and the experimentation result in a motor with prefabricated eccentricity are presented. Copyright © 2020 Praise Worthy Prize S.r.l. - All rights reserved.

Intelligent nonmodel-based fault diagnosis of electric motors using current signature analysis

This paper proposes an efficient technique for detecting mechanical faults in three-phase induction motors, without using mechanical sensors. Only measurements of the currents of every phase are used to identify the fault. The proposed system can diagnose two types of faults corresponding to shaft misalignment or imbalance, along with normal operation. The power spectrum of the experimental data is generated, followed by applying a soft-computing mathematical algorithm that will extract the peaks of the fundamental frequencies and their harmonics, while filtering out noise. Carefully selected peaks at certain frequencies will be collected and examined to generate a robust algorithm that can be used to produce a decision regarding the operating condition of the motor, via applying an intelligent soft-computing technique. Mathematical details regarding the consistency checks for validating the experimental data, and the testing/validation phases will be investigated. Detailed analysis of the obtained results is provided to highlight the advantages and limitations of the proposed algorithm. In addition, a comparison is made with similar techniques that use mechanical sensors to contrast their differences and highlight the superiority of the proposed system. The obtained results prove the intelligence and robustness of the proposed system and allow for versatile extensions that promote its application in real-time scenarios for many industrial applications.

Stator Short-Circuit Diagnosis in Induction Motors Using Mutual Information and Intelligent Systems

This paper proposes a novel approach for detection of stator short-circuit faults in three-phase induction motors. The method is based on two stages: feature extraction and classification by intelligent systems. First, mutual information is estimated from delayed stator current signals, which are used as inputs of C4.5 decision trees and multilayer perceptron neural networks in the second step. Several offline and online experimental tests are presented considering voltage unbalance, load torque variations, and 1% to 10% short-circuit levels. The obtained results corroborate the effectiveness of this new diagnostic approach.

A Modified Direct-Quadrature Axis Model for Characterization of Air-gap Mixed Eccentricity Faults in Three-Phase Induction Motor

Advanced signal processing techniques and high-speed analog to digital converters enabled on-line detection of internal faults of induction motor even at inception. Reliable and accurate identification of fault signatures in practical situations is always a challenge due to load oscillations, supply harmonics or the presence of multiple faults. Hence model-based analyses are essential for diagnostic studies of faults in machines. This paper proposes a modified direct and quadrature (d-q) axis based approach for modeling a three-phase squirrel cage induction motor with air-gap mixed eccentricity faults. In the proposed model, air-gap length- and thus magnetizing reactance- are modeled as a rotor position-dependent function, to represent various variable air-gap fault conditions. Stator current spectrum is used as the diagnostic signal for detection of the presence of these faults. This simple approach of modeling is computationally less intensive compared to alternative approaches such as multiple coupled circuit modeling and finite element approach. Characteristic signatures of mixed eccentricity fault obtained by simulation studies were also validated in the motor current spectrum obtained through experimentation on a motor with prefabricated eccentricity.

Robust simultaneous estimation of stator and rotor resistances and rotor speed for predictive maintenance of sensorless induction motor drives

In this paper, a novel rotor and stator resistances estimator has been proposed for detecting faults while they are still developing or adequate warning of imminent failure and to give minimum downtime and optimum maintenance schedules to apply in sensorless IM drives. The stator resistance estimation is applied for monitoring stator windings temperature and monitoring the status of stator windings insulation of induction motor. On the other hand, broken bars and end-rings are common faults in three-phase induction motors. A parallel model reference adaptive system (MRAS) has been proposed in order for simultaneous estimation of the stator and rotor resistances and rotor speed. The aim of the proposed MRAS is to save the cost of implementation and the processes time. The proposed parallel MRAS depends on the point of slowly variation of the rotor resistance with respect to the rotor speed.

Robust simultaneous estimation of stator and rotor resistances and rotor speed for predictive maintenance of sensorless induction motor drives

In this paper, a novel rotor and stator resistances estimator has been proposed for detecting faults while they are still developing or adequate warning of imminent failure and to give minimum downtime and optimum maintenance schedules to apply in sensorless IM drives. The stator resistance estimation is applied for monitoring stator windings temperature and monitoring the status of stator windings insulation of induction motor. On the other hand, broken bars and end-rings are common faults in three-phase induction motors. A parallel model reference adaptive system (MRAS) has been proposed in order for simultaneous estimation of the stator and rotor resistances and rotor speed. The aim of the proposed MRAS is to save the cost of implementation and the processes time. The proposed parallel MRAS depends on the point of slowly variation of the rotor resistance with respect to the rotor speed.

A novel multi-agent approach to identify faults in line connected three-phase induction motors

Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.

Neural Approach to Fault Detection in Three-phase Induction Motors

Three-phase induction motors play a key role in electromotive force production, although their widespread use in industrial applications leads to a variety of defects that can impair their normal operation. This paper proposes an alternative method, based on artificial neural networks, for classifying and detecting bearing faults in three-phase induction motors connected directly to the power grid. Experimental tests conducted in the laboratory consider practical conditions such as voltage unbalances and differing load torques. Analyses are performed in the time domain, and are based on electric motor quantities such as voltages and currents, which are acquired considering a half-cycle of the voltage grid. The performance and efficacy of the proposed fault detection method is evaluated and validated by using a personal computer to conduct online experimental tests, and by embedding in digital signal processors. The proposed method is also tested in electrical machines used in the sugar cane industry.

Predictive Monitoring of Induction Motors via Magnetic Flux Analysis

The diagnosis of defects with the analysis of magnetic flux has been considered for equipment manufactures of the machine monitoring area. However, this technique has not been deep for researches. Through preliminary studies already some advantages have been found in relation to the conventional techniques of current analysis, as for example, not necessity of knowledge of the motor load, the number of bars and slots for the detection of turn-to-turn short circuit in the stator. This work considers the exploration of this technique for the detection of electric faults in three-phase induction motors. Theoretical and practical aspects, as well advantages and advantages of the method considered are discussed from the application of this method in the diagnosis of faults introduced in a workbench of tests, composed by an electric motor of 5 CV, 4 poles and controlable mechanical load.

Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants

Stator faults typically have a significant share amongst the common type of faults in industrial three-phase induction (asynchronous) motors. This paper presents a motor current signature analysis (MCSA)-based diagnostics of the stator winding short circuit fault. This type of fault happens due to the destruction of the turn insulation, and can be very detrimental causing motor shutdown. Instead of traditional MCSA using the motor stator current, in this paper, analysis using the zero crossing time (ZCT) signal of the stator current is presented. The theoretical aspects of the stator short circuit detection are presented. Following that, a diagnostic algorithm utilizing the ZCT signals is proposed. Experiments are performed with real motors, healthy and with shorted stator windings. Frequency analysis of the ZCT signals from the experimental data substantiates the theoretical arguments with significant accuracy.

A new method for early fault detection and diagnosis of broken rotor bars

A new method has been developed for the detection and diagnosis of broken rotor bars faults in three-phase induction motors under no-load conditions. Early detection of faults is made by using a sliding window constructed by Hilbert transforms of one of the phases of the thee-phase currents and the size of a fault is diagnosed by motor current signature analysis (MCSA) of the stored Hilbert transforms of several periods of one-phase current. The information entropy of a symbol tree generated by each sliding window is used as a fault index. The method was tested using healthy and damaged 0.37 kW induction motors under no-load conditions with applied voltages ranging from 220 V to 380 V. One and two broken rotor bars were detected under no-load conditions when supply voltages were 260 V and above. The results indicate that the method yields a high degree of accuracy in fault identification.