Failures In Induction Motors Research Articles

Health monitoring and fault analysis of induction motors: a review

Abstract Induction motors are in high demand for industrial applications due to their reliable and efficient performance compared to other electrical motors. Therefore, the implementation of a predictive maintenance scheme is necessary to minimise financial losses in terms of revenue and maintenance costs. This scheme involves health monitoring system, aimed to enhance motor reliability, extend their useful lifespan and reduce maintenance expenses. Furthermore, traditional health monitoring systems often involve human intervention, which can slow down the diagnosis process. Therefore, a self-sufficient automated system has evolved to improve motor performance and reliability. This paper presents a comprehensive study of induction motor failures and the various methods used for their diagnosis. Additionally, it also discussed the importance of predictive maintenance and the pivotal role played by automated systems in ensuring the sustained efficiency of induction motors in industrial settings.

Induction Motor Failure Identification Based on Multiscale Acoustic Entropy Feature Selection and Hierarchical Adaptive Neuro-Fuzzy Inference System With Localized Recurrent Input

Induction Motor Failure Identification Based on Multiscale Acoustic Entropy Feature Selection and Hierarchical Adaptive Neuro-Fuzzy Inference System With Localized Recurrent Input

Bearing fault diagnosis for electric machines using motor current signals and state classification

Procedures for diagnosing bearing failures of induction motors based on motor current signals in published methods commonly denoise signals acquired from current sensors, then extract the typical characteristics from the denoised signals, and use a classifier to discriminate the state of bearing. However, the current signals in practice could be affected by surrounding noises, creating extraordinary peaks in the signals which may lead to an inaccurate diagnostic result. Thus, the traditional methods may not be very effective in diagnosing failures for induction motors using motor current signals in real-time. To mitigate these issues, this work introduces a new technique, which consists of characterizing the bearing health as a state vector composed of signal features, evaluating the real bearing status from the characteristic-space using a Kalman filter and a k-NN classifier. This technique still achieves quite high precision even in noisy condition. Experimental results with noise-adding signals demonstrate that the proposed technique has a mean proper identification proportion of 92.06% and a mean false proportion of 7.94%, while the conventional ones turn out a maximum mean true identification proportion of 53.12 % and a minimum mean mistake proportion of 46.88%.

AC Motor Monitoring and Controlling Using IoT

The design of IOT technology is shown to monitor and diagnose the performance of a three-phase induction motor and record crucial operational characteristics. Today's technology is rapidly tied to the Internet of Things (IOT), keeping things connected efficiently. For the purpose of gathering and processing induction motor parameters, the solutions given include an IOT-based platform. The parameters are made up of sensors, including humidity, temperature, voltage, and current sensors. With the help of the pocket IOT application, this data may be shown on a smartphone, accessed via web sites, and stored in a cloud platform. It will be promptly informed if performance limits are exceeded. To prevent motor downtime, an induction motor can be checked and immediate action taken can save money and time. Utilising IOT to monitor induction motors has several benefits, including notification of problem alerts and historical data for preventative maintenance. Recent technological advancements have greatly improved the quality, speed, and ease of our lives. This article explains how to manage and control induction motors (IOT) using the Internet of Things. The IOT is more effective and convenient for controlling systems because it can be used from anywhere via Wi-Fi. This intelligent system's main objective is to prevent induction motor failure by taking preventive measures. Because of its many benefits, including their self-starting nature, low cost, high power factor, and robust construction, induction motors are utilised in a wide range of applications, including those for electric vehicles, businesses, and agricultural areas. In order to maximise motor efficiency and assure safe and reliable operation, it is crucial to identify defects in motors as soon as possible using the best smart protection approach currently available. Remote monitoring is possible for the induction motor's speed, voltage, current, temperature, humidity, and other electrical, mechanical, and environmental parameters because errors in these areas seriously damage the motors and have an impact on the induction motor's ability to function in other applications. In this system for monitoring and controlling Induction using IOT, a number of sensors are employed to acquire the motor data in real-time and a relay is used to control the motor. The proposed system will collect and analyse induction motor parameters in real-time using an IOT-based platform.

The High Efficiency of Induction Motor Failure using Predictive Maintenance with Deep Learning Model

The High Efficiency of Induction Motor Failure using Predictive Maintenance with Deep Learning Model

IoT-Based Three-Phase Induction Motor Monitoring System

This paper describes how to use the Internet of Things to control and monitor induction motors. Because it can be utilized from anywhere via Wi-Fi, IoT is more convenient and efficient to control the system. The purpose of this monitoring system is to prevent induction motor failure by implementing preventive measures. Induction motors are used in a variety of applications such as in industry due to their many advantages such as self-starting, high power factor, and rugged design. As a result, using the best available smart protection techniques, it is imperative to detect flaws in the motor at an early stage to improve the efficiency of the motor and ensure safe and reliable operation. The voltage, current and temperature of the induction motor can be monitored remotely with the Blynk IoT application. The monitoring results will determine the error in the motor operating parameters that caused severe damage to the motor. Three PZEM-004T sensors to obtain parameters of each R, S, and T phase of the motor in real-time and relays to control the motor are used in this system to monitor and regulate using IoT. Information from the sensor is received by the microcontroller unit, which processes the sensing data. If an abnormal value is detected, the system automatically generates a control signal to start or stop the motor.

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.

The Commercialization of Medium-Voltage Induction Motor Rotor Diagnostics Equipment: Helping Prevent In-Service Failures of Induction Motors and Processes

The R&D process for commercializing diagnostics equipment for detecting rotor faults in MV induction motors is described in this article. This was the result of a five-year collaborative effort among three independent parties: the equipment end user, equipment developer/supplier, and a university research group, where the tasks performed by each party are summarized in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> . The steps taken for successful development are given, along with a performance summary of the end product for which the first units have been in field use since 2017.

A Qualified review of ML and DL algorithms for Bearing Fault Diagnosis

Moving machinery is the backbone of socio-economic development. The use of machines helps in increasing the production of everyday used items, and tools, that generate electricity and mechanical energy, and provides easy and fast transportation and help by saving human efforts, energy, and time. The mechanical industry is totally dependent on the bearing and it is considered bread and butter of the system. Bearing failure is about 40% of the total failures of induction motors which is why it is a crucial challenge to predict the failure and helps prevent future downtime events through maintenance schedules with the latest techniques and tools of. This paper presents a review of how DL techniques and algorithms outsmarted ML for bearing fault detection and diagnosis and summarizes the accuracy results generated by most common DL algorithms over classical ML algorithms. Additionally this paper reasons different criteria for which DL algorithms have been proved efficient for building productive model in the field of bearing fault detection. Furthermore, some of the most famous datasets by different universities have been discussed and accuracy results are provided by reviewing algorithms on the CWRU dataset by different researchers and comparison chart is listed in the results section.

Diagnosis of induction motor failures using discrete wavelet transform of an auxiliary winding voltage

Over last years, great attention has been developed to avoid the machines breakdown especially in the squirrel cage induction motor which suffer from different failures. The aim of this paper is to present a new method for the diagnosis of rotor bar breakage and end ring in induction machine fed by an inverter based on discrete wavelet transform applied on the voltage of an auxiliary winding as a new monitoring signature. The expression of the auxiliary winding voltage related to a small coil inserted between two stator phases is presented. The study is focused on the high-level signals of approximation and details coefficients. Thus, the evolution of any frequency of interest in the waveform is given in this paper. The method is validated by simulations of four broken bars and end ring cases under unloaded and loaded machine.

Fault simulation of medium voltage squirrel cage induction motors

The main objective of this paper is to model failures of squirrel cage rotor induction motors, it presents the equations that model the machine variables, and we simulate an induction motor in the Matlab-Simulink software, to know and evaluate the effects of the stator windings when the motor experiences failure to function as a short circuit between spirals that there are many diagnostic methodologies for each class of failure. The most widely used technique is analyzing the frequency spectrum of one or several motor signals. A fault in the motor represents an asymmetry concerning its construction; consequently, harmonic components appear in the electrical signals that are related to the present fault, and the spectral analysis of motor current signatures applying the fast Fourier transform (FFT) to the current since these contain relevant information that is distinguished in a spectrum of frequencies.

Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors

Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time&#x2013;frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time&#x2013;frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43&#x0025;, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model&#x2019;s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise.

Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors

The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.

EVALUATION OF FAULTS IN THE SQUIRREL CAGE THREE-PHASE INDUCTION MOTORS

Induction motors have played a central role in the techno-economic development of modern industries and electric power generation. However, the presence of recurring failures hinders a cost-effective performance and leads to catastrophic damage. Therefore, the present study proposed an assessment to investigate the influence of two types of failures in induction motors, namely failure due to broken bars in the rotor and defects in the connection between the rotor bars and the end ring. Accordingly, a three-phase induction motor was evaluated under different failure conditions that modified the operational torque and rotational speed. The results indicated that both types of failures magnify both the core and copper power losses by up to 13.3 % and 8 %, respectively, compared to the healthy condition. On the other hand, an efficiency reductionbetween 1.94 % to 3.41 % is an indication of failure progression. Finally, the appearance of harmonics 3 and 7, and the intensified magnitude of harmonic 5, represent a clear sign of failure occurrence related to rotor barsand defects in the connection to the end ring. In conclusion, the proposed methodology proved to be an adequate tool to predict failure appearance, which has a direct impact on extending the lifetime of induction motors.

A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm

Today, due to the widespread utilization of Induction Motors (IMs) in different industries, their condition monitoring is of great importance. Concentrating on the failures of IMs, it has been acknowledged that the stator inter-turn faults (SITFs) are the most frequent electrical failures. This paper puts forward an algorithm for SITF detection founded on Kalman Filter (KF). More specifically, the proposed algorithm employs KF to extract motor current signatures (MCS) and motor voltage signatures (MVS). Afterward, a statistical SITF index is used, based on the standard deviation of the extracted signatures. The proposed SITF index is technically robust against non-fault conditions including voltage quality problems and heavy load changes as well as has a significant performance in the presence of high measured noise-impregnated signals due to utilization of KF algorithm-based. Moreover, during source harmonic pollutions, the proposed algorithm has a very robust performance. Also, due to straightforward and low-computational mathematical basis, the proposed method is computationally efficient. The performance of the proposed method is validated with numerous simulation and experimental scenarios. The results indicate the proposed SITF index has robust performance, promising accuracy and good speed of convergence.

Analyzing Vibration as a Useful Domain for Getting Bearing Fault Signals in Induction Motors

Due to widespread usage of induction motor (IM) in various industries, the requirement for its condition monitoring have been considerably raised. It is essential to detect faults that happened in IMs in a short time with high accuracy because they may cause considerable financial losses. Bearing faults contribute to a large percentage of IM failures. In this paper, vibration signal is analyzed for getting reliable indicator for faulty modes of the bearings of the IMs. The proper direction for measuring the vibration signal is analyzed first. This analysis shows that the fault-related vibration frequency components along the Z-axis, i.e. the axis perpendicular to the motor's installing surface, usually have the largest magnitude. Thus, it is recommended to measure the vibration signal in the Z-axis. Then, the bearing fault diagnosis using the vibration signal is investigated in various scenarios. The results confirm that the vibration indicators are not sensitive to environmental parameters like temperature and also load variation of the IM but the severity of the fault has a considerable influence on them.

Simulation of three-phase induction motor different bearing faults in Matlab Simulink environment

The article describes the simulation of bearing failures in a three-phase induction motor in the Matlab Simulink environment. According to statistics most of induction motor faults happens due to bearings destruction or stator winding insulation degradation. Vibration analysis is often used to diagnose the bearings condition. This paper considered the model designing principles of different types of bearing faults of induction motor.

Оценка последствий отказа асинхронных электродвигателей вследствие обрывов стержней короткозамкнутых обмоток роторов

Despite the great attention paid by Russian and foreign authors to the problem of timely fault detection in the squirrel-cage rotor windings of high-voltage induction motors, the papers published by them pay little attention to issues related to substantiating the need for carrying out the relevant studies. However, this is especially important for high-voltage electric motors used in power plant auxiliaries. The aim of the study is to assess the consequences from failure of induction motors used in power plant auxiliaries caused by a break of the rotor winding bars. Assessment of the failure consequences is an important component of the transition to risk-oriented management of induction motors operation. The main components of the failure consequences and the resulting economic damage are identified. It is shown that failures of high-voltage induction motors caused by broken rotor bars can lead to economic damage of several million rubles. It has been found, based on computer simulation results, that during operation of induction motors with broken rotor bars, their magnetic and electrical losses increase significantly. The study results have shown that the economic damage may also be significant during long-term operation of a large number of induction motors with this type of failure. The accomplished assessment of the failure consequences in money terms confirms the relevance and necessity to perform functional monitoring of the state of the rotor windings of high-voltage induction motors used in the system of power plant auxiliaries.

АНАЛИЗ МЕТОДОВ ДИАГНОСТИКИ ОБРЫВА СТЕРЖНЯ РОТОРА АСИНХРОННОГО ДВИГАТЕЛЯ

Relevance Uninterrupted operation of industrial facilities, oil and gas sector, metallurgy, power generation and other industries directly depends on reliable operation of critical mechanisms driven by electromechanical converters, operated as a part of working complexes. Reliability of operation of such mechanisms depends on reliability of all elements that make up the technological process. Inductions motor with squirrel-cage rotor (including high-voltage motors) is one of the most common types of converters of electrical energy into mechanical energy. Its uninterrupted operation directly depends on the reliability of two main elements: stator and rotor. If the causes of induction motor failure due to stator causes (inter-turn, inter-phase and single-phase short circuits) are determined by the provided protections, the damage in the rotor circuit can be implicit and exist for a long time, violating the machine uptime. Also, most of the faults in the inductions motor, leading to an emergency shutdown of technological processes of working complexes have their own history of development. At the moment of occurrence and during the period when the defect does not affect the serviceability of the machine, but its operation becomes critically dangerous, because it is not possible to determine the presence of the defect. So, for example, the breakage of the short-circuited rotor core of a high-voltage induction motor manifests itself at the stage of exit to the air gap at the time of operation, with subsequent damage to the stator winding and the magnetic core. In this case, the existence of this defect took place within a certain period of time, sufficient for its detection by means of diagnostic devices. The article analyzes the currently developed systems for diagnosing the presence of a fault according to various parameters (temperature, noise, vibration, analysis of electrical values consumed) both in the stator and in the rotor. It is determined that the most promising and technically feasible are methods based on the analysis of stator currents. When studying the works in the direction of inductions motor diagnostics it was found that a significant proportion of failures of mechanisms operated in the working complexes due to failure of inductions motor occurs due to broken rotor bar. Aim of research Analyze the existing methods for diagnosing induction motors during operation. Research methods The article used general scientific research methods: the method of analysis of literary sources, the study and generalization of information, comparison, classification. Results The analysis of existing methods of continuous diagnostics of induction motors for internal electrical and mechanical damage is carried out. It has been determined that the most promising from the point of view of economic and technical feasibility are methods based on the analysis of currents consumed by the stator.

Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.