Fault Detection In Induction Motors Research Articles

Non-invasive intelligent monitoring system for fault detection in induction motor based on lead-free-piezoelectric sensor using ANN

This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements were collected then monitored in real time and transmitted via an ESP32 board. A new, flexible, lead-free piezoelectric sensor, developed previously in our laboratory, was used for vibration analysis (VA). An infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network (ANN) algorithm implemented on the microcontroller. Additionally, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of VA, thermal signature analysis and ANN provides a better diagnosis and provides efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning

Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning

Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach

The most recent trend in the electric motor condition monitoring area relies on combining the information obtained from different machine quantities to reach a more reliable conclusion about the motor’s health. This knowledge is of critical importance today, especially in industrial applications, in which unexpected outages can lead to severe repercussions. This article presents a new intelligent sensor that combines, in a single unit, the information obtained from the analysis of stray fluxes (both axial and radial) and currents by means of a feedforward neural network (FFNN) for classification purposes. Unlike other solutions, the sensor is based on the application of advanced signal processing tools that are adapted to the online analysis of these quantities under transient conditions from a single processing unit (a smart sensor). The combination of these new tools with the classical steady-state analysis of such quantities enables one to obtain a more reliable conclusion on the motor health. The experiments included in the article demonstrate the reliability provided by the sensor, which is being prepared to incorporate a third input based on infrared data.

Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).

Induction motor fault detection by a new sliding mode observer based on backstepping

Induction motor fault detection by a new sliding mode observer based on backstepping

Inter-turn short-circuit fault diagnosis using robust adaptive parameter estimation

This paper presents a robust adaptive parameter estimation procedure, applied to stator inter-turn short-circuit (ITSC) fault detection in induction motors (IM). Using a fault state space model, it is proposed the application of an adaptive parameter estimation methodology, used to identify a parameter related to the failure severity. A linear parameter varying (LPV) robust H∞ filtering technique, obtained by linear matrix inequalities (LMI), is applied to estimate the unavailable states, being the proposed method robust to external disturbances, and also adequate to online applications. The proposed framework is validated through simulations and experimental tests — conducted in a modified IM that allows the emulation of ITSC faults. Tests are performed under several operational conditions, including different levels of fault severity, load torque and voltage unbalance, demonstrating the validity of the ITSC fault detection approach.

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

Rotor winding inter-turn short-circuit fault detection in wound rotor induction motors using Wing Technique

Rotor winding inter-turn short-circuit fault detection in wound rotor induction motors using Wing Technique

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and 1D-CNN. Initially, we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults (SWF) using Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT) to detect and identify the healthy and unhealthy conditions with phase to ground, single phasing and in multiple fault conditions using Motor Current Signature Analysis. The faults impact on stator current is presented in the time and frequency domain (i.e., power spectrum). The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions. This is further investigated with three deep learning models (i.e., MLP, LSTM, and 1D-CNN) for checking the fault detection and identification (i.e., classification) improvement in a three-phase induction motor. By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB, we have collected current signature data in the time domain, labeled them accordingly and created the 50 thousand samples dataset for DL models. All the DL models are trained and validated with a suitable number of architecture layers. By simulation, the multiclass confusion matrix, precision, recall, and F1-score are obtained in several conditions. The result shows that the stator current signature of the motor can be used to detect individual and multiple faults. Moreover, deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature. In deep learning (DL) models, the LSTM has shown better accuracy among all other three models. These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Broken Rotor Bar and Rotor Eccentricity Fault Detection in Induction Motors Using a Combination of Discrete Wavelet Transform and TeagerKaiser Energy Operator

In this paper, a hybrid approach is proposed to detect the broken rotor bar and rotor mixed eccentricity faults of three-phase squirrel cage induction motors based on one phase of the stator current signal. The hybrid method is based on a combination of discrete wavelet transform (DWT), Teager–Kaiser energy operator (TKEO) and power spectral density (PSD). At the first stage, DWT is applied on the stator current signal; then TKEO is applied on some of the DWT detail coefficients and finally PSD is used to extract the required feature to detect the aforementioned faults. The proposed method is tested on an experimental test-rig having a 1.5 kW induction motor under various load conditions and three different power supplies: 1) directly supplied by power grid; 2) supplied by a drive system based on scalar control technique; and 3) supplied by a drive system based on direct torque control (DTC) technique. The results show the efficacy of the proposed method in accurate detection of the broken rotor bar and rotor mixed eccentricity faults.

Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

Centrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors

Deep learning can play a pivotal role in early fault detection in squirrel cage induction motors (SCIMs) and achieving Industry 4.0. SCIM finds application in industries like mining, textile, manufacturing, and many more. Early fault detection in SCIM can significantly reduce downtime and optimize productivity. This paper proposes a novel fault detection technique for bearing faults and broken rotor bar detection in SCIM using the dilated convolutional neural network-based model. A simple 1-D signal to image conversion technique is also proposed for transforming the 1-D vibration signal acquired from multiple accelerometers to images. The proposed method provides an end-to-end learning solution for fault detection. The propounded approach has accomplished an average accuracy of more than 99.50%. A comparison has also been made between different convolutional neural network (CNN) models and conventional machine learning models to show the proposed method’s efficiency. The complete experimental work has been carried out on a 5 kW, 3-phase, 415 V, 50 Hz SCIM. The dilated CNN model development has been done using python software, and the packages used are Keras and TensorFlow.

Bearing Fault Detection in Induction Motors with Hilbert-Huang Transformation

Bearing Fault Detection in Induction Motors with Hilbert-Huang Transformation

Induction Machines Fault Detection: An Overview

Today, instrumentation plays an essential role within Industry 4.0, integrating elements and technologies at the forefront of the industrial world such as the Internet of Things (IoT), Cloud Computing, Business Intelligence, or Cybersecurity, among others. Thinking about the Industrial Internet of Things (IIoT), it is looking for the highest number of systems to be connected to the Internet and provide reports on their work, performance, condition, temperature, levels, etc. to improve production processes, save energy, reduce costs, among others. Regarding the maintenance and condition of the machines, currently in the literature, there are several works about the detection of faults or possible failures in the short term in the systems, that intend to plan preventive or corrective maintenance. All of this implies a high development of signal processing and decision-making algorithms. In this paper, a description related to bearing and broken rotor fault detection in induction motors for con-dition-based maintenance is presented.

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.

Corrigendum: Dynamic air gap asymmetry fault detection in single‐sided linear induction motors

IET Electric Power ApplicationsVolume 16, Issue 1 p. 137-137 CORRIGENDUMOpen Access Corrigendum: Dynamic air gap asymmetry fault detection in single-sided linear induction motors This article corrects the following: Dynamic air gap asymmetry fault detection in single-sided linear induction motors Jawad Faiz, Mehrage Ghods, Armin Tajdyni, Volume 14Issue 4IET Electric Power Applications pages: 605-613 First Published online: February 25, 2020 First published: 26 September 2021 https://doi.org/10.1049/elp2.12138AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In [1], the following corrections should be noted. The spelling in the author name Arman Tajdiny is updated and the ORCID is included. Arman Tajdiny https://orcid.org/0000-0001-8333-6391 REFERENCE 1Faiz, J., Mehrage, G., Tajdiny, A.: Dynamic air gap asymmetry fault detection in single-sided linear induction motors. IET Electr. Power Appl. 14(4), 605– 613 (2020). https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-epa.2019.0323Wiley Online LibraryWeb of Science®Google Scholar Volume16, Issue1January 2022Pages 137-137 ReferencesRelatedInformation

Fault Detection of Induction Motor Based on ALO Optimized TKSVDD

Fault Detection of Induction Motor Based on ALO Optimized TKSVDD

Training virtual reality-based system for detection and simulation of motors failures

The need to have human resources trained efficiently is imperative. However, most industries do not give it the necessary importance. Similarly, staying technologically up-to-date allows the industrial sector to optimize its resources and satisfy the needs of its customers. This research presents the development of a virtual reality (VR) system for learning fault detection and correction in induction motors. It has been compared to a conventional methodology in terms of time and acquired knowledge. A 58% reduction was observed in the first parameter, while for the second, a p-value of 0.0000005 was obtained, thus demonstrating that this system is more efficient than conventional training in terms of learning. Finally, its usability was evaluated with the VRUSE method, with averages above 3.8 and standard deviations below 0.70.

Broken rotor bars fault detection in induction motors based on current envelope and neural network

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions