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Early Detection of Faults in Induction Motors

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 23598

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Prof. Dr. Daniel Morinigo-Sotelo

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Institute of Advanced Manufacturing Technologies, Research Group ADIRE-HspDigital, Universidad de Valladolid, 47011 Valladolid, Spain
Interests: induction motors; fault detection and diagnosis; condition monitoring; predictive maintenance; signal processing; smart grids; power quality
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rene Romero-Troncoso

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HSPdigital–CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, C. P., San Juan del Rio 76807, Mexico
Interests: hardware signal processing and mechatronics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Joan Pons-Llinares

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Guest Editor

Instituto de Ingeniería Energética, Universitat Politècnica de València, 46022 València, Spain
Interests: time-frequency transforms; condition monitoring; efficiency estimation of electrical machines

Special Issue Information

Dear Colleagues,

Induction motors are a crucial element in many industry fields, in transportation, and in the service and utility sector. Although they are considered robust machines, they are also subject to failures that, if not detected in time, can lead to catastrophic breakdowns. This can lead to increased costs for companies, unplanned production stops, destruction of facilities, or service interruptions.

For these reasons, the interest of industry and academia in developing early detection systems to prevent these incipient failures from evolving into catastrophic ones has been renovated and boosted. Techniques for early fault detection would allow the implementation of predictive maintenance systems, which are an essential element of Industry 4.0.

Condition monitoring of induction motors has been traditionally based on the analysis of the stator current or motor vibrations. However, currently, new solutions based on the analysis of other signals, such as stray flux, sound, and speed, have been proposed.

This Special Issue has therefore a broad scope, though it is focused on the induction motor. Submitted works may deal with the early detection of any type of fault in motors working in stationary or transient regimes and line- or inverter-fed. Innovative papers related to advanced signal processing techniques, machine learning, artificial intelligence, big data, and sensors will be welcome.

Prof. Dr. Daniel Morinigo-Sotelo
Prof. Dr. Rene Romero-Troncoso
Prof. Dr. Joan Pons-Llinares
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Induction motors
Condition monitoring
Predictive maintenance
Fault detection and diagnosis
Early detection and diagnosis
Detection in transient regimes
Detection in steady-state regimes
Line- and inverter-fed motors
Signal processing for monitoring and diagnosis
Smart sensors for monitoring electric motors
Monitoring and diagnosis of electric motors in Industry 4.0

Related Special Issue

Early Detection of Faults in Induction Motors II in Energies

Published Papers (10 papers)

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Research

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16 pages, 6006 KiB

Open AccessArticle

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

by Jie Ma, Xiaodong Liu, Jisheng Hu, Jiyou Fei, Geng Zhao and Zhonghuan Zhu

Energies 2023, 16(15), 5612; https://doi.org/10.3390/en16155612 - 26 Jul 2023

Cited by 1 | Viewed by 718

Abstract

EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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20 pages, 859 KiB

Open AccessArticle

Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework

by Moritz Benninger, Marcus Liebschner and Christian Kreischer

Energies 2023, 16(8), 3429; https://doi.org/10.3390/en16083429 - 13 Apr 2023

Cited by 3 | Viewed by 1455

Abstract

This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the simulation-trained neural network to a real environment. Neglecting bearing faults, the fault cases from the validation data are classified with an accuracy of 94.81%. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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17 pages, 4188 KiB

Open AccessArticle

A Generalized Fault Tolerant Control Based on Back EMF Feedforward Compensation: Derivation and Application on Induction Motors Drives

by Mahdi Tousizadeh, Amirmehdi Yazdani, Hang Seng Che, Hai Wang, Amin Mahmoudi and Nasrudin Abd Rahim

Energies 2023, 16(1), 51; https://doi.org/10.3390/en16010051 - 21 Dec 2022

Cited by 1 | Viewed by 1097

Abstract

In this paper, a fault-tolerant three-phase induction drive based on field-oriented control is studied, and an analytical approach is proposed to elucidate the limitations of FOC in flux-torque regulation from the controller perspective. With an open-phase fault, the disturbance terms appear in the controller reference frame and degrade the controller performance when operating in a d-q plane with DC quantities. In addition, the hardware reconfiguration, which is essential to operate faulted three-phase drives, causes substantial change in the way the control parameters v_d, v_q are reflected onto the machine terminals. An accurate understanding of the feedforward term, by considering the open-phase fault and the hardware modifications, is provided to re-enable the FOC in presence of an open-phase fault. Furthermore, the concept of feedforward term derivation is generically extended to cover multiphase induction drives encountering an open-phase fault whereby no hardware reconfiguration is intended. The proposed method is explained based on a symmetrical six-phase induction and can be extended to drives with a higher number of phases. The effectiveness of the proposed derivation method, which is required to form a feedforward fault-tolerant controller, is verified and compared through the simulation and experiment, ensuring smooth operation in postfault mode. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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24 pages, 5348 KiB

Open AccessArticle

Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors

by Luis Alonso Trujillo Guajardo, Miguel Angel Platas Garza, Johnny Rodríguez Maldonado, Mario Alberto González Vázquez, Luis Humberto Rodríguez Alfaro and Fernando Salinas Salinas

Energies 2022, 15(10), 3513; https://doi.org/10.3390/en15103513 - 11 May 2022

Cited by 8 | Viewed by 1835

Abstract

This article presents an evaluation of Prony method and its implementation considerations for motor current signal analysis diagnostics in rotor cage induction motors. The broken rotor bar fault signature in current signals is evaluated using Prony method, where its advantages in comparison with fast Fourier transform are presented. The broken rotor bar fault signature could occur during the life cycle operation of induction motors, so that is why an effective early detection estimation technique of this fault could prevent an insulation failure or heavy damage, leaving the motor out of service. First, an overview of cage winding defects in rotor cage induction motors is presented. Next, Prony method and its considerations for the implementation in current signature analysis are described. Then, the performance of Prony method using numerical simulations is evaluated. Lastly, an assessment of Prony method as a tool for current signal analysis diagnostics is performed using a laboratory test system where real signals of an induction motor with broken rotor bar operated with/without a variable frequency drive are analyzed. The summary results of the estimation (amplitudes and frequencies) are presented in the results and discussion section. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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17 pages, 5882 KiB

Open AccessArticle

A Negative Sequence Current Phasor Compensation Technique for the Accurate Detection of Stator Shorted Turn Faults in Induction Motors

by Syaiful Bakhri and Nesimi Ertugrul

Energies 2022, 15(9), 3100; https://doi.org/10.3390/en15093100 - 24 Apr 2022

Cited by 7 | Viewed by 2001

Abstract

Stator faults are the most critical faults in induction motors as they develop quickly hence requiring fast online diagnostic methods. A number of online condition monitoring techniques are proposed in the literature to respond to such faults, including the signature analysis of the stator current, vibration monitoring, flux leakage monitoring, negative sequence components of voltage and current and sequence components monitoring based on the identification of asymmetrical behavior in a machine. Negative sequence components of voltage and current and sequence components monitoring are commonly considered for the identification of asymmetrical behavior of induction motors. Negative sequence current analysis is a sensitive technique for the detection of shorted turns as it directly measures the asymmetry produced by the fault. However, the technique is sensitive to other asymmetrical faults and disturbances, which can also produce negative sequence currents. These disturbances include sensor errors, inherent asymmetry and voltage unbalance. This paper provides a comprehensive investigation of the disturbances using a motor model along with experimental measurements under varying load conditions. Then, a new phasor compensation technique is explained to eliminate such disturbances effectively. This technique enables the accurate detection of even relatively small shorted turn faults, even at an early stage. The technique is tested experimentally, and a set of practical results are given to validate the methods developed. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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21 pages, 6218 KiB

Open AccessArticle

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

by David Gonzalez-Jimenez, Jon del-Olmo, Javier Poza, Fernando Garramiola and Izaskun Sarasola

Energies 2021, 14(16), 4886; https://doi.org/10.3390/en14164886 - 10 Aug 2021

Cited by 12 | Viewed by 3452

Abstract

Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help maintenance assistants on identifying faults in the power connections of induction machines. Specifically, a strategy for failure modes such as high resistance connections, single phasing faults and opposite wiring connections has been designed. In this case, as field data under the aforementioned faulty events are scarce in industry, a simulation-driven ML-based fault diagnosis strategy has been implemented. Hence, training data for the ML algorithm has been generated via Software-in-the-Loop simulations, to train the machine learning models. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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14 pages, 1660 KiB

Open AccessArticle

Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms

by Andre S. Barcelos and Antonio J. Marques Cardoso

Energies 2021, 14(9), 2509; https://doi.org/10.3390/en14092509 - 27 Apr 2021

Cited by 18 | Viewed by 3023

Abstract

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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16 pages, 7161 KiB

Open AccessArticle

Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

by Tomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez and Rene de J. Romero-Troncoso

Energies 2021, 14(5), 1469; https://doi.org/10.3390/en14051469 - 8 Mar 2021

Cited by 21 | Viewed by 2789

Abstract

The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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Review

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21 pages, 6443 KiB

Open AccessReview

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

by Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan, Jose de Jesus Rangel-Magdaleno and Juan Manuel Ramirez-Cortes

Energies 2023, 16(12), 4780; https://doi.org/10.3390/en16124780 - 18 Jun 2023

Cited by 7 | Viewed by 1384

Abstract

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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18 pages, 384 KiB

Open AccessReview

Early Detection of Faults in Induction Motors—A Review

by Tomas Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero and Rene Romero-Troncoso

Energies 2022, 15(21), 7855; https://doi.org/10.3390/en15217855 - 23 Oct 2022

Cited by 18 | Viewed by 3334

Abstract

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented. Full article

(This article belongs to the Special Issue Early Detection of Faults in Induction Motors)

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