Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Failure Diagnosis and Prognosis of Induction Machines
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Failure Diagnosis and Prognosis of Induction Machines

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 24388

Special Issue Editor

Prof. Guy Clerc

E-Mail Website
Guest Editor
Laboratoire Ampère UMR5005, Univ. Lyon, Université Claude Bernard Lyon 1, Lyon, France
Interests: condition monitoring; digital twin; electrical drives; failure diagnosis; failure prognosis; control; pattern recognition; induction machines; synchronous machines; power electronic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Induction motors present numerous advantages due to their robustness and their power–weight ratio. However, they are subject to several electrical and mechanical faults. Many methods have been developed to diagnose such failures and prevent unwanted stop. The can be based on MCSA, vibrations, noise, electrical or magnetic field, etc. Different techniques have been developed, such as the model-based approach and the data-driven approach. The data-driven method deals with signal processing, statistical tools, data mining, and artificial intelligence.

Recent trends include an improvement of diagnostic reliability and accuracy, and new prognostic techniques have been developed for assessing the remaining useful life of these electrical drives and, thus, optimizing maintenance scheduling.

This Special Issue deals with the most recent research on incipient failure diagnosis of induction machines, the prognosis of their remaining useful life in steady state or in variable speed, and the operation in degraded mode.

Prof. Guy Clerc
Guest Editor

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 machine
  • failure diagnosis
  • failure prognosis
  • short circuit
  • open bars
  • eccentricity
  • artificial intelligence
  • modeling
  • signal processing
  • degraded mode

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

2 pages, 158 KiB  
Editorial
Failure Diagnosis and Prognosis of Induction Machines
by Guy Clerc
Energies 2022, 15(4), 1483; https://doi.org/10.3390/en15041483 - 17 Feb 2022
Viewed by 1099
Abstract
Induction motors have numerous advantages due to their robustness and their power–weight ratio [...] Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)

Research

Jump to: Editorial, Review

18 pages, 2882 KiB  
Article
Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters
by Vicente Biot-Monterde, Ángela Navarro-Navarro, Jose A. Antonino-Daviu and Hubert Razik
Energies 2021, 14(18), 5757; https://doi.org/10.3390/en14185757 - 13 Sep 2021
Cited by 11 | Viewed by 1883
Abstract
The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) [...] Read more.
The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Figure 1

19 pages, 10258 KiB  
Article
A Novel Deep Clustering Method and Indicator for Time Series Soft Partitioning
by Alexandre Eid, Guy Clerc, Badr Mansouri and Stella Roux
Energies 2021, 14(17), 5530; https://doi.org/10.3390/en14175530 - 4 Sep 2021
Cited by 4 | Viewed by 2203
Abstract
The aerospace industry develops prognosis and health management algorithms to ensure better safety on board, particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity [...] Read more.
The aerospace industry develops prognosis and health management algorithms to ensure better safety on board, particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, a clustering algorithm using a deep neural network core is developed. Time series are encoded into pictures to be fed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihoods, without prior knowledge. It was then compared to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As the state-of-the-art indexes were not producing relevant results, a new indicator was built to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Figure 1

21 pages, 2879 KiB  
Article
A New Analytical Method for Modeling the Effect of Assembly Errors on a Motor-Gearbox System
by Bilal El Yousfi, Abdenour Soualhi, Kamal Medjaher and François Guillet
Energies 2021, 14(16), 4993; https://doi.org/10.3390/en14164993 - 14 Aug 2021
Cited by 8 | Viewed by 2061
Abstract
The well-known gear tooth defects such as root cracks and flank spalls have been widely investigated in previous studies to model their effects on the time varying mesh stiffness (TVMS) and consequently the dynamic response of motor-gearbox systems. Nevertheless, the effect of assembly [...] Read more.
The well-known gear tooth defects such as root cracks and flank spalls have been widely investigated in previous studies to model their effects on the time varying mesh stiffness (TVMS) and consequently the dynamic response of motor-gearbox systems. Nevertheless, the effect of assembly errors such as the center distance and the eccentricity has been less considered in past works. Determining the signature of these errors on the system response can help for their early detection and diagnostic to avoid overloading and failure of gears. An original geometric-based method combined with the potential energy method is proposed in this paper to accurately model the effect of these assembly errors on the TVMS of mating spur gear pairs. This is achieved by updating the line of action equation (LOA) at each meshing step using the actual coordinates of gear centers and employing a contact detection algorithm (CDA) to determine the actual contact points coordinates. An electrical model of a three-phase induction machine was then coupled with a dynamic model of a one-stage spur gear system to simulate the effect of assembly errors on the electromechanical response of the motor-gearbox system. The simulation results showed that the center distance error induces a reduction in the TVMS magnitude and the contact ratio, whereas the eccentricity error causes a double modulation of the TVMS magnitude and frequency. In addition, the results showed that assembly errors can be detected and diagnosed by analyzing the system vibration and the motor phase-current. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Figure 1

12 pages, 5336 KiB  
Article
Induction Motor PI Observer with Reduced-Order Integrating Unit
by Tadeusz Białoń, Roman Niestrój, Jarosław Michalak and Marian Pasko
Energies 2021, 14(16), 4906; https://doi.org/10.3390/en14164906 - 11 Aug 2021
Cited by 9 | Viewed by 1776
Abstract
This article presents an innovative induction motor state observer designed to reconstruct magnetic fluxes and the angular speed of an induction motor for speed sensorless control system applications such as field-oriented control (FOC). This observer is an intermediate solution between the proportional observer [...] Read more.
This article presents an innovative induction motor state observer designed to reconstruct magnetic fluxes and the angular speed of an induction motor for speed sensorless control system applications such as field-oriented control (FOC). This observer is an intermediate solution between the proportional observer and the classical proportional-integral (PI) observer with respect to which the order of the integrating unit is reduced. Additional modifications of the observer’s structure have been implemented to ensure stability and to improve its functional properties. As a result, two versions of the observer structure were produced and experimentally tested using a sensorless FOC control system. Both structures resulted in correct control system operation for a wide range of angular speeds, including low speed ranges. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Figure 1

18 pages, 6990 KiB  
Article
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction
by Tarek Berghout, Mohamed Benbouzid and Leïla-Hayet Mouss
Energies 2021, 14(8), 2163; https://doi.org/10.3390/en14082163 - 13 Apr 2021
Cited by 22 | Viewed by 2899
Abstract
Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized [...] Read more.
Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research

31 pages, 6895 KiB  
Review
Variable Speed Diesel Generators: Performance and Characteristic Comparison
by Mohammadjavad Mobarra, Miloud Rezkallah and Adrian Ilinca
Energies 2022, 15(2), 592; https://doi.org/10.3390/en15020592 - 14 Jan 2022
Cited by 20 | Viewed by 11237
Abstract
Diesel generators (DGs) are set to work as a backup during power outages or support the load in remote areas not connected to the national grid. These DGs are working at a constant speed to produce reliable AC power, while electrical energy demand [...] Read more.
Diesel generators (DGs) are set to work as a backup during power outages or support the load in remote areas not connected to the national grid. These DGs are working at a constant speed to produce reliable AC power, while electrical energy demand fluctuates according to instantaneous needs. High electric loads occur only for a few hours a day in remote areas, resulting in oversizing DGs. During a low load operation, DGs face poor fuel efficiency and condensation of fuel residues on the walls of engine cylinders that increase friction and premature wear. One solution to increase combustion efficiency at low electric loads is to reduce diesel engine (DE) speed to its ideal regime according to the mechanical torque required by the electrical generator. Therefore, Variable Speed Diesel Generators (VSDGs) allow the operation of the diesel engine at an optimal speed according to the electrical load but require additional electrical equipment and control to maintain the power output to electrical standards. Variable speed technology has shown a significant reduction of up to 40% fuel consumption, resulting in low GHG emissions and operating costs compared to a conventional diesel generator. This technology also eliminates engine idle time during a low load regime to have a longer engine lifetime. The main objective of this survey paper is to present the state of the art of the VSDG technologies and compare their performance in terms of fuel savings, increased engine lifetime, and reduced greenhouse gases (GHG) emissions. Various concepts and the latest VSDG technologies have been evaluated in this paper based on their performance appraisal and degree of innovation. Full article
(This article belongs to the Special Issue Failure Diagnosis and Prognosis of Induction Machines)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop