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Advances in Fault Detection, Diagnosis and Prognosis in Industrial Motors

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 8454

Special Issue Editor

Electrical Machines Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, University Campus, GR-671 00 Xanthi, Greece
Interests: electrical machines design; analysis, modeling, optimization and fault diagnosis of electrical machines; controller design; artificial intelligence methods application to electrical machines
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Special Issue Information

Dear Colleagues,

Electric motors are widely used in numerous industrial applications. They operate continuously and for long-term periods both at nominal and overload conditions. As such, it is evident that the occurrence of faults is quite frequent. A possible motor failure can lead to temporary shutdown or interruption of the production process, which results in a loss of services and/or supplies. Additionally, the Industry 4.0 framework strongly supports smart manufacturing, complying with sustainability of all the involved systems and operations. Thus, it is of great importance to proceed to fast and reliable assessment of the health status of industrial drives. The development of effective mechanisms for electric motor fault detection has therefore attracted widespread attention from both academical and industrial fields. The goal of this issue is to bring researchers together to share their research findings and present attractive perspectives in the fields of fault detection, diagnosis, and prognosis in industrial motors. Prospective authors are invited to submit original and high-quality papers. Topics of interest include but are not limited to the following areas:

  • Advanced diagnostic approaches for mechanical (e.g., bearings, gearbox, shaft bending, static and dynamic eccentricity), electrical (short circuits, winding interruption, asymmetry in supply voltage, voltage fluctuation, insulation failure, etc.), and electromechanical (rotor bars breaking, rotor end-ring detachment, etc.) faults;
  • Diagnosis of multiple simultaneous faults;
  • Early detection of incipient faults and fault isolation;
  • Multisensor data fusion;
  • Line- and inverter-fed electrical machines;
  • Signal analysis and faults diagnosis during motor operation under harsh conditions;
  • Non-invasive techniques;
  • Predictive maintenance and real-time condition monitoring systems;
  • Discrimination between faulty conditions and healthy conditions under the presence of load oscillations or speed variation;
  • Modern signal processing techniques toward information quality improvement;
  • Enhanced pattern recognition algorithms;
  • Advanced fault detection and diagnosis methods based on artificial intelligence (e.g. supervised/unsupervised machine learning).

Prof. Dr. Yannis L. Karnavas
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

  • Electrical machines
  • Industrial motors
  • Faults detection
  • Diagnosis
  • Artificial intelligence
  • Predictive maintenance
  • Industry 4.0

Published Papers (4 papers)

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Research

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17 pages, 5752 KiB  
Article
Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest
by Jie Ma, Yingxue Li, Liying Wang, Jisheng Hu, Hua Li, Jiyou Fei, Lin Li and Geng Zhao
Energies 2023, 16(13), 4949; https://doi.org/10.3390/en16134949 - 26 Jun 2023
Cited by 1 | Viewed by 642
Abstract
The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction [...] Read more.
The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%. Full article
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20 pages, 5285 KiB  
Article
Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals
by Arturo Yosimar Jaen-Cuellar, Miguel Trejo-Hernández, Roque Alfredo Osornio-Rios and Jose Alfonso Antonino-Daviu
Energies 2023, 16(2), 948; https://doi.org/10.3390/en16020948 - 14 Jan 2023
Cited by 5 | Viewed by 1277
Abstract
Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because [...] Read more.
Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach. Full article
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20 pages, 7120 KiB  
Article
A Method of Comparative Evaluation of the Drive Units of Two Bucket Elevators—A Case Study
by Piotr Sokolski and Justyna Sokolska
Energies 2021, 14(21), 7439; https://doi.org/10.3390/en14217439 - 08 Nov 2021
Cited by 1 | Viewed by 2668
Abstract
Bucket elevators are applied in many industries for bulk material handling. One of the main requirements for these devices is their high operational reliability. This applies in particular to power units that must operate continuously without failure. This article presents a comparative assessment [...] Read more.
Bucket elevators are applied in many industries for bulk material handling. One of the main requirements for these devices is their high operational reliability. This applies in particular to power units that must operate continuously without failure. This article presents a comparative assessment of the drive units of two bucket elevators. The vibration intensity of their bearing units was used as the basis for the comparison. The evaluation was carried out using three independent methods based on the vibration velocity analysis: in the time domain, in the frequency domain and using the probabilistic approach. Full article
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Review

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21 pages, 2691 KiB  
Review
A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines
by Zijian Liu, Pinjia Zhang, Shan He and Jin Huang
Energies 2021, 14(14), 4296; https://doi.org/10.3390/en14144296 - 16 Jul 2021
Cited by 14 | Viewed by 2753
Abstract
Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines [...] Read more.
Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research. Full article
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