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Energies
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Novel Approaches to Electrical Machine Fault Diagnosis
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Novel Approaches to Electrical Machine Fault Diagnosis

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 30143

Special Issue Editors

Prof. Dr. Jose Alfonso Antonino-Daviu

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Guest Editor
Department of Electrical Engineering, Universitat de València, 46022 Valencia, Spain
Interests: Electrical machines; fault diagnosis; reliability; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Toomas Vaimann

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical machines and diagnostics of electrical machines
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anton Rassõlkin

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical drives; mechatronics; electric vehicles; electrical machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical machines play an important role in the industry. They are performing crucial and highly responsible tasks in production, propulsion, power generation and numerous other industrial processes. Hence, the condition monitoring, prognosis and diagnosis of the faults threatening the smooth operation of the machines is of utmost importance. It leads to minimizing downtime, avoiding financial loss and the threat to human life as well as preserving the environment.

There are many traditional diagnostic techniques in use and under investigation today. Yet, with the world and technology moving rapidly forward, new horizons are opening also in the diagnostic field. The possibility of using Internet of Things, powerful Artificial Intelligence tools, virtual sensors, cloud computing and all the different technological solutions classified as Industry 4.0 options, more advanced, complex, but at the same time more precise diagnostic techniques can be used. There is a good possibility for the novel diagnosis approaches for electrical machines to be introduced, and at the same time, old and at some point not very promising techniques can find their new life due to advanced IT solutions, providing more available computational resources and faster calculation and simulation times.

As the usage of electrical machines in the world is rising rapidly in all the sectors of life, novel approaches to electrical machine fault diagnosis can show the way for a more efficient use and prolonged lifetime for the machines and lead the introduction of different intelligent technologies in engineering.

Prof. Dr. Jose A Antonino-Daviu
Dr. Toomas Vaimann
Prof. Dr. Anton Rassõlkin
Guest Editors

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Keywords

  • Fault diagnosis
  • Electric machines
  • Condition monitoring
  • Operation monitoring
  • Prognosis of faults
  • Diagnostic techniques
  • Fault models
  • Diagnostic models
  • Steady state operation
  • Transient operation
  • Predictive maintenance
  • Signal processing
  • Fault tolerant control

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Related Special Issue

Published Papers (9 papers)

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Editorial

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4 pages, 193 KiB  
Editorial
Novel Approaches to Electrical Machine Fault Diagnosis
by Toomas Vaimann, Jose Alfonso Antonino-Daviu and Anton Rassõlkin
Energies 2023, 16(15), 5641; https://doi.org/10.3390/en16155641 - 27 Jul 2023
Viewed by 1193
Abstract
The increasing demand for intelligent machines, coupled with the drive for the more efficient utilization of these machines in various industries, and the emergence of Industry 4 [...] Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)

Research

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12 pages, 4250 KiB  
Article
Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square
by Bon-Gwan Gu
Energies 2022, 15(22), 8735; https://doi.org/10.3390/en15228735 - 20 Nov 2022
Cited by 5 | Viewed by 1699
Abstract
This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series [...] Read more.
This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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23 pages, 1440 KiB  
Article
A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study
by Mikko Tahkola, Áron Szücs, Jari Halme, Akhtar Zeb and Janne Keränen
Energies 2022, 15(9), 3317; https://doi.org/10.3390/en15093317 - 2 May 2022
Cited by 7 | Viewed by 2762
Abstract
Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and [...] Read more.
Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers’ generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed signals. While both approaches resulted in similar accuracy with simulated current and measured vibration acceleration data, the feature-based models were faster to develop and run. With measured vibration acceleration data, better accuracy was obtained with the raw fast Fourier-transformed signals. The results demonstrate that an accurate and fast broken rotor bar detection model can be developed with the presented approach. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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15 pages, 3606 KiB  
Article
Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method
by Seong-Hwan Im and Bon-Gwan Gu
Energies 2022, 15(3), 977; https://doi.org/10.3390/en15030977 - 28 Jan 2022
Cited by 19 | Viewed by 2709
Abstract
This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a [...] Read more.
This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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16 pages, 18409 KiB  
Article
Study of Induction Motor Inter-Turn Fault Part I: Development of Fault Models with Distorted Flux Representation
by Seong-Hwan Im and Bon-Gwan Gu
Energies 2022, 15(3), 894; https://doi.org/10.3390/en15030894 - 26 Jan 2022
Cited by 10 | Viewed by 3569
Abstract
An inter-turn fault (ITF) is one of the most frequent induction motor faults; thus, many previous works have studied its model and diagnosis. However, previous works, simplifying the specific distorted flux distribution by the ITF, presented induction motor fault models and focused on [...] Read more.
An inter-turn fault (ITF) is one of the most frequent induction motor faults; thus, many previous works have studied its model and diagnosis. However, previous works, simplifying the specific distorted flux distribution by the ITF, presented induction motor fault models and focused on the fault signal analysis for diagnoses. Consequently, these results are only adequate for the pretested motor and sensitive to fault signal distortion. This paper presents an induction motor ITF model in the stationary DQ frame, for a model-based diagnosis. Furthermore, to describe the distorted flux distribution along the air gap by the ITF, the rotor flux linkages are described in the independent DQ frame of every pole, and the mutual flux linkages among the rotor, stator, and ITF windings are specifically modeled. Hence, the proposed full model has many current states and mutual inductances to describe the high pole number motor. A simplified model is also proposed for easier usage in the diagnosis, with light ITF to overcome this complexity. Finally, simulation and experiments are performed to verify the presented induction motor ITF fault models. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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13 pages, 63687 KiB  
Article
Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps
by Vincent Becker, Thilo Schwamm, Sven Urschel and Jose Alfonso Antonino-Daviu
Energies 2021, 14(15), 4514; https://doi.org/10.3390/en14154514 - 26 Jul 2021
Cited by 6 | Viewed by 2726
Abstract
The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only [...] Read more.
The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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14 pages, 4580 KiB  
Article
Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis
by Damian Grzechca, Paweł Rybka and Roman Pawełczyk
Energies 2021, 14(11), 3206; https://doi.org/10.3390/en14113206 - 31 May 2021
Cited by 5 | Viewed by 3221
Abstract
Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level [...] Read more.
Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level crossing systems, it is recommended that fault and anomaly detection algorithms be implemented. Both algorithms are important in terms of safety (information on whether a barrier boom has been lifted/lowered as required) and predictive maintenance (information about the condition of the mechanical components). Here, the authors propose fault models for barrier machine fault and anomaly detection procedures based on current waveform observation. Several algorithms were applied and then assessed such as self-organising maps (SOM), autoencoder artificial neural network, local outlier factor (LOF) and isolation forest. The advantage of the proposed solution is there is no change of hardware, which is already homologated, and the use of the existing sensors (in a current measurement module). The methods under evaluation demonstrated acceptable rates of detection accuracy of the simulated faults, thereby enabling a practical application at the test stage. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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16 pages, 5030 KiB  
Article
Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study
by Noman Shabbir, Lauri Kütt, Bilal Asad, Muhammad Jawad, Muhammad Naveed Iqbal and Kamran Daniel
Energies 2021, 14(7), 2001; https://doi.org/10.3390/en14072001 - 5 Apr 2021
Cited by 8 | Viewed by 4022
Abstract
In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has [...] Read more.
In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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Review

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20 pages, 5118 KiB  
Review
Methods of Condition Monitoring and Fault Detection for Electrical Machines
by Karolina Kudelina, Bilal Asad, Toomas Vaimann, Anton Rassõlkin, Ants Kallaste and Huynh Van Khang
Energies 2021, 14(22), 7459; https://doi.org/10.3390/en14227459 - 9 Nov 2021
Cited by 33 | Viewed by 6401
Abstract
Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and [...] Read more.
Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis)
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