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Fault Diagnosis of Electrical Machines and Drives

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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 December 2021) | Viewed by 26533

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Prof. Dr. Maciej Sułowicz

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Guest Editor

Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31–155 Cracow, Poland
Interests: data acquisition; fault diagnosis of electrical machines and drives; time-frequency analysis; wavelet transforms; artificial intelligence; neural networks; fuzzy logic; measurements and data analysis; diagnostics of industrial process; industry applications diagnostic methods
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Special Issue Information

Dear Colleagues,

Electric machines and drives fault detection using diagnostic signal analysis is one of the most commonly used diagnostic methods. Currently, in the electric machine fault detection process, it is one of the most relevant diagnostic approaches for machine condition assessment, based on measured signals of the operating machine. Non-invasive electric machine fault detection methods that can be used during the machine’s operation in a drive system are very important in many industry branches. For reliable machine condition assessment, especially of those in critical drive systems with high expected reliability, objective methods, tools, and means for realizing the diagnostic process are required.

This Special Issue invites contributions on the topic of fault diagnosis in electric machines and drives based on experimental research and simulation studies. In the area of experimental research, I encourage you to present new diagnostic methods based on new advanced signal processing and analysis techniques, the use of new sensors, or the unusual use of previously used measuring instruments. Issues related to multi-sensor data fusion for fault diagnosis which could be very useful in the construction of smart machines that meet the assumptions of the Industry 4.0 concept will be very much appreciated.

In the field of simulation research, I particularly encourage you to present the results of research based on multi-physics analyzes using FEM based tools as well as methods based on equivalent circuit models or others as an alternative to FEM calculations.

Submissions from the fields of fault diagnosis are new concept and strategies for early detection of faults, minimizing fault occurrence and limiting the consequences of the faults, prognosis changes in the state of the machine are of the special interest.

Contributions from the fields of artificial intelligence, deep learning applied to fault diagnosis in electric machines and drives, predictive maintenance methods are also welcome, including areas such as industry, automotive or other unusual applications.

Prof. Dr. Maciej Sułowicz
Guest Editor

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Keywords

Fault diagnosis
Electric machines
Power drives
Diagnostic mathematical models
Steady state conditions
Transient conditions
Predictive maintenance
Prognosis
Signal processing techniques
Multi-sensor data fusion for fault diagnosis
Artificial intelligence
Industry applications
Automotive applications

Published Papers (11 papers)

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Research

19 pages, 1906 KiB

Open AccessArticle

Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing

by Marcin Tomczyk, Ryszard Mielnik, Anna Plichta, Iwona Goldasz and Maciej Sułowicz

Energies 2022, 15(1), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/en15010117 - 24 Dec 2021

Cited by 1 | Viewed by 1970

Abstract

This paper presents a method of inter-turn short-circuit identification in induction motors during load current variations based on a hybrid analytic approach that combines the genetic algorithm and simulated annealing. With this approach, the essence of the method relies on determining the reference matrices and calculating the distance between the reference matric values and the test matrix. As a whole, it is a novel approach to the process of identifying faults in induction motors. Moreover, applying a discrete optimization algorithm to search for alternative solutions makes it possible to obtain the true minimal values of the matrices in the identification process. The effectiveness of the applied method in the monitoring and identification processes of the inter-turn short-circuit in the early stage of its creation was confirmed in tests carried out for several significant state variables describing physical magnitudes of the selected induction motor model. The need for identification of a particular fault is related to a gradual increase in its magnitude in the process of the induction motor’s exploitation. The occurrence of short-circuits complicates the dynamic properties of the measured diagnostic signals of the system to a great extent. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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20 pages, 43714 KiB

Open AccessArticle

A Method for Monitoring the Technical Condition of Large-Scale Bearing Nodes in the Bodies of Machines Operating for Extended Periods of Time

by Piotr Sokolski and Tadeusz Smolnicki

Energies 2021, 14(20), 6637; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206637 - 14 Oct 2021

Cited by 3 | Viewed by 1446

Abstract

Failure of systems applied in machines comprising rolling and slewing bearings usually causes downtime of the entire heavy machine. The problem of failures can be aggravated by extremely difficult operating conditions, such as significant loads or a harsh environment. The entire issue inspired us to develop a method of monitoring the condition of such units. A study was carried out for six different large-scale excavators which examined strain distributions in the tested subassemblies. In order to estimate the technical condition of wheeled bogies, we used the phenomenon of strain propagation caused by the concentrated force acting in the ring girder web. Flamant theory was utilized to describe this phenomenon. Measurements were performed using strain gauges and the obtained results were compared with the FEM model. To determine whether bearing joints were in a good or bad condition, a coefficient of variation and an impulse factor were introduced as diagnostic indicators. It turned out that by evaluating these indicators, it was possible to distinguish between these two conditions. The method was successfully validated on machines that are in operation. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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23 pages, 15170 KiB

Open AccessArticle

Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors

by Petr Kacor, Petr Bernat and Petr Moldrik

Energies 2021, 14(20), 6573; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206573 - 13 Oct 2021

Cited by 3 | Viewed by 1958

Abstract

In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline measurement procedures and technologies are finally validated by testing measurements on a rotor that simulates the most frequently occurring faults. A test system is also developed that provides the operator continuous information about the running rotor measurements and makes it easier to evaluate the quality of the cast rotor by means of graphical visualization of the faults. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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15 pages, 19148 KiB

Open AccessArticle

Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines

by Krzysztof Tomczyk, Marek Sieja and Grzegorz Nowakowski

Energies 2021, 14(11), 3091; https://0-doi-org.brum.beds.ac.uk/10.3390/en14113091 - 26 May 2021

Viewed by 1384

Abstract

This paper presents the use of identification reference nets (IRNs) for modeling electric power system (EPS) components using electrical machines (EMs) as an example. To perform this type of task, a database of reference nets is necessary, to which the identification net (IN) of the modeled machine is adjusted. Both the IRN and IN are obtained by using a special algorithm that allows the relevant transfer function (TF) to be converted to the rounded trajectory. This type of modeling can be a useful tool for the initial determination of parameters included in the TF associated with the EM, preceding advanced parametric identification procedures, e.g., those based on artificial intelligence methods. Two types of electrical machines are considered, i.e., the squirrel-cage asynchronous (SCA) and brushless direct-current (BLDC) machines. The solution proposed in this paper is a new approach intended for modeling EPS components. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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18 pages, 1780 KiB

Open AccessArticle

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

by Jan Maciej Kościelny, Michał Syfert and Paweł Wnuk

Energies 2021, 14(9), 2476; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092476 - 26 Apr 2021

Cited by 9 | Viewed by 1293

Abstract

The paper analyses the research problem of conducting diagnostic reasoning for dynamic objects to eliminate the possibility of formulating false diagnoses resulting from different delays of the symptoms related to a particular fault while simultaneously striving to obtain high distinguishability. The research aimed to develop a new diagnostic inference method robust to symptom delays and characterised by high accuracy of generated diagnosis. Known methods ensuring the correctness of inference in the case of symptom delays but at the cost of reducing distinguishability of faults have been characterised. A new inference method was developed, which uses the three-valued residual evaluation and knowledge regarding elementary symptom sequences. A formal description of the diagnosing system and the proposed method are given. The method of obtaining the knowledge about the order of symptoms based on a cause-and-effect graph and was characterised. The method’s effectiveness was presented in simulation studies on the example of diagnosing a set of serially connected tanks. The comparison of the fault distinguishability obtained using the proposed method and other approaches illustrates the new method’s advantages. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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23 pages, 11859 KiB

Open AccessArticle

A Novel Feature Extraction Method for the Condition Monitoring of Bearings

by Abdenour Soualhi, Bilal El Yousfi, Hubert Razik and Tianzhen Wang

Energies 2021, 14(8), 2322; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082322 - 20 Apr 2021

Cited by 4 | Viewed by 2199

Abstract

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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13 pages, 3731 KiB

Open AccessArticle

Diagnosis and Mitigation of Electromagnetic Interference Generated by a Brushless DC Motor Drive of an Electric Torque Tool

by Jerzy Baranowski, Tomasz Drabek, Paweł Piątek and Andrzej Tutaj

Energies 2021, 14(8), 2149; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082149 - 12 Apr 2021

Cited by 4 | Viewed by 2234

Abstract

Electrical devices in the consumer markets need to comply with stringent standards for electromagnetic interference (EMI) distortion and electromagnetic compatibility (EMC). This paper presents the results of measurements of electromagnetic interference generated by an electrical drive of an electric torque tool with a brushless DC motor. The measurements were made in accordance with the PN-EN 55014-1:2017-06E standard, in the frequency band of 148 kHz–30 MHz. The results confirmed that the tested drive can meet the requirements defined in this document. Another document, the PN-EN IEC 61000-3-2:2019-04E standard, provides limits for the harmonic content in the current drawn by electrical devices from a single-phase AC line. This paper also presents the results of measurements related to this standard. Harmonics, up to and including the 40th one, were determined and compared with the limits given in the standard for class B devices. The measurement results indicate a need to use an active power factor corrector (PFC) filter. Such a system has been tested by computer simulations. The results confirmed its ability to meet the requirements of relevant standards. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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26 pages, 16822 KiB

Open AccessArticle

Eccentricity in Induction Machines—A Useful Tool for Assessing Its Level

by Janusz Petryna, Arkadiusz Duda and Maciej Sułowicz

Energies 2021, 14(7), 1976; https://0-doi-org.brum.beds.ac.uk/10.3390/en14071976 - 02 Apr 2021

Cited by 6 | Viewed by 2788

Abstract

In the condition monitoring of induction machines operating in various industry sectors, the assessment of eccentricity is as important as the assessment of the condition of windings, bearings, mechanical vibrations or noise. The reasons for the eccentricity can be various; for example, rotor imbalance, damage or wear of the bearings, improper alignment of the rotor and the load machine and finally, assembly errors after overhaul. Disregard of this phenomenon during routine tests may result in the development of vibrations transmitted to the stator windings, faster wear of the bearings and even, in extreme cases, rubbing of the rotor against the stator surface and damage to the windings and local overheating of the machine core. On the basis of years of experience in the diagnosis of large induction machines operating in various industries, the article deals with the problem of developing reliable indicators for assessing the levels of commonly accepted types of eccentricity. Starting from field calculations and analyzing various cases of eccentricity, the methodology for determining the indicators for evaluation from the stator current spectrum is shown. The changes in the values of these indices for various cases of simultaneous occurrence of static and dynamic eccentricity are shown. The calculation results were verified in the laboratory. Also shown are three interesting cases from diagnostic practice in the evaluation of high-power machines in the industry. It has been shown that the proposed indicators are useful and enable an accurate diagnosis of levels of eccentricity. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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23 pages, 12603 KiB

Open AccessArticle

Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

by Przemyslaw Pietrzak and Marcin Wolkiewicz

Energies 2021, 14(6), 1630; https://0-doi-org.brum.beds.ac.uk/10.3390/en14061630 - 15 Mar 2021

Cited by 27 | Viewed by 3154

Abstract

Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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24 pages, 9440 KiB

Open AccessArticle

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

by Pawel Ewert, Teresa Orlowska-Kowalska and Kamila Jankowska

Energies 2021, 14(3), 712; https://0-doi-org.brum.beds.ac.uk/10.3390/en14030712 - 30 Jan 2021

Cited by 26 | Viewed by 3520

Abstract

Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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25 pages, 8322 KiB

Open AccessArticle

Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis

by Arkadiusz Duda and Piotr Drozdowski

Energies 2020, 13(24), 6528; https://0-doi-org.brum.beds.ac.uk/10.3390/en13246528 - 10 Dec 2020

Cited by 14 | Viewed by 2848

Abstract

This paper presents some considerations regarding the application of the stator zero-sequence current component (ZSC) in the fault detection of cage induction machines, including the effects of magnetic core saturation. Faults such as rotor cage asymmetry and static, dynamic, and mixed eccentricity were considered. The research started by developing a harmonic motor model, which allowed us to obtain a voltage equation for the zero-sequence current component. The equation allowed us to extract formulas of typical frequencies for particular fault types. Next, in order to verify the effectiveness of ZSC in induction motor fault diagnosis, finite element calculations and laboratory tests were carried out for the previously mentioned faults for delta and wye connections with neutral wire stator winding configurations. Full article

(This article belongs to the Special Issue Fault Diagnosis of Electrical Machines and Drives)

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