Special Issue "Fault Diagnosis in Electric Motors"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editor

Prof. Dr. Alberto Bellini
E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Alma Mater Studiorum, University of Bologna, Bologna, Italy
Tel. +39 0547 339229
Interests: Electric drives; fault diagnosis of electric machines; energy conversion

Special Issue Information

Dear Colleagues,

Electrical machines are critical components of many industrial processes. Currently, much attention is being paid to energy-efficient processes, and within this framework electrical machines have increased their application areas. However, this requires new technologies for drives, power converters, and machines. Hence, reliability has become a critical issue, since for performance and efficiency reasons, high-frequency power signals are fed into machines, intrinsically reducing the lifetime of insulation and magnetic materials. In this context, the fault detection and diagnosis of electrical machines are of increasing importance and mature technologies are required for “open-loop” system fails for electric drives.

This Special Issue will focus on emerging technologies for efficient, non-invasive and online diagnosis of electrical machines. Topics of interest for publication include, but are not limited to:

  • Fault diagnosis of electrical machines and drives.
  • Specialized signal processing techniques for fault analysis.
  • Fault tolerant electrical systems, including multi-phase machines and/or redundant systems.
  • Fault diagnosis of power supply for electrical machines.
  • Fault diagnosis of electrical power generators.
  • Digital technologies for fault tolerant systems.

Prof. Dr. Alberto Bellini
Guest Editor

Manuscript Submission Information

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Keywords

  • diagnosis
  • digital technologies
  • electric drives
  • electric machines

Published Papers (12 papers)

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Research

Open AccessArticle
Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods
Energies 2019, 12(17), 3392; https://doi.org/10.3390/en12173392 - 03 Sep 2019
Abstract
Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or [...] Read more.
Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines
Energies 2019, 12(17), 3361; https://doi.org/10.3390/en12173361 - 31 Aug 2019
Abstract
Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, [...] Read more.
Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it—short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives
Energies 2019, 12(17), 3232; https://doi.org/10.3390/en12173232 - 22 Aug 2019
Abstract
This paper deals with fault-tolerant control (FTC) of an induction motor (IM) drive. An inter-turn short circuit (ITSC) of the stator windings was taken into consideration, which is one of the most common internal faults of induction machines. The sensitivity of the classic, [...] Read more.
This paper deals with fault-tolerant control (FTC) of an induction motor (IM) drive. An inter-turn short circuit (ITSC) of the stator windings was taken into consideration, which is one of the most common internal faults of induction machines. The sensitivity of the classic, well-known voltage and current models to the stator winding faults was analyzed. It has been shown that these classical state variable estimators are sensitive to induction motor parameter changes during stator winding failure, which results in unstable operation of the direct field-oriented control (DFOC) drive. From a safety-critical applications point of view, it is vital to guarantee stable operation of the drive even during faults of the machine. Therefore, a new FTC system has been proposed, which consists of new modified rotor flux estimators, robust to stator winding faults. A detailed description of the proposed system is presented herein, as well as the results of simulation and experimental tests. Simulation analyses were performed using MATLAB/Simulink software. Experimental tests were carried out on the experimental test bench with a dSpace DS1103 card. The proposed solution could be applied as an alternative rotor flux estimation technique for the modern FTC drive. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current
Energies 2019, 12(13), 2476; https://doi.org/10.3390/en12132476 - 27 Jun 2019
Abstract
Large synchronous generators are the heart of the modern world, while producing the vast majority of the electric power consumed globally. Although they are robust devices, they are prone to degradation and failure. If such failures are not detected at an early stage, [...] Read more.
Large synchronous generators are the heart of the modern world, while producing the vast majority of the electric power consumed globally. Although they are robust devices, they are prone to degradation and failure. If such failures are not detected at an early stage, then the negative impact may be catastrophic in terms of financial costs, repair times, human lives and quality of life. This is the reason for continuous research in the field of condition monitoring aiming towards the reliable operation of synchronous generators. This paper proposes a novel technique for the diagnosis of the static eccentricity in synchronous generators. The proposed approach is off-line and non-intrusive, allowing the estimation of the fault severity with stator current measurements only. The performed work has been carried out with Finite Element Simulations and extensive experimental testing. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors
Energies 2019, 12(12), 2392; https://doi.org/10.3390/en12122392 - 21 Jun 2019
Cited by 1
Abstract
This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an [...] Read more.
This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors
Energies 2019, 12(11), 2105; https://doi.org/10.3390/en12112105 - 01 Jun 2019
Cited by 1
Abstract
Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because [...] Read more.
Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Design for Reliability: The Case of Fractional-Slot Surface Permanent-Magnet Machines
Energies 2019, 12(9), 1691; https://doi.org/10.3390/en12091691 - 05 May 2019
Abstract
Surface permanent-magnet machines are widely used in different applications, from industrial automation to home appliance and electrical traction. Among any possible machine topology, the fractional-slot surface permanent-magnet one has gained increasing importance, because of its high torque density, low cogging torque, extended flux [...] Read more.
Surface permanent-magnet machines are widely used in different applications, from industrial automation to home appliance and electrical traction. Among any possible machine topology, the fractional-slot surface permanent-magnet one has gained increasing importance, because of its high torque density, low cogging torque, extended flux weakening capability and high efficiency. In addition, fractional-slot machines are attractive for tooth concentrated windings, which allow some optimized manufacturing solutions such as modular stator tooth and high slot filling factor, which result in copper volume reduction; cost reduction, and lower stator parasitic resistances. The slot–pole combination is one of the most important design parameter and, as shown in this paper, it affects performances and the robustness of the machine with respect to the manufacturing imperfections. In the literature, slot–pole combinations are optimized at design phase by finite-element analysis relying on a healthy machine model. The original contribution of this paper is a design for reliability method that models manufacturing defects and includes them at design phase in the optimization process of slot–pole combinations. A method is presented that allows defining the optimal design parameters for maximum performances and robustness towards unavoidable imperfections caused by tolerances of the manufacturing process. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems
Energies 2019, 12(8), 1438; https://doi.org/10.3390/en12081438 - 15 Apr 2019
Cited by 2
Abstract
This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal [...] Read more.
This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System
Energies 2019, 12(7), 1351; https://doi.org/10.3390/en12071351 - 08 Apr 2019
Abstract
Among the total energy consumption by utilities, pumping systems contribute 30%. It is evident that a tremendous energy saving potential is achievable by improving the energy efficiency and reducing faults in the pumping system. Thus, optimal operation of centrifugal pumps throughout the operating [...] Read more.
Among the total energy consumption by utilities, pumping systems contribute 30%. It is evident that a tremendous energy saving potential is achievable by improving the energy efficiency and reducing faults in the pumping system. Thus, optimal operation of centrifugal pumps throughout the operating region is desired for improved energy efficiency and extended lifetime of the pumping system. The major harmful operations in centrifugal pumps include cavitation and water hammering. The pump faults are simulated in a real-time experimental setup and the operating point of the pump is estimated correspondingly. In this article, the experimental power quality and vibration measurements of cascade pumps during cavitation and water hammering is recorded for different operating conditions. The results are compared with the normal operating conditions of the pumping system for fault prediction and parameter estimation in a cascade water pumping system. Moreover, the Fast Fourier Transform (FFT) analysis comparison of normal and water hammering (faulty condition) highlights the frequency response of the pumping system. Also, the various power quality issues, i.e., voltage, current, total harmonic distortion, power factor, and active, reactive, and apparent power for a cascade multipump control is discussed in this article. The vibration, FFT, and various power quality measurements serve as input data for the classification of faulty pump operating condition in contrast with the normal operation of pumping system. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring
Energies 2019, 12(5), 794; https://doi.org/10.3390/en12050794 - 27 Feb 2019
Cited by 5
Abstract
This paper presents a method for the detection of broken rotor bars in an induction motor. After introducing a simplified dynamic model of an induction motor with broken cage bars in a rotor field reference frame which allows for observation of its internal [...] Read more.
This paper presents a method for the detection of broken rotor bars in an induction motor. After introducing a simplified dynamic model of an induction motor with broken cage bars in a rotor field reference frame which allows for observation of its internal states, a fault detection algorithm is proposed. Two different motor estimation models are used, and the difference between their rotor flux angles is extracted. A particular frequency component in this signal appears only in the case of broken rotor bars. Consequently, the proposed algorithm is robust enough to load oscillations and/or machine temperature change, and also indicates the fault severity. The method has been verified at different operating points by simulations as well as experimentally. The fault detection is reliable even in cases where traditional methods give ambiguous verdicts. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessFeature PaperArticle
Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals
Energies 2019, 12(4), 597; https://doi.org/10.3390/en12040597 - 14 Feb 2019
Cited by 2
Abstract
The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor [...] Read more.
The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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Open AccessArticle
Symmetrical Loss of Excitation Fault Diagnosis in an Asynchronized High-Voltage Generator
Energies 2018, 11(11), 3054; https://doi.org/10.3390/en11113054 - 06 Nov 2018
Cited by 1
Abstract
As a new type of generator, an asynchronized high-voltage generator has the characteristics of an asynchronous generator and high voltage generator. The effect of the loss of an excitation fault for an asynchronized high-voltage generator and its fault diagnosis technique are still in [...] Read more.
As a new type of generator, an asynchronized high-voltage generator has the characteristics of an asynchronous generator and high voltage generator. The effect of the loss of an excitation fault for an asynchronized high-voltage generator and its fault diagnosis technique are still in the research stage. Firstly, a finite element model of the asynchronized high-voltage generator considering the field-circuit-movement coupling is established. Secondly, the three phase short-circuit loss of excitation fault, three phase open-circuit loss of excitation fault, and three phase short-circuit fault on the stator side are analyzed by the simulation method that is applied abroad at present. The fault phenomenon under the stator three phase short-circuit fault is similar to that under the three phase short-circuit loss of excitation. Then, a symmetrical loss of the excitation fault diagnosis system based on wavelet packet analysis and the Back Propagation neural network (BP neural network) is established. At last, we confirm that this system can eliminate the interference of the stator three phase short-circuit fault, accurately diagnose the symmetrical loss of the excitation fault, and judge the type of symmetrical loss of the excitation fault. It saves time to find the fault cause and improves the stability of system operation. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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