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Condition Monitoring and Diagnosis of Electrical Machines

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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 (30 June 2020) | Viewed by 51811

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Prof. Dr. Loránd Szabó

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Department of Electrical Machines and Drives, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: variable reluctance machines; fault tolerant electrical machines; linear and surface motors; condition monitoring and diagnosis of electrical machines
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Special Issue Information

Dear Colleagues,

Condition monitoring and diagnosis of electrical machines is a crucial industrial task These are indispensable for the safe and reliable working of the electromechanical systems, moreover of those of safety-critical importance (used in energy power generation, transportation, medical and defense applications, etc.).
The key aim of this Special Issue is to present novel theoretical and applicative research developments covering these topics. Therefore, high quality, not yet published papers from researchers and professionals working in the field of the diagnostics and monitoring of electrical machines are expected.
The proposed papers can deal with detecting winding, bearing or other mechanical faults of the electrical machines and their power converters by means of online and offline, intrusive and non-intrusive, signal-, model- or data-based methods. The proposed approaches can be based on improvements of the traditional time and frequency domain and discrete wavelet transform analysis, or modern artificial intelligence-based methods.
Papers covering advanced diagnosis and monitoring methods for machines other than induction machines are strongly welcomed. Also, those dealing with methods to be directly applied in the industrial environment or with comprehensive industrial experiences in the field will be highly appreciated.

Prof. Dr. Lorand Szabo
Guest Editor

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Keywords

acoustic monitoring
artificial intelligence-based methods
big data feature learning
data-based techniques
deep learning
digital image processing
digital signal processing
empirical mode decomposition
entropy based-methods
feature extraction methods
fuzzy logic-based techniques
industrial Internet of Things
intelligent sensors
machine current signature analysis
machine learning
mathematical morphology
model-based techniques
neural network-based methods
noise filtering
partial discharge monitoring
signal-based techniques
statistical diagnosis methods
stray flux monitoring
support vector machine
vibration monitoring

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Published Papers (12 papers)

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Research

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17 pages, 8970 KiB

Open AccessArticle

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

by Zia Ullah, Bilal Ahmad Lodhi and Jin Hur

Energies 2020, 13(15), 3834; https://doi.org/10.3390/en13153834 - 26 Jul 2020

Cited by 51 | Viewed by 4436

Abstract

Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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20 pages, 8097 KiB

Open AccessArticle

Condition-Monitoring System for Identification and Representation of the Capability Diagram Limits for Multiple Synchronous Generators in a Hydro Power-Plant

by Boris Glavan, Zlatko Hanić, Marinko Kovačić and Mario Vražić

Energies 2020, 13(15), 3800; https://doi.org/10.3390/en13153800 - 24 Jul 2020

Cited by 3 | Viewed by 2972

Abstract

This paper presents an experience in the design and implementation of the condition-monitoring system for the synchronous generators whose primary purpose is to record data for the identification of the capability limits of the P–Q diagram of three generators in hydro power-plant. Paper presents details about the monitoring system, the underlying theory of the identification of the synchronous generator model with a focus on the calculation of the capability limits in the P–Q diagram. Furthermore, a computationally efficient method for the representation of capability limits suitable for the implementation within the industrial automation and control system of the power-plant is described in detail. Finally, the capability diagrams for three generators were implemented in the power-plant supervisory control and data acquisition system (SCADA) system. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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37 pages, 40496 KiB

Open AccessArticle

On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

by Wojciech Sikorski, Krzysztof Walczak, Wieslaw Gil and Cyprian Szymczak

Energies 2020, 13(12), 3271; https://doi.org/10.3390/en13123271 - 24 Jun 2020

Cited by 29 | Viewed by 9400

Abstract

The article presents a novel on-line partial discharge (PD) monitoring system for power transformers, whose functioning is based on the simultaneous use of three unconventional methods of PD detection: high-frequency (HF), ultra-high frequency (UHF), and acoustic emission (AE). It is the first monitoring system equipped in an active dielectric window (ADW), which is a combined ultrasonic and electromagnetic PD sensor. The article discusses in detail the process of designing and building individual modules of hardware and software layers of the system, wherein the most attention was paid to the PD sensors, i.e., meandered planar inverted-F antenna (MPIFA), high-frequency current transformer (HFCT), and active dielectric window with ultrasonic transducer, which were optimized for detection of PDs occurring in oil-paper insulation. The prototype of the hybrid monitoring system was first checked on a 330 MVA large power transformer during the induced voltage test with partial discharge measurement (IVPD). Next, it was installed on a 31.5 MVA substation power transformer and integrated according to the standard IEC 61850 with SCADA (Supervisory Control and Data Acquisition) system registering voltage, active power, and oil temperature of the monitored unit. The obtained results showed high sensitivity of the manufactured PD sensors as well as the advantages of the simultaneous use of three techniques of PD detection and the possibility of discharge parameter correlation with other power transformer parameters. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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18 pages, 7927 KiB

Open AccessArticle

Influence of Different Structure and Specification Parameters on the Propagation Characteristics of Optical Signals Generated by GIL Partial Discharge

by Xiaoxin Chen, Chen Li, Liangjin Chen, Hui Wang, Yiming Zang and Weijia Yao

Energies 2020, 13(12), 3241; https://doi.org/10.3390/en13123241 - 23 Jun 2020

Cited by 2 | Viewed by 1787

Abstract

Partial discharge (PD) leads to the generation of electrical, acoustic, optical, and thermal signals. The propagation characteristics of optical signals in gas insulated metal-enclosed transmission lines (GIL) are the basis of optical detection research. This paper simulates the propagation of PD optical signals in GIL through modeling GIL with different structures and specification parameters. By analyzing the optical parameters on the probe surface and the detection points when the PD source position is different, the influence of the difference in specifications caused by the voltage level on the propagation of the GIL PD optical signal is studied. The results show that the GIL cavity structure will affect the faculae distribution and the relative irradiance (RI) of the detection surface; the PD source position has a huge impact on the faculae distribution on the detection surface, but has little influence on the RI; as the voltage rises, the faculae distribution on the detection surface becomes more obvious, and the mean of RI decreases. The above results have the reference value for the manufacture of GIL equipment and the research of PD optical detection. When the specular reflection coefficient of surface material is smaller and the diffuse reflection coefficient is larger, the outline of the light spot is clearer, the proportion of brighter parts is larger, and the maximum value of the RI is larger. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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13 pages, 4681 KiB

Open AccessArticle

Frequency Response Modelling of Transformer Windings Connected in Parallel

by Szymon Banaszak, Konstanty Marek Gawrylczyk and Katarzyna Trela

Energies 2020, 13(6), 1395; https://doi.org/10.3390/en13061395 - 17 Mar 2020

Cited by 10 | Viewed by 2763

Abstract

This paper describes the approach to the frequency response modelling of transformer windings consisting of coils connected in parallel. At present, computer models are intensively developed with the aim of simulating the influence of faults on the frequency response of the active part of power transformers. Frequency response analysis (FRA) is one of the standard methods used for the assessment of the mechanical condition of a transformer’s windings and core. The interpretation of the FRA results is crucial in the diagnostics of the active part of the transformer. Proper simulations of the FRA results allow the improvement and simplification of the interpretation process of the windings’ faults. Usually only serial winding wires are simulated in computer modelling and parallel wires are simplified, leading to simulation inaccuracies. In this work, a combined electromagnetic field/network method, which includes parallel connections of the coils, is proposed. The method is based on lumped RLC elements. The results of the analysis conducted by the computer model are referred to as the real transformer measurement. The modelling was also performed for the case of a winding with a fault. The results of modelling were assessed with four numerical indices used for FRA interpretation. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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19 pages, 6850 KiB

Open AccessArticle

Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors

by Marcin Skora, Pawel Ewert and Czeslaw T. Kowalski

Energies 2019, 12(21), 4212; https://doi.org/10.3390/en12214212 - 5 Nov 2019

Cited by 13 | Viewed by 4223

Abstract

In recent years, the number of outer rotor permanent magnet brushless direct current (PM BLDC) motor drives has been intensively growing. Due to the specifics of drive operation, bearing faults are especially common, which results in motor stoppage. In a number of these types of motor applications, the monitoring and diagnostics of bearing conditions is relatively rare. This article presents the results of research aimed at searching for simple and simultaneously effective methods for assessing the condition of bearings that can be built into the drive control system. In the experimental research, four vibration signal processing methods were analysed with regards to the identification accuracy of fault symptoms in the geometric elements of bearings (characteristic frequencies). The results are presented for three cases of bearing faults and compared with a new bearing, they were obtained based on a vibration signal analysis using the classical fast Fourier transform (FFT), Fourier transform of signal absolute values, Fourier transform of an envelope signal obtained using the Hilbert transform, and the Fourier transform of a signal filtered with the Teager–Kaiser energy operator (TKEO). Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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15 pages, 5142 KiB

Open AccessArticle

Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors

by Isac Antônio dos Santos Areias, Luiz Eduardo Borges da Silva, Erik Leandro Bonaldi, Levy Ely de Lacerda de Oliveira, Germano Lambert-Torres and Vitor Almeida Bernardes

Energies 2019, 12(21), 4029; https://doi.org/10.3390/en12214029 - 23 Oct 2019

Cited by 15 | Viewed by 4443

Abstract

Motor current signature analysis (MCSA) enables non-invasive monitoring, without interruption of machine operation in a remote and online way, allowing the identification of various types of faults of electrical and mechanical nature without the need of accessing the motor itself, but only its supply cables. Despite its advantages, it has limitations in accurately diagnosing incipient roller bearing faults. For the detection of incipient roller bearing faults, envelope analysis of vibration signals is a well-known and stablished technique used by motor condition monitoring experts for a long time, overcoming MCSA for that purpose. Thus, it is proposed in this paper, that the fault characteristic frequencies of roller bearings are identified in the current spectrum with the aid of envelope analysis on the bearing vibration signal. After this aided identification, the fault related spectral components in the current spectrum can be correctly tracked over time for trending evaluation and decision-making. This approach can represent a significant economic value in a motor condition monitoring program, since vibration envelope analysis is performed only at a first step and, after that, its results can be applied for the MCSA monitoring of all same-model motor drivers in an industrial site. This approach is even more valuable considering the concept of the Self-Supplied Wireless Current Transducer (SSWCT) also proposed in this paper. The SSWCT is an Industrial Internet of Things (IIOT) device for MCSA application in an Industry 4.0 environment. This proposed device has wireless communication interface and wireless/battery less power supply, being supplied by the energy harvested from the magnetic field of the same currents it is transducing. So, it is a completely galvanic isolated monitoring device, without batteries and without any electric connections to the industry electric system, easily installable to the motor cables, not using precious space in the electric panels of the motor control centers and not having any physical contact to the monitored asset. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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14 pages, 3648 KiB

Open AccessArticle

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

by Nathaly Murcia-Sepúlveda, Jorge M. Cruz-Duarte, Ignacio Martin-Diaz, Arturo Garcia-Perez, J. Juan Rosales-García, Juan Gabriel Avina-Cervantes and Carlos Rodrigo Correa-Cely

Energies 2019, 12(19), 3736; https://doi.org/10.3390/en12193736 - 30 Sep 2019

Cited by 3 | Viewed by 2525

Abstract

Fault monitoring systems in Induction Motors (IMs) are in high demand since many production environments require yielding detection tools independent of their power supply. When IMs are inverter-fed, they become more complicated to diagnose via spectral techniques because those are susceptible to produce false positives. This paper proposes an innovative and reliable methodology to ease the monitoring and fault diagnosis of IMs. It employs fractional Gaussian windows determined from Caputo operators to stand out from spectral harmonic trajectories. This methodology was implemented and simulated to process real signals from an induction motor, in both healthy and faulty conditions. Results show that the proposed technique outperforms several traditional approaches by getting the clearest and most useful patterns for feature extraction purposes. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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29 pages, 14942 KiB

Open AccessArticle

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

by Cleber Gustavo Dias, Luiz Carlos da Silva and Ivan Eduardo Chabu

Energies 2019, 12(12), 2381; https://doi.org/10.3390/en12122381 - 20 Jun 2019

Cited by 19 | Viewed by 3863

Abstract

This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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20 pages, 13254 KiB

Open AccessArticle

Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive

by Grzegorz Tarchała and Marcin Wolkiewicz

Energies 2019, 12(8), 1507; https://doi.org/10.3390/en12081507 - 21 Apr 2019

Cited by 9 | Viewed by 3284

Abstract

This paper deals with the diagnosis of stator winding inter-turn faults for an induction motor drive operating without a speed sensor in a speed-sensorless mode. The rotor direct field oriented control structure (DFOC) was applied, its reference current and voltage component values were analyzed, and their selected harmonics were applied as effective fault indicators. To ensure robust speed estimation, a sliding mode model reference adaptive system (SM-MRAS) estimator was selected. The influence of load torque, reference speed, proportional-integral (PI) controller parameters, and short-circuit current on fault diagnosis and speed estimation performance was verified. Experimental test results obtained for a 3 kW induction motor drive are included. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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12 pages, 1542 KiB

Open AccessArticle

Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach

by Hong-Chan Chang, Yu-Ming Jheng, Cheng-Chien Kuo and Yu-Min Hsueh

Energies 2019, 12(8), 1471; https://doi.org/10.3390/en12081471 - 18 Apr 2019

Cited by 31 | Viewed by 5008

Abstract

This study develops a condition monitoring system, which includes operating condition monitoring (OCM) and fault diagnosis analysis (FDA). The OCM uses a vibration detection approach based on the ISO 10816-1 and NEMA MG-1 international standards, and the FDA uses a vibration-electrical hybrid approach based on various indices. The system can acquire real-time vibration and electrical signals. Once an abnormal vibration has been detected by using OCM, the FDA is applied to classify the type of faults. Laboratory results indicate that the OCM can successfully diagnose induction motors healthy condition, and FDA can classify the various damages stator fault, rotor fault, bearing fault and eccentric fault. The FDA with the hybrid approach is more reliable than the traditional approach using electrical detection alone. The proposed condition monitoring system can provide simple and clear maintenance information to improve the reliability of motor operations. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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Review

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26 pages, 1847 KiB

Open AccessReview

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

by Lucia Frosini

Energies 2020, 13(19), 5066; https://doi.org/10.3390/en13195066 - 27 Sep 2020

Cited by 50 | Viewed by 4767

Abstract

This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature. Full article

(This article belongs to the Special Issue Condition Monitoring and Diagnosis of Electrical Machines)

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