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Condition Monitoring and Failure Prevention of Electric Machines

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 22642

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Special Issue Editors

Prof. Dr. Yuling He

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Guest Editor

Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Interests: condition monitoring and failure prevention of electric machines; mathematical modelling for electromechanical systems; intelligent equipment development

Dr. David Gerada

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Guest Editor

Power Electrics, Machines and Control (PEMC) Research Group, The University of Nottingham, Nottingham NG7 2RD, UK
Interests: high-speed machines; novel materials and their applications to electromechanical energy conversion; traction machines
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Conggan Ma

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Guest Editor

School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China
Interests: intelligent fault diagnosis of vehicle electric machines; vibration/noise test and control in electric machines

Prof. Dr. Haisen Zhao

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Guest Editor

School of Electrical and Electronics Engineering, North China Electric Powre University, Beijing 102206, China
Interests: electrical energy conversion and high-efficiency utilization; analysis and control of new energy power systems

Special Issue Information

Dear Colleagues,

Electric machines are key components for both power generation and industrial propulsion. Electric machines include not only rotating motors/generators, but also all kinds of electric energy conversion/transformation machines (power transformers, linear motors, etc.).

The development tendency of current electric machines is moving towards larger capacity, higher power density, lower mass, and better strength. This tendency places stricter demands for the stable and reliable operation of the components in electric machines.

Since the unexpected breakdown of electric machines leads to considerable economic loss and even disaster, the condition monitoring and failure prevention of electric machines is important.

With the rapid development of computer science and intelligent technologies, more and more novel monitoring and diagnosis approaches/methods are being developed. To provide a qualified gathering for readers/researchers on this topic, we propose this Special Issue primarily focusing on the issues related to the advanced monitoring, diagnosis, and prevention of typical and complex faults in all kinds of electric machines.

The scope of this Special Issue includes but is not limited to the following:

Fault characteristic analysis of all kinds of electric machines;
Vibration/noise test and control of different electric machines;
Fault detection and diagnosis of large-capacity electric machines, especially power generators;
Failure-prevention-based design/manufacturing improvement for special-use electric machines;
Online monitoring and fault diagnosis in wind generators;
Property analysis and improvement in transportation motors;
New materials/structure/component development and application for new/high-performance electric machines;
Advanced signal processing methods to extract faulty characteristics in electric machines;
Monitoring/analysis technologies in electric machine–power grid coupling systems.

Prof. Dr. Yuling He
Dr. David Gerada
Prof. Dr. Conggan Ma
Prof. Dr. Haisen Zhao
Guest Editors

Manuscript Submission Information

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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

electric machines
condition monitoring
fault diagnosis
failure prevention
faulty characteristic analysis
vibration/noise control
property improvement
new technology development

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Published Papers (11 papers)

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Research

17 pages, 23093 KiB

Open AccessArticle

Analysis of Direct Torque Control Response to Stator and Rotor Faults in Permanent Magnet Synchronous Machines

by Ibrahim M. Allafi and Shanelle N. Foster

Energies 2023, 16(19), 6940; https://doi.org/10.3390/en16196940 - 3 Oct 2023

Cited by 1 | Viewed by 1651

Abstract

Direct-torque-control-driven permanent magnet synchronous machines eliminate the need for a position sensor while providing improved torque dynamics. However, the structure, regulation principle and nature of compensation of hysteresis-based controllers used in direct torque control impacts performance under faulty operating conditions. This paper analyzes the reaction of direct torque control to the presence of various faults that occur in permanent magnet synchronous machines. The analysis presented reveals that the direct torque control injects a negative sequence voltage and manipulates the torque angle to meet the control objectives when a fault occurs. The co-simulation of finite element analysis and a multi-physic circuit simulator is used to validate the response of the hysteresis-based controller to the machine health. The results indicate that the hysteresis comparators have the ability to mask the impact of the faults in the direct-torque-control-driven permanent magnet synchronous machines. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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17 pages, 5010 KiB

Open AccessArticle

Optimization Method Based on Hybrid Surrogate Model for Pulse-Jet Cleaning Performance of Bag Filter

by Shirong Sun, Libing Liu, Zeqing Yang, Wei Cui, Chenghao Yang, Yanrui Zhang and Yingshu Chen

Energies 2023, 16(12), 4652; https://doi.org/10.3390/en16124652 - 12 Jun 2023

Cited by 1 | Viewed by 1738

Abstract

The pulse-jet cleaning process is a critical part of the bag filter workflow. The dust-cleaning effect has a significant impact on the operating stability of bag filters. Aiming at the multi-parameter optimization problem involved in the pulse-jet cleaning process of bag filters, the construction method of hybrid surrogate models based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging sub-surrogate models is investigated. With four sub-surrogate model hybrid modes, the corresponding hybrid surrogate models, namely PR-HSM, PK-HSM, RK-HSM, and PRK-HSM, are constructed for the multi-parameter optimization involved in the pulse-jet cleaning process of bag filters, and their objective function is the average pressure on the inner side wall of the filter bag at 1 m from the bag bottom. The genetic algorithm is applied to search for the optimal parameter combination of the pulse-jet cleaning process. The results of simulation experiments and optimization calculations show that compared with the sub-surrogate model PRSM, the evaluation indices RMSE,

R^{2}

, and RAAE of the hybrid surrogate model RK-HSM are 9.91%, 4.41%, and 15.60% better, respectively, which greatly enhances the reliability and practicability of the hybrid surrogate model. After using the RK-HSM, the optimized average pressure F on the inner side wall of the filter bag at 1 m from the bag bottom is −1205.1605 Pa, which is 1321.4543 Pa higher than the average pressure value under the initial parameter condition set by experience, and 58.4012 Pa to 515.2836 Pa higher than using the three sub-surrogate models, verifying its usefulness. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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18 pages, 6488 KiB

Open AccessArticle

Parameter Identification of Asynchronous Load Nodes

by Andrey Kryukov, Konstantin Suslov, Pavel Ilyushin and Azat Akhmetshin

Energies 2023, 16(4), 1893; https://doi.org/10.3390/en16041893 - 14 Feb 2023

Cited by 3 | Viewed by 1561

Abstract

Asynchronous loads (AL), because of their low negative-sequence resistance, produce the effect of reduced unbalance at their connection points. Therefore, proper modeling of unbalanced load flows in power supply systems requires properly accounting for AL. Adequate models of the induction motor can be realized in the phase frame of reference. The effective use of such models is possible only if accurate data on the parameters of induction motor equivalent circuits for positive and negative sequences are available. Our analysis shows that the techniques used to determine these parameters on the basis of reference data can yield markedly disparate results. It is possible to overcome this difficulty by applying parameter identification methods that use the phase frame of reference. The paper proposes a technique for parameter identification of models of individual induction motors and asynchronous load nodes. The results of computer-aided simulation allow us to conclude that by using parameter identification, we can obtain an equivalent model of an asynchronous load node, and such a model provides high accuracy for both balanced and unbalanced load flow analysis. By varying load flow parameters, we demonstrate that the model proves valid over a wide range of their values. We have proposed a technique for the identification of asynchronous load nodes with such asynchronous loads, including electrical drives equipped with static frequency converters. With the aid of the AL identification models proposed in this paper, it is possible to solve the following practical tasks of management of electric power systems: increasing the accuracy of modeling their operating conditions; making informed decisions when taking measures to reduce unbalance in power grids while accounting for the balancing adjustment effect of AL. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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21 pages, 7676 KiB

Open AccessArticle

Condition Monitoring Accuracy in Inverter-Driven Permanent Magnet Synchronous Machines Based on Motor Voltage Signature Analysis

by Ibrahim M. Allafi and Shanelle N. Foster

Energies 2023, 16(3), 1477; https://doi.org/10.3390/en16031477 - 2 Feb 2023

Cited by 6 | Viewed by 2259

Abstract

Condition monitoring and preventative maintenance are essential for reliable and efficient operation of permanent magnet synchronous machines driven by inverters. There are two types of industrial inverter drives available: field oriented control and direct torque control. Their compensation nature and control structure are distinct and, therefore, the condition monitoring approach designed for the former control may not be applicable to the latter one. In this paper, we investigate the Motor Voltage Signature Analysis approach for both inverter drives under healthy and faulty conditions. Four typical fault conditions are addressed: turn-to-turn short circuit, high resistance contact, static eccentricity, and local demagnetization. High fidelity cosimulation is developed by coupling the finite element machine model with both control drives. The spectral elements of the commanded stator voltage are utilized as indicators for supervised classification to identify, categorize, and estimate the severity of faults. Linear discriminate analysis, k-nearest neighbor, and support vector machines are the classification techniques used. Results indicate that the condition monitoring based on the Motor Voltage Signature Analysis performs adequately in field oriented control. Nevertheless, the utilized monitoring scheme does not exhibit satisfactory performance in direct torque control owing to the nonlinear characteristics and tolerance nature of this drive. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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15 pages, 6226 KiB

Open AccessArticle

Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions

by Jose R. Huerta-Rosales, David Granados-Lieberman, Juan P. Amezquita-Sanchez, Arturo Garcia-Perez, Maximiliano Bueno-Lopez and Martin Valtierra-Rodriguez

Energies 2022, 15(22), 8508; https://doi.org/10.3390/en15228508 - 14 Nov 2022

Cited by 1 | Viewed by 1836

Abstract

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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18 pages, 9208 KiB

Open AccessArticle

Dynamic Simulation of Starting and Emergency Conditions of a Hydraulic Unit Based on a Francis Turbine

by Andrey Achitaev, Pavel Ilyushin, Konstantin Suslov and Sergey Kobyletski

Energies 2022, 15(21), 8044; https://doi.org/10.3390/en15218044 - 29 Oct 2022

Cited by 1 | Viewed by 2236

Abstract

The Francis hydro-turbine is a typical nonlinear system with coupled hydraulic, mechanical, and electrical subsystems. It is difficult to understand the reasons for its operational failures, since the main cause of failures is due to the complex interaction of the three subsystems. This paper presents an improved dynamic model of the Francis hydro-turbine. This study involves the development of a nonlinear dynamic model of a hydraulic unit, given start-up and emergency processes, and the consideration of the effect of water hammer during transients. To accomplish the objectives set, existing models used to model hydroelectric units are analyzed and a mathematical model is proposed, which takes into account the dynamics during abrupt changes in the conditions. Based on these mathematical models, a computer model was developed, and numerical simulation was carried out with an assessment of the results obtained. The mathematical model built was verified on an experimental model. As a result, a model of a hydraulic unit was produced, which factors in the main hydraulic processes in the hydro-turbine. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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15 pages, 11449 KiB

Open AccessArticle

Analysis of the Characteristics of Stator Circulating Current Inside Parallel Branches in DFIGs Considering Static and Dynamic Air-Gap Eccentricity

by Yu-Ling He, Xiang-Ao Liu, Ming-Xing Xu, Wen Zhang, Wen-Jie Zheng, De-Rui Dai, Gui-Ji Tang, Shu-Ting Wan and David Gerada

Energies 2022, 15(17), 6152; https://doi.org/10.3390/en15176152 - 24 Aug 2022

Cited by 2 | Viewed by 1946

Abstract

In this article, the stator winding circulating current inside parallel branches (CCPB) of a doubly fed induction generator (DFIG) is comprehensively investigated. Different from other studies, this study not only focuses on the CCPB in radial static air-gap eccentricity (RSAGE) and radial dynamic air-gap eccentricity (RDAGE) but also takes the radial hybrid air-gap eccentricity (RHAGE) cases into account. Firstly, the detailed expressions of CCPB in normal and radial air-gap eccentricity (RAGE) are obtained. Then, the finite element analysis (FEA) and experimental studies are performed on a four-pole DFIG with a rated speed of 1470 rpm in order to verify the theoretical analysis. It is shown that the RAGE increases the amplitude of the CCPB and brings new frequency components to the CCPB. For RSAGE, the CCPB brings new frequency components, which are f₁ (50) and f_μ (540/640). For RDAGE, the newly generated frequency components are f₁± f_r (25/75), f_u ± f_r (515/565/615/665, and k = ±1). For RHAGE, the newly added frequency components in RSAGE and RDAGE are present at the same time. In addition, the more the RAGE degree is, the larger the amplitude of characteristic frequency components will be. The results obtained in this paper can be used as a supplementary criterion for diagnosing DFIG eccentric faults. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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22 pages, 3906 KiB

Open AccessArticle

Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks

by Zeqing Yang, Wenbo Zhang, Wei Cui, Lingxiao Gao, Yingshu Chen, Qiang Wei and Libing Liu

Energies 2022, 15(15), 5671; https://doi.org/10.3390/en15155671 - 4 Aug 2022

Cited by 3 | Viewed by 1836

Abstract

Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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15 pages, 1872 KiB

Open AccessArticle

In-Situ Efficiency Estimation of Induction Motors Based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA)

by Mahamadou Negue Diarra, Yifan Yao, Zhaoxuan Li, Mouhamed Niasse, Yonggang Li and Haisen Zhao

Energies 2022, 15(13), 4905; https://doi.org/10.3390/en15134905 - 5 Jul 2022

Cited by 11 | Viewed by 1925

Abstract

The accuracy estimation of induction motors’ efficiency is beneficial and crucial in the industry for energy savings. The requirement for in situ machine efficiency estimation techniques is increasing in importance because it is the precondition to making the energy-saving scheme. Currently, the torque and speed identification method is widely applied in online efficiency estimation for motor systems. However, the higher precision parameters, such as stator resistance R_s and equivalent resistance of iron losses R_fe, which are the key to the efficiency estimation process with the air gap torque method, are of cardinal importance in the estimation process. Moreover, the computation burden is also a severe problem for the real-time data process. To solve these problems, as for the torque and speed-identification-based efficiency estimation method, this paper presents a lower time burden method based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA). The contribution of the proposed method is to transform the disadvantages of former algorithms to develop a reliable hybrid algorithm to identify the crucial parameters, namely, R_s and R_fe. Sensorless speed identification based on the rotor slot harmonic frequency (RSHF) method is adopted for speed determination. This hybrid algorithm reduces the computation burden by about 1/3 compared to the classical genetic algorithm (GA). The proposed method was validated by testing a 5.5 kW motor in the laboratory and a 10 MW induction motor in the field. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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16 pages, 5242 KiB

Open AccessArticle

Analysis of the Influence of Parameter Condition on Whole Load Power Factor and Efficiency of Line Start Permanent Magnet Assisted Synchronous Reluctance Motor

by Jin Wang, Yan Li, Shengnan Wu, Zhanyang Yu and Lihui Chen

Energies 2022, 15(11), 3866; https://doi.org/10.3390/en15113866 - 24 May 2022

Cited by 2 | Viewed by 1582

Abstract

Line start permanent magnet assisted synchronous reluctance motor (LSPMaSynRM) is an important high-efficiency and high-quality motor. Its parameter matching and operating characteristics are complex, with an increase in salient ratio resulting in a valley in the power factor curve. In this study, the formation principle of power factor curve valley was first deduced by the mathematical model of LSPMaSynRM. Then, the parameter matching principle of power factor curve valley was analyzed in detail. On this basis, the characteristics of load rate corresponding to the critical state of the power factor curve valley were obtained, and its influence on whole load efficiency was analyzed. The design principles for optimal efficiency in wide high-efficiency region and specific load point were obtained. Finally, a 5.5 kW LSPMaSynRM was designed and manufactured to verify the validity of the principle. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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11 pages, 2973 KiB

Open AccessArticle

Electromagnetic Torque Fluctuating Properties under Dynamic RISC Fault in Turbogenerators

by Yuling He, Minghao Qiu, Xinghua Yuan, Haipeng Wang, Mengya Jiang, Chris Gerada and Shuting Wan

Energies 2022, 15(10), 3821; https://doi.org/10.3390/en15103821 - 23 May 2022

Viewed by 2007

Abstract

This paper analyzes the electromagnetic torque (EMT) fluctuation characteristics in synchronous generators under rotor interturn short-circuit (DRISC) fault. The novelty of this paper is that the DRISC fault is proposed based on the intermittent interturn short circuit existing in the actual operation and compared with the static rotor interturn short-circuit (SRISC) fault. In the work, by studying the influence of DRISC with different positions and different short-circuit degrees, the fluctuation characteristic of the EMT is analyzed and verified. The results show that when the DRISC5% fails, the location is in slot 3, the amplitude of first harmonic decreases by 7.2%, second harmonic amplitude increases by 33.4%, third harmonic decreases by 4.3%, and fourth harmonic increases by 26.8%. As the degree increased and positioned away from the large tooth of the DRISC, the overall EMT amplitude and reverse pulse increased, first and third harmonics decreased, and second and fourth harmonics increased. Full article

(This article belongs to the Special Issue Condition Monitoring and Failure Prevention of Electric Machines)

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