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Dissertations / Theses on the topic 'Electric motor fault diagnosis'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Cameron, James R. "Vibration and current monitoring for on-line detection of air-gap eccentricity in induction motors." Thesis, Robert Gordon University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328784.

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2

Akin, Bilal. "Low-cost motor drive embedded fault diagnosis systems." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1488.

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3

Sekar, Booma Devi. "Hybrid intelligent technology based fault diagnosis system for squirrel cage induction motor." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1678023.

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4

Tugsal, Umut. "FAULT DIAGNOSIS OF ELECTRONIC FUEL CONTROL (EFC) VALVES VIA DYNAMIC PERFORMANCE TEST METHOD." ProQuest, 2009. http://hdl.handle.net/1805/2094.

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Indiana University-Purdue University Indianapolis (IUPUI)
Electronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.
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5

Cheng, Siwei. "Utilizing the connected power electronic converter for improved condition monitoring of induction motors and claw-pole generators." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43638.

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This dissertation proposes several simple, robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters and claw-pole generators with built-in rectifiers. While the flexible energy forms synthesized by power electronic converters greatly enhance the performance and expand the operating region of induction motors and claw-pole generators, they also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. In this dissertation, special characteristics of the connected closed-loop inverter and rectifier have been thoroughly analyzed, with particular interest in their impact on fault behaviors of the induction motor and the claw-pole generator. Based on the findings obtained from the theoretical and experimental analysis, several sensorless thermal, mechanical, and insulation monitoring methods are proposed by smartly utilizing special features and capabilities of the connected power electronic converter. A simple and sensitive stator turn-fault detector is proposed for induction motors fed by closed-loop inverter. In addition, a stator thermal monitoring method based on active DC current injection and direct voltage estimation is also proposed to prevent the closed-loop controlled induction motors from thermally overloading. The performance of both methods is demonstrated by extensive experimental results. Methods to detect serpentine belt slip, serpentine belt defect, rotor eccentricity have been proposed for claw-pole generators using only the available electric sensor information. Methods to detect and protect stator turn faults in claw-pole generators are also presented in this dissertation. Lastly, a novel method to detect the generalized bearing roughness fault is proposed. All the proposed condition monitoring techniques have been validated by experimental results.
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6

Moosavi, Anchehpoli Seyed Saeid. "Analysis and diagnosis of faults in the PMSM drivetrains for series hybrid electrical vehicles (SHEVs)." Thesis, Belfort-Montbéliard, 2013. http://www.theses.fr/2013BELF0224/document.

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L'intérêt pour les véhicules électriques ne cesse de croitre au sein de la société contemporaine compte tenu de ses nombreuses interrogations sur l’environnement et la dépendance énergétique. Dans ce travail de thèse, nous essayons d’améliorer l’acceptabtabilité sociétale du véhicule électrique en essayant de faire avancer la recherche sur le diagnostique des défauts d’une chaine de traction électrique. Les résultats escomptés devraient permettre à terme d’améliorer la fiabilité et la durabilité de ces systèmes.Nous commençons par une revue des problèmes des défauts déjà apparus dans les véhicules hybrides séries qui disposent de l’architecture la plus proche du véhicule électrique. Une étude approfondie sur le diagnostic des défauts d’un convertisseur de puissance statique (AC-DC) ainsi que celle du moteur synchrone à aimants permanents est menée. Quatre types de défauts majeurs ont été répertoriés concernant le moteur (court-circuit au stator, démagnétisation, excentricité du rotor et défaut des roulements). Au niveau du convertisseur, nous avons considéré le défaut d’ouverture des interrupteurs. Afin d’être dans les mêmes conditions d’utilisation réelle, nous avons effectué des tests expérimentaux à vitesse et charge variables. Ce travail est basé aussi bien sur l’expérimentation que sur la modélisation. Comme par exemple, la méthode des éléments finis pour l’étude de la démagnétisation de la machine. De même, l’essai en court-circuit du stator du moteur en présence d’un contrôle vectoriel.Afin de réaliser un diagnostic en ligne des défauts, nous avons développé un modèle basé sur les réseaux de neurones. L’apprentissage de ce réseau de neurone a été effectué sur la base des résultats expérimentaux et de simulations, que nous avons réalisées. Le réseau de neurones est capable d'assimiler beaucoup de données. Ceci nous permet de classifier les défauts en termes de sévérité et de les localiser. Il permet ainsi d'évaluer le degré de performance de la chaine de traction électrique en ligne en présence des défauts et nous renseigner ainsi sur l'état de santé du système. Ces résultats devraient aboutir à l’élaboration d’une stratégie de contrôle tolérant aux défauts auto-reconfigurable pour prendre en compte les modes dégradés permettant une continuité de service du véhicule ce qui améliorera sa disponibilité
The interest in the electric vehicles rose recently due both to environmental questions and to energetic dependence of the contemporary society. Accordingly, it is necessary to study and implement in these vehicle fault diagnosis systems which enable them to be more reliable and safe enhancing its sustainability. In this work after a review on problem of faults in the drivetrain of series hybrid electric vehicles (SHEV), a deep investigation on fault diagnosis of AC-DC power converter and permanent magnet synchronous motor (PMSM) have been done as two important parts of traction chains in SHEVs. In other major part of this work, four types of faults (stator winding inter turn short circuit, demagnetization, eccentricity ant bearing faults) of a PMSM have been studied. Inter turn short circuit of stator winding of PMSM in different speeds and loads has been considered to identify fault feature in all operation aspects, as it is expected by electric vehicle application. Experimental results aiming short circuits, bearing and eccentricity fault detection has been presented. Analytical and finite element method (FEM) aiming demagnetization fault investigation has been developed. The AC-DC converter switches are generally exposed to the possibility of outbreak open phase faults because of troubles of the switching devices. This work proposes a robust and efficient identification method for data acquisition selection aiming fault analysis and detection. Two new patterns under AC-DC converter failure are identified and presented. To achieve this goal, four different level of switches fault are considered on the basis of both simulation and experimental results. For accuracy needs of the identified pattern for SHEV application, several parameters have been considered namely: capacitor size changes, load and speed variations. On the basis of the developed fault sensitive models above, an ANN based fault detection, diagnosis strategy and the related algorithm have been developed to show the way of using the identified patterns in the supervision and the diagnosis of the PMSM drivetrain of SHEVs. ANN method have been used to develop three diagnosis based models for : the vector controlled PMSM under inter turn short circuit, the AC/DC power converter under an open phase fault and also the PMSM under unbalanced voltage caused by open phase DC/AC inverter. These models allow supervising the main components of the PMSM drivetrains used to propel the SHEV. The ANN advantages of ability to include a lot of data mad possible to classify the faults in terms of their type and severity. This allows estimating the performance degree of that drivetrains during faulty conditions through the parameter state of health (SOH). The latter can be used in a global control strategy of PMSM control in degraded mode in which the control is auto-adjusted when a defect occurs on the system. The goal is to ensure a continuity of service of the SHEV in faulty conditions to improve its reliability
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7

Wu, Long. "Separating Load Torque Oscillation and Rotor Faults in Stator Current Based-Induction Motor Condition Monitoring." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14545.

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Stator current spectral analysis techniques are usually used to detect rotor faults in induction machines. Magnetic field anomalies in the airgap due to the rotor faults result in characteristic side-band harmonic components in the stator current spectrum, which can be measured as rotor fault signatures. A position-varying load torque oscillation at multiples of the rotational speed, however, has exactly the same effect. Stator current harmonics due to a load torque oscillation often obscure and even overwhelm rotor eccentricity fault detection since the magnitude of load oscillation induced harmonics is usually much larger. Although previous research has suggested some methods to differentiate between these two effects, most of them rely heavily on the accurate estimation of motor parameters. The objective of this research is to develop a far more practical and computationally efficient method to detect rotor faults effectively in the presence of a load torque oscillation. A significant advantage of the proposed scheme is that it does not need any knowledge of motor parameters. The normalized negative sequence information induced by a mixed rotor eccentricity in the stator current or terminal voltage space vector spectra, serves as a reliable rotor fault indicator to eliminate load oscillation effects. Detailed airgap magnetic field analysis for an eccentric motor is performed and all machine inductance matrices as well as their derivatives are reformulated accordingly. Careful observation of these inductance matrices provides a fundamental understanding of motor operation characteristics under a fault condition. Simulation results based on both induction motor dynamic model and Maxwell 2D Finite Element Model demonstrate clearly the existence of the predicted rotor fault indicator. Extensive experimental results also validate the effectiveness and feasibility of the proposed detection scheme.
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8

Urresty, Betancourt Julio César. "Electrical and magnetic faults diagnosis in permanent magnet synchronous motors." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/101505.

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Permanent magnet synchronous motors (PMSMs) are an alternative in critical applications where high-speed operation, compactness and high efficiency are required. In these applications it is highly desired to dispose of an on-line, reliable and cost-effective fault diagnosis method. Fault prediction and diagnosis allows increasing electric machines performance and raising their lifespan, thus reducing maintenance costs, while ensuring optimum reliability, safe operation and timely maintenance. Consequently this thesis is dedicated to the diagnosis of magnetic and electrical faults in PMSMs. As a first step, the behavior of a healthy machine is studied, and with this aim a new 2D finite element method (FEM) modelbased system for analyzing surface-mounted PSMSs with skewed rotor magnets is proposed. It is based on generating a geometric equivalent non-skewed permanent magnet distribution which accounts for the skewed distribution of the practical rotor, thus avoiding 3D geometries and greatly reducing the computational burden of the problem. To diagnose demagnetization faults, this thesis proposes an on-line methodology based on monitoring the zero-sequence voltage component (ZSVC). Attributes of the proposed method include simplicity, very low computational burden and high sensibility when compared with the well known stator currents analysis method. A simple expression of the ZSVC is deduced, which can be used as a fault indicator parameter. Furthermore, mechanical effects arising from demagnetization faults are studied. These effects are analyzed by means of FEM simulations and experimental tests based on direct measurements of the shaft trajectory through self-mixing interferometry. For that purpose two perpendicular laser diodes are used to measure displacements in both X and Y axes. Laser measurements proved that demagnetization faults may induce a quantifiable deviation of the rotor trajectory. In the case of electrical faults, this thesis studies the effects of resistive unbalance and stator winding inter-turn short-circuits in PMSMs and compares two methods for detecting and discriminating both faults. These methods are based on monitoring and analyzing the third harmonic component of the stator currents and the first harmonic of the ZSVC. Finally, the Vold-Kalman filtering order tracking algorithm is introduced and applied to extract selected harmonics related to magnetic and electrical faults when the machine operates under variable speed and different load levels. Furthermore, different fault indicators are proposed and their behavior is validated by means of experimental data. Both simulation and experimental results show the potential of the proposed methods to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults.
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9

Leith, Douglas. "Expert systems in A.C. induction motor fault diagnosis." Thesis, Robert Gordon University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280372.

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10

Uliyar, Hithesh Sanjiva. "FAULT DIAGNOSIS OF VEHICULAR ELECTRIC POWER GENERATION AND STORAGE." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1284602099.

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11

Zhang, Guoguang. "Fault Estimation and Fault-tolerant Control for In-wheel Motor Electric Vehicles." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500421425793541.

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12

Alwodai, Ahmed. "Motor fault diagnosis using higher order statistical analysis of motor power supply parameters." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/26620/.

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Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics.
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13

Lawson, Shannon Edward. "Distributed reconfiguration and fault diagnosis in cellular processing arrays." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06302009-040317/.

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14

Hong, Mingguo. "Controllability and diagnosis in electric power systems /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/6088.

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15

Abdelhamid, F. S. aleh. "Detection and Diagnosis of Electrical Faults in Induction Motors using Instantaneous Phase Variation." Thesis, University of Manchester, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520502.

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16

Liu, Dong. "Analog and mixed-signal test and fault diagnosis." Ohio : Ohio University, 2003. http://www.ohiolink.edu/etd/view.cgi?ohiou1177701780.

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17

Cherubal, Sasikumar. "Fault isolation and diagnosis techniques for mixed-signal circuits." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/15450.

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18

Sangha, Mahavir Singh. "Intelligent fault diagnosis for automative engines and real data evaluation." Thesis, Liverpool John Moores University, 2008. http://researchonline.ljmu.ac.uk/5867/.

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19

Rittenhouse, Scott A. "Diagnosis of operational changes in microelectromechanical systems via fault detection." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3632.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains v, 141 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 137-141).
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20

Wang, Rongrong. "Fault-Tolerant Control and Fault-Diagnosis Design for Over-Actuated Systems with Applications to Electric Ground Vehicles." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365522537.

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21

Kar, Tapas Ranjan. "Fuzzy logic-based fault diagnosis for mining equipment failures." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/43090.

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Equipment availability is the most significant factor in the productivity of many mines and processing plants. Machine breakdowns are not only expensive in terms of production losses but also important in meeting production schedules. In a complex piece of machinery like a shearer or a powered support system in a highly automated longwall face, such breakdowns can be due to one of the large number of possible faults. A large proportion, up to 80% of the down time is spent in locating the fault. For this reason, a need for an automated diagnostic method to assist the operator in the diagnosis process is felt. In this study, a diagnostic system is developed by modeling the partially known or imprecise relations and poorly defined variables found in a diagnostic environment. Logic of fuzzy sets and systems theory finds an interesting application in this area. This study presents a diagnostic algorithm, which relates the possible causes of failure to their respective symptoms through fuzzy logic paths. Applications of the diagnostic method are illustrated through examples of a compressor and a shearer.
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22

Wang, Peng. "Intelligent signal/image processing for fault diagnosis and prognosis." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/13308.

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23

Li, Tianpei. "Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587583790925718.

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24

Chan, Si Leong. "Novel Fuzzy-Neural Networks technology for diagnosing internal fault of three-phase squirrel cage induction motor." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1807118.

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25

Adewole, Adeyemi Charles. "Investigation of methodologies for fault detection and diagnosis in electric power system protection." Thesis, Cape Peninsula University of Technology, 2012. http://hdl.handle.net/20.500.11838/1273.

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Thesis Submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology, 2012
The widespread deregulation and restructuring of electric power utilities throughout the world and the surge in competition amongst utility companies has brought about the desire for improved economic efficiency of electric utilities and the provision of better service to energy consumers. These end users are usually connected to the distribution network. Thus, there is a growing research interest in distribution network fault detection and diagnosis algorithms for reducing the down-time due to faults. This is done so as to improve the reliability indices of utility companies and enhance the availability of power supply to customers. The application of signal processing and computational intelligence techniques in power systems protection, automation, and control cannot be overemphasized. This research work focuses on power system distribution network and is aimed at the development of versatile algorithms capable of accurate fault detection and diagnosis of all fault types for operation in balanced/unbalanced distribution networks, under varying fault resistances, fault inception angles, load angles, and system operating conditions. Therefore, different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, capacitor switching, and load switching were applied to the IEEE 34 Node Test Feeder in order to generate the data needed. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. The length of the feeder was also extended and investigations carried out. This was implemented by modelling the IEEE 34-node benchmark test feeder in DIgSILENT PowerFactory (DPF). In the course of this research, a hybrid combination of Discrete Wavelet Transform (DWT), decision-taking rule-based algorithms, and Artificial Neural Networks (ANNs) algorithms for electric power distribution network fault detection and diagnosis was developed. The integrated algorithms were capable of fault detection, fault type classification, identification of the faulty line segment, and fault location respectively. Several scenarios were simulated in the test feeder. The resulting waveforms were exported as ASCII or COMTRADE files to MATLAB for DWT signal processing. Experiments with various DWT mother wavelets were carried out on the waveforms obtained from the simulations. In particular, Daubechies db-2, db-3, db-4, db-5, and db-8 were considered. Others are Coiflet-3 and Symlet-4 mother wavelets respectively. The energy and entropy of the detail coefficients for each decomposition level based on a sampling frequency of 7.68 kHz were analysed. The best decomposition level for the diagnostic tasks was then selected based on the analysis of the wavelet energies and entropy in each level of decomposition. Consequently, level-1 db-4 detail coefficients were selected for the fault detection task, while level-5 db4 detail coefficients were used to compute the wavelet entropy per unit indices which were then used for fault classification, fault section identification, and fault location tasks respectively. Decision-taking rule-based algorithms were used for the fault detection and fault classification tasks respectively. The fault detection task verifies if a fault did indeed occur or not, while the fault classification task determines the fault class and the faulted phase(s). Similarly, Artificial Neural Networks (ANNs) were used for the fault section identification and fault location tasks respectively. For the fault section identification task, the ANNs were trained for pattern classification to identify the lateral or segment affected by the fault. Conversely, the fault location ANNs were trained for function approximation to predict the location of the fault from the substation in kilometres. Also, the IEEE 13 Node Benchmark Test Feeder was modelled in RSCAD software and batch mode simulations were carried out using the Real-Time Digital Simulator (RTDS) as a ‘proof of concept’ for the proposed method, in order to demonstrate the scalability, and to further validate the developed algorithms. The COMTRADE files of disturbance records retrieved from an external IED connected in closed-loop with the RTDS and the runtime simulation waveforms were used as test inputs to the developed Hybrid Fault Detection and Diagnosis (HFDD) method. Comparison of the method based on entropy with statistical methods based on standard deviation and Mean Absolute Deviation (MAD) has shown that the method based on entropy is very reliable, accurate, and robust. Results of preliminary studies carried out showed that the proposed HFDD method can be applied to any power system network irrespective of changes in the operating characteristics. However, certain decision indices would change and the decision-taking rules and ANN algorithms would need to be updated. The HFDD method is promising and would serve as a useful decision support tool for system operators and engineers to aid them in fault diagnosis thereby helping to reduce system down-time and improve the reliability and availability of electric power supply. Key words: Artificial neural network, discrete wavelet transform, distribution network, fault simulation, fault detection and diagnosis, power system protection, RTDS.
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Bai, Hao. "A generic fault detection and diagnosis approach for pneumatic and electric driven railway assets." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1202/.

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The railway assets studied in this project, are those widely distributed pieces of equipment that are critical to the dependable operation of the railway system. A failed asset is likely to cause significant delay to rail services, and may even place the system into an unsafe state. A generic fault detection and diagnosis (FDD) solution for a number of railway assets of different types is therefore desired. In this thesis, five assets, namely the pneumatic train door, point machine and train-stop, the electric point machine and the electro-hydraulic level crossing barrier, are considered as case studies. Based on their common dynamic characteristics, these assets are also known as Single Throw Mechanical Equipments (STMEs). A generic FDD method is proposed for these STMEs, which consists of sensor inputs and pre-processing, fault detection processes and fault diagnosis processes. A generic model, composed of a series of sub-models, is constructed to describe the behaviour of each asset. The results of fault detection approaches indicate that the proposed method has good performance and is generically applicable to the five assets. Two fault diagnosis methods using fault model and residual analysis are proposed and the fault model based fault diagnosis is preliminarily approached. Finally, a new three level architecture for railway condition monitoring is discussed for practical applications.
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Zhong, Jian Hua. "Intelligent system based facility monitoring and fault diagnosis of power generators." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2550655.

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28

Kruckenberg, John. "Fault Diagnosis and Hardware in the Loop Simulation for the EcoCAR Project." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306180272.

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29

Wanner, Daniel, Trigell Annika Stensson, Lars Drugge, and Jenny Jerrelind. "Survey on fault-tolerant vehicle design." KTH, Farkost och flyg, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98811.

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Fault-tolerant vehicle design is an emerging inter-disciplinary research domain, which is of increasedimportance due to the electrification of automotive systems. The goal of fault-tolerant systems is to handleoccuring faults under operational condition and enable the driver to get to a safe stop. This paperpresents results from an extended survey on fault-tolerant vehicle design. It aims to provide a holisticview on the fault-tolerant aspects of a vehicular system. An overview of fault-tolerant systems in generaland their design premises is given as well as the specific aspects related to automotive applications. Thepaper highlights recent and prospective development of vehicle motion control with integrated chassiscontrol and passive and active fault-tolerant control. Also, fault detection and diagnosis methods arebriefly described. The shift on control level of vehicles will be accompanied by basic structural changeswithin the network architecture. Control architecture as well as communication protocols and topologiesare adapted to comply with the electrified automotive systems. Finally, the role of regulations andinternational standardization to enable fault-tolerant vehicle design is taken into consideration.

Qc 20120730

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30

Cheang, Tak Son. "Math-model based machinery and AI-based diagnostic technologies for detecting and locating the inner-faults of three-phase squirrel-cage induction motors." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2148534.

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31

Sprooten, Jonathan. "Finite element and electrical circuit modelling of faulty induction machines: Study of internal effects and fault detection techniques." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210674.

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This work is dedicated to faulty induction motors. These motors are often used in industrial applications thanks to their usability and their robustness. However, nowadays optimisation of production becomes so critical that the conceptual reliability of the motor is not sufficient anymore. Motor condition monitoring is expanding to serve maintenance planning and uptime maximisation. Moreover, the use of drive control sensors (namely stator current and voltage) can avoid the installation and maintenance of dedicated sensors for condition monitoring.

Many authors are working in this field but few approach the diagnosis from a detailed and clear physical understanding of the localised phenomena linked to the faults. Broken bars are known to modulate stator currents but it is shown in this work that it also changes machine saturation level in the neighbourhood of the bar. Furthermore, depending on the voltage level, this change in local saturation affects the amplitude and the phase of the modulation. This is of major importance as most diagnosis techniques use this feature to detect and quantify broken bars. For stator short-circuits, a high current is flowing in the short-circuited coil due to mutual coupling with the other windings and current spikes are flowing in the rotor bars as they pass in front of the short-circuited conductors. In the case of rotor eccentricities, the number of pole-pairs and the connection of these pole-pairs greatly affect the airgap flux density distribution as well as the repartition of the line currents in the different pole-pairs.

These conclusions are obtained through the use of time-stepping finite element models of the faulty motors. Moreover, circuit models of faulty machines are built based on the conclusions of previously explained fault analysis and on classical Park models. A common mathematical description is used which allows objective comparison of the models for representation of the machine behaviour and computing time.

The identifiability of the parameters of the models as well as methods for their identification are studied. Focus is set on the representation of the machine behaviour using these parameters more than the precise identification of the parameters. It is shown that some classical parameters can not be uniquely identified using only stator measurements.

Fault detection and identification using computationally cheap models are compared to advanced detection through motor stator current spectral analysis. This last approach allows faster detection and identification of the fault but leads to incorrect conclusions in low load conditions, in transient situations or in perturbed environments (i.e. fluctuating load torque and unideal supply). Efficient quantification of the fault can be obtained using detection techniques based on the comparison of the process to a model.

Finally, the work provides guidelines for motor supervision strategies depending on the context of motor utilisation.
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32

Lin, Brian K. "An unsupervised neural network fault discriminating system implementation for on-line condition monitoring and diagnostics of induction machines." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14957.

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33

Awadallah, Mohamed Abdel-Azim Mohamed. "Automatic fault diagnosis and location in CSI-fed brushless DC motor drives using Neuro-Fuzzy Systems /." Search for this dissertation online, 2004. http://wwwlib.umi.com/cr/ksu/main.

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34

Galvez, Carrillo Manuel Ricardo. "Sensor fault diagnosis for wind-driven doubly-fed induction generators." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209982.

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Among the renewable energies, wind energy presents the highest growth in installed capacity and penetration in modern power systems. This is why reliability of wind turbines becomes an important topic in research and industry. To this end, condition monitoring (or health monitoring) systems are needed for wind turbines. The core of any condition monitoring system (CMS) are fault diagnosis algorithms whose task is to provide early warnings upon the occurrence of incipient (small magnitude) faults. Thanks to the use of CMS we can avoid premature breakdowns and reduce significatively maintenance costs.

The present thesis deals with fault diagnosis in sensors of a doubly-fed induction generator (DFIG) for wind turbine (WT) applications. In particular we are interested in performing fault detection and isolation (FDI) of incipient faults affecting the measurements of the three-phase signals (currents and voltages) in a controlled DFIG. Although different authors have dealt with FDI for sensors in induction machines and in DFIGs, most of them rely on the machine model with

constant parameters. However, the parameter uncertainties due to changes in the operating conditions will produce degradation in the performance of such FDI systems.

In this work we propose a systematic methodology for the design of sensor FDI systems with the following characteristics: i) capable of detecting and isolating incipient additive (bias, drifts) and multiplicative (changes in the sensor

gain) faults, ii) robust against changes in the references/disturbances affecting the controlled DFIG as well as modelling/parametric uncertainties, iii) residual generation system based on a multi-observer strategy to enhance the isolation process, iv) decision system based on statistical-change detection algorithms to treat the entire residual and perform fault detection and isolation at once.

Three novel sensor FDI approaches are proposed. The first is a signal-based approach, that uses the model of the balanced three-phase signals (currents or voltages) for residual generation purposes. The second is a model-based approach

that accounts for variation in the parameters. Finally, a third approach that combines the benefits of both the signal- and the model-based approaches is proposed. The designed sensor FDI systems have been validated using measured voltages, as well as simulated data from a controlled DFIG and a speed-controlled induction

motor.

In addition, in this work we propose a discrete-time multiple input multiple output (MIMO) regulator for each power converter, namely for the rotor side converter (RSC) and for the grid side converter (GSC). In particular, for RSC

control, we propose a modified feedback linearization technique to obtain a linear time invariant (LTI) model dynamics for the compensated DFIG. The novelty of this approach is that the compensation does not depend on highly uncertain parameters such as the rotor resistance. For GSC control, a LTI model dynamics

is derived using the ideas behind feedback linearization. The obtained LTI model dynamics are used to design Linear Quadratic Gaussian (LQG) regulators. A single design is needed for all the possible operating conditions.
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35

Salatiel, Paulo César Mendes. "Técnicas de diagnóstico de avarias em motores de indução baseadas no quadro da corrente elétrica estatórica." Master's thesis, Instituto Politécnico de Setúbal. Escola Superior de Tecnologia de Setúbal, 2019. http://hdl.handle.net/10400.26/31369.

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Dissertação submetida como requisito parcial para obtenção do grau de Mestre em Engenharia Eletrotécnica e Computadores
Os variados setores da indústria, tendo por objetivo otimizar os processos de produção e evitar custos substanciais devido a perdas de produção não programadas, têm exigido que os setores relacionados com a manutenção dos equipamentos adotem medidas que estão inseridas no âmbito da manutenção preventiva. O Motor de Indução Trifásico representa de momento a máquina elétrica rotativa mais utilizada nos setores industriais. Deste modo, existe a necessidade de adotar sistemas de detecção e diagnóstico de avarias para este tipo de máquinas. O trabalho que irá ser desenvolvido nesta dissertação apresenta, como objetivo principal, propor técnicas de diagnóstico de avarias que têm por base o uso do quadrado do sinal da corrente elétrica estatórica. Através das diversas técnicas apresentadas será efetuado um estudo pormenorizado para se concluir, por um lado, qual a técnica que apresenta um conteúdo espectral com maior informação e, por outro, estabelecer o conceito de índice de severidade e ainda concluir qual técnica que apresenta a melhor relação entre o valor percentual da carga e este índice. Para a implementação experimental deste trabalho, foi desenvolvido um sistema de aquisição de sinais, assim como, foram modificados fisicamente motores, de modo a realizar ensaios em vazio e em carga que se aproximassem das situações de avaria real.
Currently, the various sectors of industry to optimize production processes and avoid substantial costs to companies due to unscheduled production losses have required equipment maintenance sectors to adopt measures that fall within the scope of preventive maintenance. The Three-Phase Induction Motor currently represents the most widely used rotary electric machine in the industrial sectors. Thus, there is a need to adopt fault detection and diagnosis systems for this type of machines. The work that will be developed in this dissertation presents as main objective, to propose fault diagnosis techniques based on the use of the signal square. of the statistical electric current. Through the various techniques presented, a detailed study will be carried out to conclude, on the one hand, which technique has a spectral content with more information, on the other hand, to establish the concept of severity index and to conclude which technique has the best relation between the percentage value of the load and this index. For the implementation of this work, a signal acquisition system was developed, as well as motors that were physically modified to carry out electrical tests under load and nozzle that approached the actual fault situation.
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36

Meng, Jianwen. "Battery fault diagnosis and energy management for embedded applications." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST003.

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Les véhicules électriques (VEs) connaissent un développement en plein essor ces dernières années pour faire face aux problèmes environnementaux et aux dérèglements climatiques. Du point de vue du stockage de l'énergie, c'est essentiellement la technologie des batteries lithium-ion (LIB) qui est la plus utilisée pour d'alimentation des véhicules électriques compte tenu de leur haute densité d'énergie / puissance et de leur longue durée de vie. La fiabilité des LIBs est sans aucun doute d'une importance fondamentale pour le développement des VE. Dans cet objectif, les travaux de thèse s'inscrivent dans le développement des algorithmes dédiés à l'estimations des états de la batterie ainsi qu'au diagnostic de court-circuit naissant. L'estimation des états de la batterie, qui peut également être qualifiée de surveillance de la batterie, est un élément indispensable de la stratégie de gestion de l'énergie d'un véhicule électrique ou hybride. Par ailleurs, le vieillissement prématuré peut être évité grâce à la surveillance des états de batterie telles que l'état de charge (SOC) et l'état de santé (SOH). De plus, étant donné que l'emballement thermique (TR) peut être la conséquence d'un défaut de court-circuit (SC) électrique, de ce fait, une détection efficace de SC naissant de la batterie peut donc donner une alerte protectrice de TR. La principale contribution de cette thèse réside dans les aspects théoriques et méthodologiques dans le domaine de la surveillance de la batterie et du diagnostic SC naissant
In order to cope with environmental problems and climate change, electric vehicles (EVs) gain the ever booming development in recent years. From the point of view of energy storage, because of their high energy / power density and their extended lifespan, it is essentially the lithium-ion battery (LIB) technology which is the most used power unit for EVs. Doubtlessly, the reliability of LIBs is of vital importance for the development of EVs. To this end, this thesis is dedicated to the algorithmic development of battery state and parameter estimation as well as incipient short-circuit diagnosis. The battery state and parameter estimation, which can also be termed as battery monitoring, is a critical part in the so-called health conscious energy management strategy for electric or hybrid electric vehicle. Premature aging can be avoided through the accurate battery state estimation such as state of charge (SOC) and state of health (SOH). Furthermore, as the thermal runaway (TR) can be ultimately attributed to short-circuit (SC) electrical abuse, therefore, effective battery incipient SC detection can give an early warning of TR. The main contribution of this thesis lies in the theoretical and methodological aspects in the domain of battery monitoring and incipient SC diagnosis
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Huang, Jian, Toshio Fukuda, 敏男 福田, and Takayuki Matsuno. "Model-Based Intelligent Fault Detection and Diagnosis for Mating Electric Connectors in Robotic Wiring Harness Assembly Systems." IEEE, 2008. http://hdl.handle.net/2237/11173.

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38

Ge, Yuxue. "Energy Management in More Electric Aircraft through PMSM Fault Diagnosis, Adaptive Load Shedding and Efficient Aircraft Design." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/287798/5/contratYG.pdf.

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More electric aircraft is an electrification scheme of aircraft system with high technical feasibility and good economy. It can reduce the weight of aircraft structure, improve maintenance efficiency and reduce fire hazards. However, the electrification of aircraft system will drastically increase the proportion of electrical equipment, the total power demand and the difficulty of fault diagnosis. This paper uses the energy management method to take up the challenge, with focus on fault diagnosis of permanent-magnet synchronous machines (PMSMs), adaptive load shedding and energy efficient aircraft design. A literature review of the concept evolution from all/more-electric aircraft to energy-optimized aircraft is presented. The main issues of the aircraft electrification process are summarized, and followed by an introduction to the current research and methods. The model of the aircraft electrical system is qualitatively and mathematically recalled, including the generator, the battery, the DC motor, the AC motor, and the electric power converter. The accuracy and computation cost of the aircraft model depends on the complexity of the subsystem models that are involved. Therefore, the level of detail that is necessary for a good precision-versus-simulation-time ratio is discussed by taking the electric system of an industrial level hybrid energy quadcoptor UAV as an example. The analysis shows that the bi-directional instruments, i.e. the electric machine, should be modeled in details while other components can be simplified. PMSMs are a group of on-board electric machines with promising future prospects because of high power density and stability. The model of PMSMs is further developed in this work, especially in the inter-turn and phase-to-phase short-circuit conditions. In case of inter-turn short-circuit fault, a winding-function-based and a fault-current-based model are separately developed. The accuracy of both models are verified and compared through experimental results. The fault-current-based modeling method is applied to the phase-to-phase short-circuit fault and experimentally examined and discussed. General condition monitoring methods require the use of a large number of sensors. A fault detection and isolation method that can have low requirement of sensor is recalled and inherited. The description of the fault phase identification index using this method is relatively imprecise, which is not applicable to the inter-turn short-circuit fault. In this work, the analytical expression of the faulty phase identification index is derived based on the fault models. A method to isolate inter-turn and phase-to-phase short-circuit faults is proposed by a combination of the current- and the voltage-signature residuals. This development expands the application scope of the original fault detection and isolation tool and improves its accuracy. The validity of this fault diagnosis method has been verified by experimental results.Load management is developed to guarantee the normal operation of critical loads by shedding some other loads in case of emergency. Generally, binary decisions are made: either something has gone wrong or everything is fine. However, different types of fault influence the working performance of the load and the entire network in different ways. There are multiple states between totally wrong and pure fine, and the load management decision should be adaptive to each state. In this work, fuzzy logic method is used to degrade the load priority according to the instantaneous working state. Combining it with the fault detection and isolation process, a fault-tolerant adaptive load management is achieved. Finally, this work discusses the aircraft design from the energy management point of view, which consists of the energy efficiency analysis and the multidisciplinary energy efficient design of the integrated aircraft system. The first thermodynamic efficiency has been widely used as a common parameter for depicting the energy utilization, i.e. the ratio of output to input power of the system. However, it ignores the irreversible increase of the entropy and cannot reveal the upper limit of the available work of the system.Based on the second thermodynamic law, this work uses the exergy parameters to analyze the energy utilization of a MEA design scheme. Based on the exergy analysis, an energy-efficient aircraft design method is proposed by optimizing the exergy lost of the whole design. The method could provide a global optimization reference for the integrated aircraft design of a MEA.
Doctorat en Sciences de l'ingénieur et technologie
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39

Rostaing, Gilles. "DIAGNOSTIC DE DÉFAUT DANS LES ENTRAINEMENTS ÉLECTRIQUES." Phd thesis, Grenoble INPG, 1997. http://tel.archives-ouvertes.fr/tel-00909645.

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Cette thèse représente une contribution aux études sur la disponiblité des dispositifs électrotechnique. L'étude présentée vise à définir la méthode de redondance analytique, basée sur l'estimation d'état, la mieux adaptée au diagnostic des entraînements électriques en considérant les défauts de l'ensemble du convertisseur, de la commande et des capteurs. La méthode retenue doit permettre d'obtenir un modèle de diagnostic implantable en temps réel et sans ajout de capteurs supplémentaires. L'application retenue est un entraînement à courant continu commandé en couple. Le chapitre II compare deux modèles analytiques nommés modèles parallèle et permet de retenir un modèle parallèle "découplé" qui permet une bonne détection et une bonne localisation des défauts d'électronique de puissance ainsi que des défauts capteur. Malheureusement les modèles parallèles sont dépendants des entrées perturbatrices du procédé. Les perturbations génèrent donc des fausses alarmes La batterie d'observateur à entrées inconnues mise au point au chapitre III permet de s'affranchir de l'entrée perturbatrice que constitue dans notre cas le couple de charge. Cette technique est moins dépendante, en terme de découplage, du système car l'injection de sortie grace à la matrice de gain permet de disposer de degrés de liberté supplémentaires qui autorisent un réglage des découplages et des sensibilités. Les observateurs (à entrées inconnues) sont donc, à priori, les modèles de diagnostic les mieux adaptés à la à la détection et la localisation de défauts dans les entrainements électriques à courant continu.
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40

Hashemi, Seyed Reza. "An Intelligent Battery Managment System For Electric And Hybrid Electric Aircraft." University of Akron / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=akron1615732366021405.

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41

Zhou, Wei. "Incipient Bearing Fault Detection for Electric Machines Using Stator Current Noise Cancellation." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19706.

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The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.
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Farfan-Ramos, Luis. "Real-time Fault Diagnosis of Automotive Electrical Power Generation and Storage System." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1303129393.

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43

Carbone, Marc A. Carbone. "Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601984256665471.

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44

Hayakawa, Yoshikazu, and Akira Ito. "Design of Fault Tolerant Control System for Electric Vehicles with Steer-By-Wire and In-Wheel Motors." International Federation of Automatic Control (IFAC), 2013. http://hdl.handle.net/2237/20771.

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45

Meinguet, Fabien. "Fault-tolerant permanent-magnet synchronous machine drives: fault detection and isolation, control reconfiguration and design considerations." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209757.

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The need for efficiency, reliability and continuous operation has lead over the years to the development of fault-tolerant electrical drives for various industrial purposes and for transport applications. Permanent-magnet synchronous machines have also been gaining interest due to their high torque-to-mass ratio and high efficiency, which make them a very good candidate to reduce the weight and volume of the equipment.

In this work, a multidisciplinary approach for the design of fault-tolerant permanent-magnet synchronous machine drives is presented.

The drive components are described, including the electrical machine, the IGBT-based two-level inverter, the capacitors, the sensors, the controller, the electrical source and interfaces. A literature review of the failure mechanisms and of the reliability model of most of these components is performed. This allows understanding how to take benefit of the redundancy generally introduced in fault-tolerant systems.

A necessary step towards fault tolerance is the modelling of the electrical drive, both in healthy and faulty operations. A general model of multi-phase machines (with a number of phases equal to or larger than three) and associated converters is proposed. Next, control algorithms for multi-phase machines are derived. The impact of a closed-loop controller upon the occurrence of faults is also examined through simulation analysis and verified by experimental results.

Condition monitoring of electrical machines has expanded these last decades. New techniques relying on various measurements have emerged, which allow a better planning of maintenance operations and an optimization of the uptime of electrical machines. Regarding drives, a number of sensors are inherently present for control and basic protection functions. The utilization of these sensors for advanced condition monitoring is thus particularly interesting since they are available at no cost.

A novel fault detection and isolation scheme based on the available measurements (phase currents, DC-link voltage and mechanical position) is developed and validated experimentally. Change-detection algorithms are used for this purpose. Special attention is paid to sensor faults as well, what avoids diagnosis errors.

Fault-tolerant control can be implemented with passive and active approaches. The former consists in deriving a control scheme that gives acceptable performance for all operating conditions, including faulty conditions. The latter consists in applying dedicated solutions upon the occurrence of faults, i.e. by reconfiguring the control. Both approaches are investigated and implemented.

Finally, design considerations are discussed throughout the thesis. The advantages and drawbacks of various topologies are analyzed, which eventually leads to the design of a five-phase fault-tolerant permanent-magnet synchronous machine.
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46

Bradley, William J. "Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.
The full-text was made available at the end of the embargo period on 29th Sept 2017.
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47

Bradley, William John. "Current based fault detection and diagnosis of induction motors : adaptive mixed-residual approach for fault detection and diagnosis of rotor, stator, bearing and air-gap faults in induction motors using a fuzzy logic classifier with voltage and current measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.
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48

Marques, Miguel Alexandre Castanheira. "On-line system for faults detection in induction motors based on PCA." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8578.

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Dissertation to obtain the degree of Master in Electrical and Computer Engineering
Nowadays in the industry there many processes where human intervention is replaced by electrical machines, especially induction machines due to his robustness, performance and low cost. Although, induction machines are a high reliable device, they are also susceptible to faults. Therefore, the study of induction machine state is essential to reduce human and financial costs. The faults in induction machines can be divided mainly into two types: electrical faults and mechanical faults. Electrical faults represent between 40% and 50% of the reported faults and can be divided essentially in 2 types: stator unbalances and broken rotor bars. Taking into account the high dependency of induction machines and the massive use of automatic processes the industrial level, it is necessary to have diagnostic and monitoring systems these machines. It is presented in this work an on-line system for detection and diagnosis of electrical faults in induction motors based on computer-aided monitoring of the supply currents. The main objective is to detect and identify the presence of broken rotor bars and stator short-circuits in the induction motor. The presence of faults in the machine causes different disturbances in the supply currents. Through a stationary reference frame, such as αβ transform it is possible to extract and manipulate the results obtained from the supply currents using Eigen decomposition.
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Sarikhani, Ali. "Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/766.

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Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.
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50

Blödt, Martin. "Condition Monitoring of Mechanical Faults in Variable Speed Induction Motor Drives - Application of Stator Current Time-FrequencyAnalysis and Parameter Estimation." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2006. http://tel.archives-ouvertes.fr/tel-00105482.

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Abstract:
Ce travail de thèse traite de la détection et du diagnostic de défaillances mécaniques par analyse du courant statorique dans les entraînements électriques à base de machine asynchrone. Deux effets d'un défaut mécanique, des oscillations de couple et une excentricité d'entrefer, sont supposés. La modélisation par approche des ondes de forces magnétomotrices et de perméance conduit à deux modèles analytiques du signal courant. La conséquence des défauts est soit une modulation de phase, soit une modulation d'amplitude du signal courant statorique. Ces phénomènes sont détectés par une analyse spectrale en régime permanent, ou des méthodes temps fréquence en régime transitoire. Les méthodes étudiées sont la fréquence instantanée, le spectrogramme et la représentation de Wigner-Ville. L'estimation paramétrique d'indices de modulation a également été traitée. Des résultats de simulation et expérimentaux permettent de valider les signatures et d'extraire de façon automatique des indicateurs de défaut. De plus, une méthode permettant la distinction des oscillations de couple d'une excentricité dynamique est proposée. L'étude est complétée par une implémentation sur DSP des méthodes temps-fréquence afin de démontrer la faisabilité d'une surveillance en ligne.
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