Relevant bibliographies by topics / Motor fault analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Motor fault analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Motor fault analysis"

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Wahab, Abbas A., N. Fatimah Abdullah, and M. A. H. Rasid. "Mechanical Fault Detection on Electrical Machine: Thermal Analysis of Small Brushed DC Motor with Faulty Bearing." MATEC Web of Conferences 225 (2018): 05012. http://dx.doi.org/10.1051/matecconf/201822505012.

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Direct current motors (DC motor) are used in the small electric devices commonly. DC motor are cheap and easy to install, thus their popularity. Despite the popularity, faults occur which make diagnosis and detection of faults very important. It avoids financial loss and unexpected shutdown operation causes by these faults. This paper presents an analysis of temperature profile of the much famous small Brushed DC motor with a faulty bearing. The temperature data of healthy DC motor and DC motor with faulty bearing were measured by thermocouple and recorded using data logger in real time until steady state temperature, under different load. The analysis on the steady state temperature allow to conclude that bearing fault can clearly be recognised through characteristics temperature difference with a healthy motor.
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Amanuel, Thomas, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, and Weldekidan Bahlibi. "Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor." March 2021 3, no. 1 (2021): 61–76. http://dx.doi.org/10.36548/jei.2021.1.006.

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Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.
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Glowacz, A., W. Glowacz, Z. Glowacz, et al. "Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers." Archives of Metallurgy and Materials 62, no. 4 (2017): 2413–19. http://dx.doi.org/10.1515/amm-2017-0355.

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AbstractA degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.
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Kudelina, Karolina, Bilal Asad, Toomas Vaimann, et al. "Bearing Fault Analysis of BLDC Motor for Electric Scooter Application." Designs 4, no. 4 (2020): 42. http://dx.doi.org/10.3390/designs4040042.

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In this paper, the bearing faults analysis of the brushless DC motor is presented. The research method is based on the analysis of the vibration signal of healthy as well as faulty bearings by the identification of specific frequencies on the vibration spectrum. For the experiment, the most common faults were inflicted on the bearings. As the used motor is intended for electric scooter applications, seven different damages were chosen, which are highly likely to occur during the scooter operation. The main bearing faults and the possibility of fault monitoring are addressed. The vibration data are gathered by the acceleration sensors placed on the motor at different locations and the spectrum analysis is performed using the fast Fourier transform. The variation in the amplitude of the frequency harmonics particularly the fundamental component is presented as a fault indicator.
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Farag, Khaled, Abdullah Shawier, Ayman S. Abdel-Khalik, Mohamed M. Ahmed, and Shehab Ahmed. "Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor." Energies 14, no. 18 (2021): 5905. http://dx.doi.org/10.3390/en14185905.

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The multiphase induction motor is considered to be the promising alternative to the conventional three-phase induction motor, especially in safety-critical applications because of its inherent fault-tolerant feature. Therefore, the attention of many researchers has been paid to develop different techniques for detecting various fault types of multiphase induction motors, to securely switch the control mode of the multiphase drive system to its post-fault operation mode. Therefore, several fault detection methods have been researched and adapted; one of these methods is the indices-based fault detection technique. This technique was firstly introduced to detect open-phase fault of multiphase induction motors. The main advantage of this technique is that its mathematical formulation is straightforward and can easily be understood and implemented. In this paper, the study of the indices-based fault detection technique has been extended to test its applicability in detecting some other stator and rotor fault types of multiphase induction motors, namely, open-phase, open-switch, bad connection and broken rotor bar faults. Experimental and simulation validations of this technique are also introduced using a 1 kW prototype symmetrical six-phase induction motor.
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Ribeiro Junior, Ronny Francis, Isac Antônio dos Santos Areias, and Guilherme Ferreira Gomes. "Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types." Sensor Review 41, no. 3 (2021): 311–19. http://dx.doi.org/10.1108/sr-02-2021-0052.

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Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.
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Vinothraj, C., N. Praveen Kumar, and T. B. Isha. "Bearing Fault Analysis in Induction Motor Drives Using Finite Element Method." International Journal of Engineering & Technology 7, no. 3.6 (2018): 30. http://dx.doi.org/10.14419/ijet.v7i3.6.14928.

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Diagnosis of faults in induction motor is an indispensable process in industries to improve the reliability of the machine and reduce the financial loss. Among the various faults occurring in induction motors (IM), bearing fault is the predominant one which covers nearly 60% of faults. In this paper, a study of the electromagnetic field of an induction motor with bearing fault fed from both the mains and a three phase voltage source PWM inverter in open loop is carried out using Finite element method (FEM). Electromagnetic field parameters like flux lines distribution, flux density distribution and radial air gapflux density are analyzed. The presence of bearing fault can be detected from the spatial FFT spectrum of radial air gap flux density. From the FFT spectrum, it is seen that the amplitude of fundamental component of radial air gap flux density decreases and those around 100 mm distance increases with the severity of fault.
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Alawady, A. A., M. F. M. Yousof, N. Azis, and M. A. Talib. "Frequency response analysis technique for induction motor short circuit faults detection." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 3 (2020): 1653. http://dx.doi.org/10.11591/ijpeds.v11.i3.pp1653-1659.

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<p>The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase induction motors (TPIM), were analysed according to two status: healthy induction motor at normal winding status and same motor with windings shorted of main windings. A conclusion of this paper provides the interpretation of and validation the FRA response due to internal SC fault case by using NCEPRI algorithm, which is considered as one of certified statistical indicators. The proposed method in this paper had a useful result for detect and diagnosis of stator windings faults of TPIM. The applications of developed method can be used to detece the other machines types faults.</p>
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Treetrong, Juggrapong. "Fault Prediction of Induction Motor Based on Time-Frequency Analysis." Applied Mechanics and Materials 52-54 (March 2011): 115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.115.

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Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault severity levels seem to not able to analyze. Hence the time-frequency Analysis (or spectrogram) of the stator phase currents is proposed here. The method is expected to show relation between the phase current signals and the fault levels which make it can detect the faults and also indicate the fault levels. The experiments show that the proposed method can provide good accuracy for fault prediction and fault level quantification. Hence it can conclude that the propose method can be an effective tool for motor fault prediction.
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Tang, Jing, Yongheng Yang, Jie Chen, Ruichang Qiu, and Zhigang Liu. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection." Energies 13, no. 1 (2019): 101. http://dx.doi.org/10.3390/en13010101.

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Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.
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Dissertations / Theses on the topic "Motor fault analysis"

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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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Abed, Wathiq. "Robust fault analysis for permanent magnet DC motor in safety critical applications." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3550.

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Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.
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Kozovský, Matúš. "Modelování a řízení střídavých elektrických pohonů při poruše." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-437978.

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Dizertační práce se zabývá modelováním a řízením elektrických pohonů během poruchových stavů. Práce se obzvláště zaměřuje na více-fázové motory. První část práce se zabývá matematickými rovnicemi obecného více-fázového motoru a následným odvozením n-krát troj-fázového zapojení motoru. Modely v dq souřadnicovém systému a modely ve statorových souřadnicích jsou navrženy pro simulaci chování motoru během poruchových stavů. Další část práce se zabývá analýzou poruch ve více-fázových motorech s využitím matematických modelů. Různé vnitřní struktury vinutí motoru jsou analyzovány z pohledu možného řízení během poruchového stavu. Taktéž je prezentováno chování těchto různých struktur motoru během poruchových stavů. Předmětem analýzy jsou elektrické poruchy vinutí motoru a elektrické poruchy výkonové elektroniky. Poslední část práce se zabývá testováním navrženého řídícího algoritmu a navržených kompenzačních strategií pro poruchy na reálných motorech. Pro testování byl použit segregovaný dvakrát troj-fázový motor a experimentální motor s odbočkami pro emulaci poruch vinutí. Provedené testy prokázaly, že vhodně navrhnutý motor v kombinaci se správným řídícím algoritmem a výkonovou elektronikou dokáže zaručit kontinuální běh pohonu i během poruchy.
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Gao, Feng. "Interior Permanent Magnet Synchronous Motor Demagnetization Fault Modeling and Analysis by Using Dynamic Phasors Model." Thesis, North Dakota State University, 2014. https://hdl.handle.net/10365/27473.

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Interior permanent magnet synchronous motor (IPMSM) has been widely used in hybrid electric vehicles (HEVs) since the high power density and efficiency. However, the primary drawback of IPMSM is the demagnetization phenomenon caused by the permanent magnets. Modeling of the demagnetization fault are important in developing and designing a protection system for the traction on HEVs, thus, an efficient and accurate IPMSM model for demagnetization fault simulation is necessary. By using the conventional dq0 IPMSM model, the current indicators of demagnetization fault are affected by noise which will cause inaccuracy of the simulation. For this reason, a dynamic phasors model of IPMSM is presented in this thesis. In this thesis, firstly, the dynamic phasors model of IPMSM is verified by using small-signal transient analysis for its stability. Secondly, the time-domain transient simulations of positive sequence currents are shown and compared to the conventional dq0 model with demagnetization fault.
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Nour, Fathy E. "The analysis of vibration signals during induction motor starting transients with a view to early fault detection." Thesis, Robert Gordon University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294706.

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Arafat, AKM. "ANALYSIS AND CONTROL OF FIVE-PHASE PERMANENT MAGNET ASSISTED SYNCHRONOUS RELUCTANCE MOTOR DRIVE UNDER FAULTS." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524168102423576.

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Babu, Hareesh. "Finite-element analysis of an induction motor with inter-turn short-circuit faults." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290082.

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Stator inter-turn short circuit (ITSC) faults are one of the common sources for induction machine failure affecting their reliable operation. In this thesis, a finite element (FE) model is developed to study the ITSC fault. The FE model is developed for a prototype induction machine that has the potential to emulate an ITSC fault in the stator. With the developed FE model of the prototype machine, a simulation study is performed to understand the behaviour of various electrical and magnetic quantities in time- and frequency-domain. The investigated quantities are potentially good signatures of the stator winding faults and they are therefore suitable to use in a condition monitoring system. The prototype machine with ITSC faults has been tested in an experimental setup and the results are compared to the simulation and also to analytical results. For the fault current it was found a good agreement between analytical results, FE simulations and experimental results. Moreover, the FE simulation results of the negative-sequence stator current amplitude present a minor mismatch with the analytical and experimental results. The reason for this mismatch is due to an inaccurate knowledge of the prototype machine geometrical parameters.<br>Kortslutning mellan varven i en asynkronmotors statorlindning (ITSC) är en av de vanligaste källorna för fel som påverkar dess drifttillförlitlighet. I detta examensarbete utvecklas en finit-element (FE) modell för att studera ITSC- fel. FE-modellen är utvecklad för en asynkronmotorprototyp som kan emulera ITSC-fel. Med den utvecklade modellen utförs en simuleringsstudie för att förstå beteendet hos olika elektriska och magnetiska egenskaper både i tids- och frekvensdomän. Dessa egenskaper är goda indikatorer av statorlindningsfel och kan därför med fördel användas i ett tillståndsövervakningssystem. Prototypmaskinen har testats experimentellt och de erhållna resultaten jämförs med FE-simuleringen och analysresultaten. Det analytiska resultatet, FEM- simuleringarna och den experimentella utvärderingen uppvisade god överrensstämmelse vad gäller felströmmen. Dock finns det en mindre avvikelse när det gäller amplituden hos statorströmmens negativa fasföljd. Orsaken till denna avvikelse är att prototypmaskinens geometri inte är helt känd.
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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<br>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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Ahnesjö, Henrik. "Fault detection of planetary gearboxes in BLDC-motors using vibration and acoustic noise analysis." Thesis, Uppsala universitet, Institutionen för elektroteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425966.

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This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acoustic sound measurements. The acquisition and analysis of the measurements were implemented using the data acquisition device, compactDAQ NI 9171, and the graphical programming software, NI LabVIEW. Two methods, i.e., power spectrum analysis and machine learning, were used for the analyzing of vibration and acoustic signals, and identifying faults in the gearbox. The first method based on the Fast Fourier transform (FFT) was used to the recorded sound from the BLDC-motor with the integrated planetary gearbox to identify the peaks of the sound signals. The source of the acoustic sound is from a faulty planet gear, in which a flank of a tooth had an indentation. Which could be measured and analyzed. It sounded like noise, which can be used as the indications of faults in gears. The second method was based on the BLDC-motors vibration characteristics and uses supervised machine learning to separate healthy motors from the faulty ones. Support Vector Machine (SVM) is the suggested machine learning algorithm and 23 different features are used. The best performing model was a Coarse Gaussian SVM, with an overall accuracy of 92.25 % on the validation data.
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Rabbi, Ata-E. "Detection of stator interturn fault of synchronous machine by rotor current analysis : A SIMULATION APPROACH." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187654.

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One of the major electrical faults of synchronous machines is an interturn short circuit in the stator winding, due to winding insulation failure. A synchronous machine is designed to have electrical and mechanical symmetry in the stator and rotor. An interturn fault in a synchronous machine damages the symmetrical property, thereby inducing abnormal symptoms like varying torque, mechanical vibration, and deviation of stator and rotor terminal currents from their usual waveform. This last symptom is what is studied in this thesis, as an indicator of the presence of an interturn fault. An interturn fault during machine operation can lead to a catastrophic machine failure and consequent long outage, unless it is rapidly detected and the machine shut down. Prevention of such outages is an important concern for machine owners and power system operators. That is why early detection of interturn faults is desirable to prevent such machine failure. Several external sensors can be implemented to detect such interturn fault, which is costly. That is why it is desirable if the interturn fault can be detected by analyzing the measured currents that already are available to a protective relay. In this thesis a simulation approach is presented to observe the pattern and frequency spectrum of rotor field current in presence of a stator inter turn fault. All simulations have been performed using Matlab-based programs written during this thesis work in the Electromagnetic department (ETK) in KTH. The outcome of the thesis is that in the presence of an interturn fault in the stator, several even harmonic components are found in the frequency spectrum of the rotor field current. The presence of these harmonics is not a definitive sign of an interturn fault, as almost the same even harmonics are found in the rotor field current if the stator winding asymmetry is taken into account.<br>En av de största elektriska felen i synkronmaskiner är en kortslutning mellan varv i statorlindningen, på grund av skadad lindningsisolering. En synkronmaskins konstruktion ger elektrisk och mekanisk symmetri i statorn och rotorn vilket ger en hög effektivitet. Ett varvfel i statorn skadar symmetrin och inducerar onormalt stora strömmarvilket leder till ett tidsvarierande vridmoment, mekanisk vibration, och onormala vågformer av strömmarna vid statorns och rotorns poler. Varvfel under maskinens drift kan snabbt orsaka stora skador, med följd av långa avbrottstider. Förebyggande av sådana avbrott är viktig hos maskinägare och systemansvariga. Därför är snabb detektering av sådana fel, och urdrifttagning av maskinen önskvärd för att minska skadan. Däremot ska detekteringen ha låg sannolikhet att lösa ut i onödan, av annat skäl. Externa sensorer kan användas för att upptäcka sådana fel, men det är tydligt önskvärt om detekteringen kan göras genom att analysera strömmarna som redan mäts av skyddsreläer för synkronmaskiner. I denna avhandling presenteras en metod för att simulera och observera mönster och frekvensspektrum av rotors magnetiseringsström i fall där statorn har varvfel, samt i andra fall som måste kunna skiljas från varvfel. Alla simuleringar har utförts med Matlab hos avdelningen för Elektroteknisk teori och konstruktion (ETK) i Skolan för Elektro- och systemteknik (EES) på KTH.
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Books on the topic "Motor fault analysis"

1

Ronca, James R. Pennsylvania motor vehicle insurance: An analysis of the Financial Responsibility Law. Trial Advocacy Foundation of the Pennsylvania Trial Lawyers Association, 1986.

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Colberg, Francis R. Damage analysis of internal faults in flux concentrating permanent magnet motors. Available from National Technical Information Service, 1994.

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Chattopadhyay, Surajit, Madhuchhanda Mitra, Samarjit Sengupta, and Subrata Karmakar. Induction Motor Fault Diagnosis: Approach through Current Signature Analysis. Springer, 2018.

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Chattopadhyay, Surajit, Madhuchhanda Mitra, Samarjit Sengupta, and Subrata Karmakar. Induction Motor Fault Diagnosis: Approach through Current Signature Analysis. Springer, 2016.

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R, Ronca James, and Ronca James R, eds. Pennsylvania motor vehicle insurance: An analysis of the Financial Responsibility Law. 2nd ed. Pennsylvania Trial Lawyers Association, 1998.

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Zagirnyak, Mykhaylo V., Zhanna Iv Romashykhina, and Andrii P. Kalinov. Diagnostics of Induction Motor Broken Rotor Bars on the Basis of the Electromotive Force Analysis. Nova Science Publishers, Incorporated, 2017.

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Book chapters on the topic "Motor fault analysis"

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Misra, Rajul, Kshitij Shinghal, Amit Saxena, and Alok Agarwal. "Industrial Motor Bearing Fault Detection Using Vibration Analysis." In International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_86.

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Lee, Dae-Jong, Jang-Hwan Park, Dong Hwa Kim, and Myung-Geun Chun. "Fault Diagnosis of Induction Motor Using Linear Discriminant Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554028_120.

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Singh, Priyanka, Rudra Narayan Dash, and Chinmoy Kumar Panigrahi. "Open Phase Fault Analysis of a Three-Phase Induction Motor." In Advances in Power Systems and Energy Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_9.

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Rauber, Thomas W., and Flávio M. Varejão. "Motor Pump Fault Diagnosis with Feature Selection and Levenberg-Marquardt Trained Feedforward Neural Network." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40261-6_54.

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Atanasov, Nasko, Zhivko Zhekov, Ivan Grigorov, and Mariela Alexandrova. "Application of Principal Component Analysis for Fault Detection of DC Motor Parameters." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68324-9_34.

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Dahi, Khalid, Soumia Elhani, Said Guedira, and Nabil Ngote. "Fault Diagnosis in Induction Motor Using Motor’s Residual Stator Current Signature Analysis." In Lecture Notes in Mechanical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_54.

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Matos, Diogo M. B., Jorge O. Estima, and Antonio J. Marques Cardoso. "Fault Analysis of Three-Level NPC Inverters in Synchronous Reluctance Motor Drives." In Technological Innovation for Cyber-Physical Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31165-4_21.

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Rgeai, Mohamed, Fengshou Gu, Andrew Ball, Mohamed Elhaj, and Mohamed Ghretli. "Gearbox Fault Detection Using Spectrum Analysis of the Drive Motor Current Signal." In Engineering Asset Lifecycle Management. Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-320-6_88.

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Verma, Alok, Haja Kuthubudeen, Viswanathan Vaiyapuri, Sivakumar Nadarajan, Narasimalu Srikanth, and Amit Gupta. "Bearings Fault Diagnosis in Electromechanical System Using Transient Motor Current Signature Analysis." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9199-0_41.

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Seetharama Rao, Y., and Devarabhotla Sai Chandra. "Comparison Analysis Through Condition Monitoring for Fault Detection of Bearing in Induction Motor." In Advances in Fluid Dynamics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4308-1_7.

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Conference papers on the topic "Motor fault analysis"

1

Li, Tianpei, Qadeer Ahmed, Giorgio Rizzoni, Jason Meyer, Mathew Boesch, and Bader Badreddine. "Motor Resolver Fault Propagation Analysis for Electrified Powertrain." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5408.

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As an integral part of electrified powertrain, resolver is broadly used to do position and speed sensing for electric motors, subject to different types of resolver faults. This paper investigates the resolver fault propagation in electrified powertrain, with focus on the amplitude imbalance, quadrature imperfection and reference phase shift in the resolver position sensing system. The resolver fault effects in the Permanent Magnet Synchronous Machine (PMSM) drive system are first analyzed based on the mathematical model of a surface mounted PMSM with direct Field-oriented Control (FOC). Then the resolver fault propagation in the powertrain is studied in terms of two different motor operating conditions, motor torque control and motor speed control. Simulation is done in Matlab/Simulink based on the PMSM drive model and the powertrain-level simulator to verify the fault propagation analyses. The results can be used to help design the resolver fault diagnostic strategy and determine speed matching condition between engine and electric motor for mode transition control in hybrid electric vehicles.
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Zhang, Guoguang, Hui Zhang, Junmin Wang, Hai Yu, and Roger Graaf. "Actuator Fault Sensitivity Analysis for In-Wheel Motor Electric Ground Vehicle With Active Steering System." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6035.

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This paper presents the sensitivity analyses on vehicle motions with regard to faults of in-wheel motors and steering motor for an electric ground vehicle (EGV) with independently actuated in-wheel rear motors. Based on the vehicle model, direct method is applied to determine, to what extent, that different actuator faults affect vehicle motions such as the longitudinal velocity, lateral velocity, and yaw rate. For motion indices like vehicle sideslip angle and longitudinal acceleration, linearizations around equilibrium points are conducted and their sensitivities to actuator faults are analyzed. Results show that all mentioned vehicle motions are more sensitive to the fault of steering motor than that of in-wheel motors. In addition, the effects on vehicle motions due to four types of faults, i.e. additive, loss-of-effectiveness, time-varying-gain and stuck-at-fixed-level faults, are examined through CarSim® simulations and vehicle experiments under a representative maneuver.
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Al-Leythi, Haidy H., Zainab A. Mohammed, Detlef Hummes, Ghulam Amjad Hussain, Cyrille Caironi, and Bernhard Fruth. "Motor Watch - Motor Fault Signature Analysis." In 2018 International Conference on Computing Sciences and Engineering (ICCSE). IEEE, 2018. http://dx.doi.org/10.1109/iccse1.2018.8374218.

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Rao, S. Ganesh, S. Lohith, P. Chandan Gowda, Annapurna Singh, and S. N. Rekha. "Fault Analysis of Induction Motor." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951336.

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Kathir, I., S. Balakrishnan, and R. J. Bevila. "Fault analysis of induction motor." In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icetect.2011.5760163.

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Harihara, Parasuram P., and Alexander G. Parlos. "Sensorless Detection and Isolation of Faults in Motor-Pump Systems." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66446.

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Induction motors are the workhorses of industry and a lot of effort has been invested in detecting and diagnosing induction motor faults through the analysis of the motor electrical signals. However, in many industrial applications, electric motors are used to drive dynamic loads such as pumps, fans, blowers etc. Failure of either the motors or the driven loads is associated with operational disruption. Consequently it would be beneficial if the entire motor-pump system is monitored and diagnosed. The large costs associated with production losses can be avoided if system degradation can be detected at early stages prior to failure. Moreover, downtime can be further reduced if the faulty component within the drive power system can be isolated thereby aiding plant personnel to be better prepared with spares and repair kits. Hence there is not only a strong need for cost-effective detection schemes to assess the condition of the drive power system as a whole, but also a strong need for efficient isolation schemes to identify the component within the system that is faulty. This paper describes a sensorless approach to detect and isolate induction motor and/or centrifugal pump faults. Motor and pump bearing degradation is considered to validate the performance effectiveness of the proposed scheme. No add-on sensors, on either the motor or the pump, are used in the development of the proposed method to avoid any reduction in overall system reliability and prevent increased costs. In fact, motor and/or pump bearing degradation is detected and isolated using only the motor line voltages and phase currents. The proposed technique is insensitive to electric power supply fluctuations and mechanical load variations and it does not require prior knowledge of either the motor or the pump design parameters. Hence this approach can be easily ported to motor-pump systems of varying manufacturers and sizes. The developed algorithm has been tested on accelerated fault data collected from a centrifugal pump fluid loop driven by a 3-φ, 3 hp induction motor. Results from these experiments indicate that the proposed fault detection and isolation scheme successfully detects and classifies bearing degradation in the motor and/or the pump without false positives or misclassification.
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Georgescu, Adrian, and P. A. Simionescu. "A Cost-Effective Computerized Data Acquisition and Motor Current Signature Analysis Demonstrator for Industry and Academia." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59085.

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This paper presents the development, results and trainee perception of a laboratory experiment used for diagnosing the occurrence of different faults in impeller-pump induction motors by means of the Motor Current Signature Analysis (MCSA) technique. This is a quintessential experiment, relatively inexpensive and easy to implement, that combines elements of computerized data acquisition, Discrete Fourier Transform analysis and fault identification of electric motors. Following this laboratory exercise, students and trainees are able to understand and apply MCSA to determine common faults of induction motors. The test stand, experimental setup, and test procedure are described with sufficient details in the paper for others to build one of their own.
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Wang, Rongrong, and Junmin Wang. "In-Wheel Motor Fault Diagnosis for Electric Ground Vehicles." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4050.

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This paper presents an in-wheel motor fault diagnosis method for fault-tolerant control of four-wheel independently driven (4WID) electric vehicles. 4WID electric vehicle is one of the promising architectures for electric ground vehicles. While such a vehicle architecture greatly increases the flexibility for vehicle control, it also raises the requirements on system reliability, safety, and fault tolerance due to the increased number of actuators. A fault diagnosis approach for finding the faulty in-wheel motor/motor driver pair is developed. The proposed diagnosis approach does not need a precise knowledge on tire-road friction coefficient (TRFC). Robustness analysis shows that the approach can work well in the presence of tire modeling errors. Simulations using a high-fidelity, CarSim, full-vehicle model indicated the effectiveness of the proposed in-wheel motor/motor driver fault diagnosis approach.
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Xue, Xin, and V. Sundararajan. "Induction Motor Multi-Fault Analysis Based on Intrinsic Mode Functions in Hilbert-Huang Transform." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87833.

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This paper reports experimental studies to detect two faults in a 3-phase 1.5hp induction motor using intrinsic mode functions from Hilbert-Huang transform. The faults studied are the eccentricity of the air-gap between the rotor and stator and damage to the outer race of bearings. The experiments are conducted under four conditions: the normal no-fault condition, two single fault conditions and the multiple faults condition. Two microphones, one vibration sensor and one current sensor are used to collect sound, vibration and current data respectively. The data is analyzed using the Hilbert-Huang transform and Fast Fourier Transform. Features are extracted from the spectrum of intrinsic mode functions and the average value of their envelope. Three simple classifiers are used to classify these four experimental conditions. The results demonstrate that the multiple sensors do improve the classification rate and that the Intrinsic Mode Functions obtained by the Hilbert-Huang transform are more effective than FFT in classifying multiple faults.
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10

Krishna, Merugu Siva Rama, and Kiran S. Ravi. "Fault diagnosis of induction motor using Motor Current Signature Analysis." In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, 2013. http://dx.doi.org/10.1109/iccpct.2013.6528849.

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