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Relevant bibliographies by topics / Condition monitoring; 3-phase induction motors

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Journal articles
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Conference papers

Academic literature on the topic 'Condition monitoring; 3-phase induction motors'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Condition monitoring; 3-phase induction motors"

1

Hsueh, Ittangihala, Wu, Chang, and Kuo. "Condition Monitor System for Rotation Machine by CNN with Recurrence Plot." Energies 12, no. 17 (August 21, 2019): 3221. http://dx.doi.org/10.3390/en12173221.

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Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

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Laadjal, Khaled, Mohamed Sahraoui, Abdeldjalil Alloui, and Antonio J. Marques Cardoso. "Three-Phase Induction Motors Online Protection against Unbalanced Supply Voltages." Machines 9, no. 9 (September 20, 2021): 203. http://dx.doi.org/10.3390/machines9090203.

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Three-phase induction motors (IMs) are the main workhorse in industry due to their many advantages as compared to other types of industrial motors. However, the efficiency and lifetime of IMs can be considerably affected by some operating conditions, in particular those related to unbalanced supply voltages (USV), which is quite a common condition in industrial plants. Therefore, early detection and a precise severity estimation of the USV for all working conditions can prevent major breakdowns and increase reliability and safety of industrial facilities. This paper proposes a reliable method allowing for a precise and online detection of the USV condition, by monitoring a pertinent indicator calculated using the voltage symmetrical components. The effectiveness of the proposed method is validated experimentally for several different working conditions, and a comparison with other indicators available in the literature is also performed.

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Kashirskikh, Veniamin, Valery Zavyalov, Irina Semykina, and Valery Borovtsov. "The Computer System for Monitoring of Induction Motors Condition of Mining Machinery." E3S Web of Conferences 105 (2019): 03023. http://dx.doi.org/10.1051/e3sconf/201910503023.

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The possibility of carrying out monitoring of parameters and variables of the induction electric motor (IM) working as a part of the electric mining machinery drive is considered. Monitoring is carried out on the working inventory in real time by means of the computer system on the basis of the IM mathematical model, mathematical estimation methods and also information which is contained in easily measured phase tension and IM stator currents. As a result IM indicators are defined difficult or cannot be measured in the mining machinery operation course. These are the active and induced windings resistances of a stator and a rotor, a magnetic linkage, an angular frequency of a rotor rotation and the electromagnetic moment. Algorithms of dynamic IM identification and the software are developed for carrying out monitoring. Theoretical provisions and conclusions are confirmed with results of the pilot studies. Information received during monitoring can be used for perfecting of the electric drive.

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Shnibha, R., A. Albarbar, A. Abouhnik, and G. Ibrahim. "A More Reliable Method for Monitoring the Condition of Three-Phase Induction Motors Based on Their Vibrations." ISRN Mechanical Engineering 2012 (October 18, 2012): 1–9. http://dx.doi.org/10.5402/2012/230314.

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This paper is concerned with accurate, early, and reliable fault diagnosis using an enhanced vibration measurement technique based on short-time Fourier transform. The novelty of this work lies in detecting very low-phase imbalance-related faults. The energy contained within specified frequency bands centred on the rotor frequency and power supply frequency, and their sideband zones were calculated. The technique was firstly demonstrated by simulated signals and then verified by experimental measurements taken from two different-sized test rigs. The first one comprised a 1.1 kW variable speed three-phase induction motor with varying output load (no load, 25%, 50%, 75%, and 100% load). Two types of common faults were introduced: imbalance in one phase as the electrical fault and misalignment of load as the mechanical fault. The second test rig had a 3 kW three-phase induction motor again with varying load, and here the two seeded faults were: phase imbalance and one broken rotor bar. The measured energy levels in the test conditions were found to be affected by type of fault and fault severity. It is concluded that the proposed method offers a potentially reliable and computationally inexpensive condition monitoring tool which can be implemented with real-time monitoring systems.

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Mustafa, Mohammed Obaid. "Broken rotor bars fault detection in induction motors based on current envelope and neural network." Eastern-European Journal of Enterprise Technologies 3, no. 2 (111) (June 30, 2021): 88–95. http://dx.doi.org/10.15587/1729-4061.2021.227315.

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The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

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Lucas, M., and W. T. Thomson. "A study of the natural vibratory response of stator structures to improve condition monitoring strategies for induction motors." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 212, no. 1 (January 1, 1998): 57–68. http://dx.doi.org/10.1243/0954406981521042.

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On-line condition monitoring is widely used to detect failure mechanisms in large highvoltage three-phase induction motors. The high-voltage winding is installed in a complex, stator core structure which vibrates due to electromagnetic forces. Insulation degradation can be diagnosed via on-line monitoring of partial discharges but the fundamental cause is often mechanical damage due to stator vibration. This paper presents results of an investigation into the natural vibratory response of the complete stator assembly. Results from finite element models of complex stator assemblies, including the frames, are supported by experimental measurements. These include freely suspended stator cores as in previous studies, and also now introduce the much more realistic condition of the complete stator assembly securely bolted on to a solid baseplate. The knowledge gathered in this study will aid the application of a vibration monitoring strategy to detect stator core vibration which can lead to winding insulation damage.

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Prakash, Chandra, and Rakesh Kumar Saini. "Crop Field Monitoring Platform: IoT-Based Monitoring and Controlling of Crop Field and Induction Motor Protection from Voltage Fluctuation." International Innovative Research Journal of Engineering and Technology 6, no. 2 (December 30, 2020): CS—1—CS—7. http://dx.doi.org/10.32595/iirjet.org/v6i2.2020.136.

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The impassable use of technologies contributes to on condition that the accurate amount of water needed by plants. Agricultural country like India, where more than 70% of rural population depends on farming and contributes around 18% to the GDP. Despite of having modern facilities, farmers are still using traditional methods for irrigation. The main reason is the lack of knowledge and the cost of resources are too high which cannot bear by the farmers. It is mostly seen that the farmer uses ground water for irrigation. The management of ground water for irrigation is essential, while contributing to environmental sustainability. Here the word management is to irrigate more and more using the least water. IoT technologies can be a better option to achieve this goal. IoT technology is used in management of ground water for irrigation in the agriculture. But the commonality of the same thing has been found in all systems i.e., the farmer has to buy lots of equipments to apply the smart irrigation. This paper implements a gadget that provide information of the crop situation like degree the resistance or conductivity across the soil matrix among two contacts are basically junk, soil disease sensor regulates while the seeds will germinate, and how rapid your flowers will develop in step with those data, microcontroller take the motion in form of induction motor gets ON/OFF, when it's far required. This paper is also aim to shield 3 phase induction motor from inrush currents on the spreading device like fluctuations and single phasing.

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Adam, Putri Ramadhani, Purwanto Gendroyono, and Nur Hanifah Yuninda. "MONITORING SUPLAI TEGANGAN PADA MOTOR INDUKSI TIGA FASA MENGGUNAKAN MIKROKONTROLER ARDUINO DAN SENSOR TEGANGAN ZMPT101B." Journal of Electrical Vocational Education and Technology 5, no. 2 (December 16, 2020): 37–43. http://dx.doi.org/10.21009/jevet.0052.06.

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ABSTRACT The purpose of this study is to create a three-phase induction motor voltage monitoring system based on Arduino Uno microcontroller using a ZMPT101B voltage sensor. In addition, this study aims to determine the percentage of source voltage unbalance entering the 3 phase induction motor based on the National Standard for Electric Power System and Equipment ANSI Std C84.1-1995, NEMA Std MGI.2009 from the results of monitoring equipment that has been was made. The research method used by the author is a laboratory experimental method which includes manipulation, control, and observation. The results showed that the value of the percentage of voltage unbalance, the average for the morning the value is still below 1%. As for the value of the percentage of voltage unbalance in the afternoon and evening, the value is already above 1%. If according to the standard, the percentage of voltage unbalance permitted without derating (decreasing power) based on NEMA is 1%. Meanwhile, the maximum percentage of allowable voltage unbalance is 5%. So, the results of the research data of the three times the safest condition is in the morning. Meanwhile, research data taken during the afternoon and evening conditions are still safe but there will be a decrease in power. In addition, the comparison of the ZMPT101B voltage sensor with a digital AVO meter when detecting the voltage has an average error of 0.07% for the first ZMPT101B voltage sensor, 0.28% for the second ZMPT101B voltage sensor and 0.15% for the sensor the third voltage ZMPT101B. So that it can be said that the voltage monitoring system that is made in accordance with the provisions and can be applied as learning by students of Electrical Engineering Education. ABSTRAK Penelitian ini bertujuan untuk membuat sistem monitoring suplai tegangan motor induksi tiga fasa berbasis mikrokontroler Arduino Uno menggunakan sensor tegangan ZMPT101B. Selain itu, dalam penelitian ini bertujuan untuk mengetahui persentase ketidakseimbangan tegangan sumber yang masuk pada motor induksi 3 fasa berdasarkan standar The National Standard for Electric Power System and Equipment ANSI Std C84.1-1995, NEMA Std MGI.2009 dari hasil monitoring alat yang telah dibuat. Metode penelitian yang digunakan penulis yaitu metode eksperimen laboratorium yang meliputi manipulasi, pengendalian, dan pengamatan. Hasil penelitian menunjukkan bahwa nilai persentase ketidakseimbangan tegangan, rata rata untuk di waktu pagi nilainya masih di bawah 1 %. Sedangkan untuk nilai persentase ketidakseimbangan tegangan di waktu siang dan sore hari nilainya sudah di atas 1 %. Jika sesuai dengan standar, persentase ketidakseimbangan tegangan yang diijinkan tanpa melakukan derating (penurunan daya) berdasarkan NEMA adalah 1 %. Sedangkan, maksimum persentase ketidakseimbangan tegangan yang diijinkan adalah 5 %. Maka, hasil dari data penelitian ketiga waktu tersebut yang paling aman kondisinya adalah di waktu pagi hari. Sedangkan, data penelitian yang diambil saat siang dan sore hari kondisinya masih aman tetapi akan ada penurunan daya. Selain itu, Perbandingan sensor tegangan ZMPT101B dengan alat ukur AVO meter digital saat mendeteksi tegangan memiliki error rata-rata yaitu 0,07 % untuk sensor tegangan ZMPT101B yang pertama, 0,28 % untuk sensor tegangan ZMPT101B yang kedua dan 0,15 % untuk sensor tegangan ZMPT101B yang ketiga. Sehingga dapat dikatakan bahwa sistem monitoring tegangan yang dibuat sesuai dengan ketentuan dan bisa diaplikasikan sebagai pembelajaran mahasiswa Pendidikan Teknik Elektro.

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Sinha, Ashish Kumar, Sukanta Das, and Tarun Kumar Chatterjee. "Empirical relation for broken bar determination in SCIM." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 1 (January 2, 2018): 242–65. http://dx.doi.org/10.1108/compel-11-2016-0515.

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Purpose Condition monitoring of squirrel cage induction motors (SCIMs) is indispensible for achieving fault-free working environment. As broken rotor bars (BRBs) are one of the more frequent faults in a SCIM especially where direct-on-line starting is indispensible, as in underground mines, a priori knowledge of fault severity in terms of the number of BRBs assists in effective fault monitoring. In this regard, this paper aims to propose a unique empirical relation to facilitate the determination of number of BRB. Design/methodology/approach Fast Fourier transform is used to obtain fault sideband amplitudes under varying number of BRBs and load torque for 5.5 kW, 7.5 kW, 10 kW, three-phase, 415 V, 50 Hz SCIMs in MATLAB/Simulink. The nature of variation is decided by an appropriate curve fitting technique for comprehending a unique empirical relation. The proposed empirical relation is validated by bootstrapping and z-test. Furthermore, hardware validation is done using 1 kW laboratory prototype with Labview interface. Findings The analytical study reveals the dependence of lower and upper sideband amplitudes on the number of BRBs, load torque and machine rating. Therefore, fault severity in terms of number of BRBs is accurately calculated using the proposed empirical relation if load torque, machine rating and amplitudes of lower and upper sidebands are known. Originality/value The unique empirical relation proposed in the present work provides accurate knowledge of fault severity in terms of the number of BRBs. This facilitates maintenance scheduling which shall reduce effective downtime and improve production.

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Ciancetta, Fabrizio, Edoardo Fiorucci, Antonio Ometto, Andrea Fioravanti, Simone Mari, and Maria-Anna Segreto. "A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method." Sensors 21, no. 3 (January 23, 2021): 754. http://dx.doi.org/10.3390/s21030754.

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Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.

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Dissertations / Theses on the topic "Condition monitoring; 3-phase induction motors"

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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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Liang, Bo. "Condition monitoring and fault diagnosis of three-phase induction motors." Thesis, University of Manchester, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488140.

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It is well known there are three main approaches to maintenance of machinery, namely breakdown maintenance, scheduled maintenance and preventive maintenance. Breakdown maintenance is practised when machines are operated until they fail before maintenance is carried out. This out-of-date maintenance policy is slowly being abandoned by industry. Scheduled maintenance relates to interrupting machinery at regular intervals for the purposes of maintenance and this policy is still being used in many areas even though it is expensive one. The third maintenance policy is that of preventive maintenance. This approach aims to perform machinery maintenance just before a fault occurs and therefore is used to optimise the maintenance function. The basis of this approach is fault detection, severity assessment and fault diagnosis collectively referred to as condition monitoring. The objective of machinery condition monitoring is to obtain the earliest possible warning of incipient damage or malfunction and to assist in the diagnosis of the subsequent cause. There are many books that focus on condition monitoring and fault diagnosis [1-3]. According to Matthew [4], machine condition monitoring is concerned with obtaining data that would aid in evaluating machinery mechanical and electrical integrity. It is the measurement of various parameters related to the mechanical and electrical condition of the machinery such as vibration, acoustic emission, temperature, oil pressure, oil debris, voltage, current, conductivity and corrosion which make it possible to determine whether the machine is healthy. In addition machinery models can be established for simulating and predicting the trend of changing parameters. These are parameters such as vibration, temperature, pressure, current and voltage that change as a result of differing operating condition, thus providing the maintenance engineer with prior knowledge that may be used for maintenance or improving the performance of the machine. It is generally accepted that condition monitoring offers many advantages.

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Schoen, Randy R. "On-line current-based condition monitoring of three-phase induction machines." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/12952.

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Barbour, Alexandra. "The analysis of airgap eccentricity in three-phase induction motors using finite elements for a reliable condition monitoring strategy." Thesis, Robert Gordon University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265866.

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Book chapters on the topic "Condition monitoring; 3-phase induction motors"

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Saad, Nordin, Muhammad Irfan, and Rosdiazli Ibrahim. "Noninvasive Methods for Motor Fault Diagnosis." In Condition Monitoring and Faults Diagnosis of Induction Motors, 27–44. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018. | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: CRC Press, 2018. http://dx.doi.org/10.1201/9781351172561-3.

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Alashter, Aisha, Yunpeng Cao, Khalid Rabeyee, Samir Alabied, Fengshou Gu, and Andrew D. Ball. "Bond Graph Modelling for Condition Monitoring of Induction Motors." In Advances in Asset Management and Condition Monitoring, 511–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57745-2_43.

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Meneceur, Redha, Abd-Razak Metatla, and Noureddine Meneceur. "State Space Modeling Theory of Induction Motors for Sensorless Control and Motoring Purposes." In Condition Monitoring of Machinery in Non-Stationary Operations, 481–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_50.

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Sousa, Diego P., Guilherme A. Barreto, Charles C. Cavalcante, and Cláudio M. S. Medeiros. "LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison." In Advances in Intelligent Systems and Computing, 130–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19642-4_13.

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Spagnol, Marco, Luigi Bregant, and Alessandro Boscarol. "Electrical Induction Motor Higher Harmonics Analysis Based on Instantaneous Angular Speed Measurement." In Applied Condition Monitoring, 27–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_3.

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Douiri, Moulay Rachid, Mohamed Cherkaoui, Ahmed Essadki, and Sidi Mohamed Douiri. "Rotor Speed Identification Using Two Nonlinear Observers for Induction Motor Drive." In Condition Monitoring of Machinery in Non-Stationary Operations, 143–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_15.

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Rahman, Ataur, Mohammad Rubaiyat Tanvir Hossain, and Md Saifullah Siddiquee. "IoT Based Bidirectional Speed Control and Monitoring of Single Phase Induction Motors." In Advances in Intelligent Systems and Computing, 1047–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68154-8_88.

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Ngote, Nabil, Saïd Guedira, and Mohamed Cherkaoui. "Conditioning of a Spectral Indicator for the Detection of Rotor Faults of an Induction Motor by Using the TSA Method." In Condition Monitoring of Machinery in Non-Stationary Operations, 339–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_36.

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Cruz, S. M. A., and A. J. Marques Cardoso. "Discriminating Between Rotor Asymmetries and Time-Varying Loads in Three-Phase Induction Motors." In Condition Monitoring and Diagnostic Engineering Management, 319–27. Elsevier, 2001. http://dx.doi.org/10.1016/b978-008044036-1/50037-8.

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Maheswari M. and Gunasekharan S. "Types of Faults and Condition Monitoring Methods of Induction Motors." In Advances in Computer and Electrical Engineering, 131–62. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6989-3.ch007.

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Induction motors are the electromechanical devices used to convert electrical energy into mechanical energy and work under the principle of mutual inductance. They have stator and rotor as two major parts. They run at constant speed when the supply voltage and frequency are constant and are suited for constant speed drives. They have rugged construction but working environment causes different faults. As per IEEE and EPRI study on induction motor faults, bearing fault and stator faults are 46% and 36%, respectively. The broad categories of the fault are stator winding fault, broken rotor fault, rotor mass unbalance fault, bowed rotor faults, single phasing fault, bearing fault, and crawling. Unbalanced stator voltage and current, oscillations in torque, drop in efficiency and torque, overheating and unwarranted vibration are the effects of these faults. Undetected faults cause complete failure of motor and it is costly in terms of lost production time, maintenance cost, and wasted raw materials.

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Conference papers on the topic "Condition monitoring; 3-phase induction motors"

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Xue, Xin, V. Sundararajan, and Wallace P. Brithinee. "The Application of Wireless Sensor Networks for Fault Condition Monitoring in Three-Phase Induction Motors." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15417.

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A commonly used technique for the detection of faults in large three-phase induction motors is to measure the supply current to the motor and analyze the signal spectrum. This technique is well established and has been shown to be indicative of a faulty condition. However, current signature analysis is usually used by very skilled technicians using expensive equipment. A cost effective condition monitoring technique is needed for smaller motors (those smaller than 200 HP). A motor's heat signature tells more about its quality and condition. For heavy-duty motors, it is very important to detect overheating because hot windings deteriorate rapidly. This paper explores the possibilities of using wireless sensors inside the motor. Wireless sensors are gaining popularity in condition monitoring applications because of their relatively low cost and ease of installation. This paper proposes a system of condition monitoring of the three-phase induction motor using wireless sensor networks (WSN) to measure the temperature and the vibration signals. The sensor nodes are placed on the rotor and the stator. The data acquisition is accomplished at a base station located at a distance of 6 feet. Issues related to electromagnetic interference between the wireless devices and the magnetic fields present within the motor are investigated.

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Agyare, Ofosu Robert, Ama Baduba Asiedu-Asante, and Adjei Reinhard Biney. "Fuzzy Logic Based Condition Monitoring of a 3-Phase Induction Motor." In 2019 IEEE AFRICON. IEEE, 2019. http://dx.doi.org/10.1109/africon46755.2019.9133780.

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Choi, S. D., B. Akin, M. M. Rahimian, H. A. Toliyat, and M. Azadpour. "A generalized condition monitoring method for multi-phase induction motors." In 2009 IEEE International Electric Machines and Drives Conference (IEMDC). IEEE, 2009. http://dx.doi.org/10.1109/iemdc.2009.5075261.

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Kumar, Dileep, Abdul Basit, Aisha Saleem, and Engr Ghulam Abbas. "PLC Based Monitoring & Protection of 3-Phase Induction Motors against Various Abnormal Conditions." In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2019. http://dx.doi.org/10.1109/icomet.2019.8673497.

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Watson, J. F. "The use of line current as a condition monitoring tool for three phase induction motors." In IEE Colloquium Design, Operation and Maintenance of High Voltage (3.3kV to 11kV) Electric Motors for Process Plant. IEE, 1999. http://dx.doi.org/10.1049/ic:19990822.

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Watson, J. F. "The use of line current as a condition monitoring tool for three phase induction motors." In IEE Symposium on Design, Operation and Maintenance of High Voltage (3.3kV to 11kV) Electric Motors in Process Plant. IEE, 1998. http://dx.doi.org/10.1049/ic:19980374.

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Xin Xue, V. Sundararajan, and Wallace P. Brithinee. "The application of wireless sensor networks for condition monitoring in three-phase induction motors." In 2007 Electrical Insulation Conference and Electrical Manufacturing Expo (EIC/EME). IEEE, 2007. http://dx.doi.org/10.1109/eeic.2007.4562657.

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Harihara, Parasuram P., and Alexander G. Parlos. "Sensorless Detection of Impeller Cracks in Motor Driven Centrifugal Pumps." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66273.

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Electrical signal analysis has been in use for quite some time to detect and diagnose induction motor faults. In most industrial applications, induction motors are used to drive dynamic loads such as pumps, compressors, fans, etc. Failure of either the motors or the driven loads results in an unscheduled downtime which in turn leads to loss of production. These operational disruptions could be avoided if the equipment degradation is detected in its early stages prior to reaching catastrophic failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of electric motors but also the driven loads. This paper presents an experimentally demonstrated sensor-less approach to detect impeller cracks in centrifugal pumps. The proposed method is sensorless in the sense that it does not use any mechanical and/or process pump sensors to detect impeller faults. Rather motor electrical measurements are used for the intended purpose. Mechanical sensors have high costs and low reliability, and frequently fail more often than the system being monitored. Hence add-on mechanical sensors reduce the overall system reliability. In this study, fault detection is accomplished using only the line voltages and phase currents of the electric motor driving the pump. The developed detection algorithm is insensitive to electrical power supply and load variations. Furthermore, it does not require prior knowledge of either a motor or the pump model or design parameters and hence the detection algorithm can be easily ported to motor-pump systems of varying manufacturers and sizes. The proposed fault detection scheme has been tested on data collected from a centrifugal pump driven by a 3-φ, 3 hp induction motor. Several cracks on the pump impeller are staged to validate the detection effectiveness of the proposed scheme and compare its effectiveness with respect to continuous vibration monitoring. In addition to these staged faults, experiments are also conducted to demonstrate the prevention of false alarms by the algorithm. Results from all of these experiments are presented to substantiate the performance of the sensorless pump fault detection scheme.

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Patel, Rakesh A., Bhavesh Bhalja, and Md Aftab Alam. "Condition Monitoring of Three-Phase Induction Motor." In 2020 IEEE International Conference for Convergence in Engineering (ICCE). IEEE, 2020. http://dx.doi.org/10.1109/icce50343.2020.9290540.

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Barbour, A. "Finite element analysis and on-line current monitoring to diagnose airgap eccentricity in 3-phase induction motors." In Eighth International Conference on Electrical Machines and Drives. IEE, 1997. http://dx.doi.org/10.1049/cp:19971057.

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