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Journal articles on the topic 'Motor Current Signature Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 24 February 2023

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1

Nambia, Renjini E. "Motor Current Signature Analysis for Speed Estimation." Asian Journal of Electrical Sciences 8, S1 (June 5, 2019): 29–32. http://dx.doi.org/10.51983/ajes-2019.8.s1.2312.

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The induction motors are widely applied due to their low price and ruggedness. There are several methods for speed estimation in the literature. The optical tachometer is an expensive and less reliable method for the speed measurement. Hence sensor less method for speed estimation is an important topic of research. In this paper, speed estimation of induction motor using motor current signature analysis is done. The comparative study of different methods shows that the motor current signature analysis (MCSA) is more accurate in speed estimation. This technique is experimentally verified on a 5HP, 3.7kW, 1430rpm 415V, 50Hz, 7.5A, 3Ø squirrel cage induction motor using LabVIEW. The experimental and simulation results show that the overall speed estimation error is within 5 rpm.

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2

Syahputra, Ramadoni, Hedi Purwanto, Rama Okta Wiyagi, Muhamad Yusvin Mustar, and Indah Soesanti. "Analysis of Induction Motor Performance Using Motor Current Signature Analysis Technique." Journal of Electrical Technology UMY 5, no. 1 (July 21, 2021): PRESS. http://dx.doi.org/10.18196/jet.v5i1.11764.

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This paper discusses the analysis of the performance of an induction motor using the motor current signature analysis (MCSA) technique. Induction motor is a type of electric machine that is widely used in industry. One of the industries that utilize induction motors is a steam power plant (SPP). The role of induction motors is very vital in SPP operations. Therefore, it is necessary to monitor the performance, stability, and efficiency to anticipate disturbances that can cause damage or decrease the life of the induction motor. MCSA is a reliable technique that can be used to analyze damage to an induction motor. In this technique, the induction motor current signal is detected using a current transducer. The signal is then passed on to the signal conditioning and then into the data acquisition device. The important signal data is analyzed in adequate computer equipment. The results of this analysis determine the condition of the induction motor, whether it is normal or damaged. In this research, a case study was carried out at the Rembang steam power plant, Central Java, Indonesia. The results of the analysis of several induction motors show that most of them are in normal conditions and are still feasible to operate.

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3

Nikolić, Saša, and Radoš Ćalasan. "Motor Current Signature Analysis in Predictive Maintenance." Journal of Energy - Energija 67, no. 4 (June 2, 2018): 3–6. http://dx.doi.org/10.37798/201867462.

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The aim of this paper is to draw attention to the possibilities offered by spectral analysis of current and voltage in the predictive maintenance of the electric motor. Motor Circuit analysis (MCA) and Motor Current Signature analysis (MCSA) are innovative and non-invasive methods that enable diagnostics and assessment of the condition of the electric motor. The main advantage of the method is that the test is carried out during the normal motor operation, without downtime. All motor defects can be detected at the earliest stage. This enable planning the overhaul according to the condition which can make significant savings. Advanced MCSA analysers enable diagnostics of electric motors that are powered either via a soft starter, frequency inverter or directly from mains. So, it is possible in a simple and reliable way make an condition assessment of frequency inverters. In addition, it is possible to detect faults of driven machine, like misalignment, imbalance, blade faults, belts, bearings issues etc. Theoretical basis and tests that are carried out are explained in the paper.

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4

Garcia-Calva, Tomas A., Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez, and Rene de J. Romero-Troncoso. "Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients." Energies 14, no. 5 (March 8, 2021): 1469. http://dx.doi.org/10.3390/en14051469.

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The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.

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5

Kliman, G. B., and J. Stein. "Methods of Motor Current Signature Analysis." Electric Machines & Power Systems 20, no. 5 (September 1992): 463–74. http://dx.doi.org/10.1080/07313569208909609.

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6

Krishnan, Shashikumar, and Vijayakumar Vengadasalam. "Detecting industrial motor faults with current signatures." F1000Research 10 (September 8, 2021): 903. http://dx.doi.org/10.12688/f1000research.54266.1.

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Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.

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7

Lv, Feng, Hao Sun, Wen Xia Du, and Shue Li. "Fault Signature Analysis Based on Transient Current for Induction Motors." Applied Mechanics and Materials 44-47 (December 2010): 1807–11. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1807.

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The characteristics of broken rotor bars in induction motors are reflected in the abnormal harmonic of the stator current. At present, fast Fourier transform( ) and time-varying frequency spectrum analysis method are used in such fault diagnosis, but non-stationary motors operation can bring a certain difficulties to the monitoring and diagnosis. This paper studies the basic characteristics of wavelet transform, adopting the wavelet analysis technologies of signal processing and selecting mother wavelet, the paper makes the multi-scale transformation to the motor starting current, excavates the harmonic informations on non-stationary condition, realizes fault diagnosis of motor broken rotor bars effectively, The consistent diagnostic results prove the effectiveness of the method.

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8

Pires, V. Fernão, Manuel Kadivonga, J. F. Martins, and A. J. Pires. "Motor square current signature analysis for induction motor rotor diagnosis." Measurement 46, no. 2 (February 2013): 942–48. http://dx.doi.org/10.1016/j.measurement.2012.10.008.

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9

Miyoshi, Masahito, Makoto Kanemaru, Yuto Yasuhara, and Toshihiko Miyauchi. "Application and Amelioration of Motor Current Signature Analysis." IEEJ Transactions on Industry Applications 142, no. 2 (February 1, 2022): 119–24. http://dx.doi.org/10.1541/ieejias.142.119.

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10

Thomson, W. T., and M. Fenger. "Current signature analysis to detect induction motor faults." IEEE Industry Applications Magazine 7, no. 4 (2001): 26–34. http://dx.doi.org/10.1109/2943.930988.

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11

Deshmukh, Sayli A., and A. R. Askhedkar. "Detecting Faults Based on Motor Current Signature Analysis for Electric Motor." International Journal of Engineering Research and Applications 07, no. 07 (July 2017): 75–79. http://dx.doi.org/10.9790/9622-0707047579.

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12

Halder, Sudip, Sunil Bhat, Daria Zychma, and Pawel Sowa. "Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review." Energies 15, no. 22 (November 16, 2022): 8569. http://dx.doi.org/10.3390/en15228569.

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The most often used motor in commercial drives is the induction motor. While the induction motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses can result in defects. Therefore, many researchers who are involved in condition monitoring have been interested in the development of reliable and efficient fault diagnostic technologies. This paper’s goal is to provide an overview of available fault detection methods for the broken rotor bar problem, one of several defects associated to induction motors. Despite the fact that it is less common than bearing or insulator failure, this fault may cause electrical machines to fail catastrophically. It can be quite harmful, especially in large motors, and it can develop as a result of manufacturing faults, repeated starting of the machine, mechanical stress, and thermal stress. Hence, a review on rotor defect diagnosis was conducted. In order to confirm rotor bar fracture, this research provides probable defect signatures that can be extracted from the current signal. Each defect signature is reported according to (a) loading level, (b) the number of BRBs, (c) validation, and (d) methodologies.

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13

Trujillo Guajardo, Luis Alonso, Miguel Angel Platas Garza, Johnny Rodríguez Maldonado, Mario Alberto González Vázquez, Luis Humberto Rodríguez Alfaro, and Fernando Salinas Salinas. "Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors." Energies 15, no. 10 (May 11, 2022): 3513. http://dx.doi.org/10.3390/en15103513.

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This article presents an evaluation of Prony method and its implementation considerations for motor current signal analysis diagnostics in rotor cage induction motors. The broken rotor bar fault signature in current signals is evaluated using Prony method, where its advantages in comparison with fast Fourier transform are presented. The broken rotor bar fault signature could occur during the life cycle operation of induction motors, so that is why an effective early detection estimation technique of this fault could prevent an insulation failure or heavy damage, leaving the motor out of service. First, an overview of cage winding defects in rotor cage induction motors is presented. Next, Prony method and its considerations for the implementation in current signature analysis are described. Then, the performance of Prony method using numerical simulations is evaluated. Lastly, an assessment of Prony method as a tool for current signal analysis diagnostics is performed using a laboratory test system where real signals of an induction motor with broken rotor bar operated with/without a variable frequency drive are analyzed. The summary results of the estimation (amplitudes and frequencies) are presented in the results and discussion section.

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14

Kutskir, H. "CURRENT METHODS OF SIGNATURES’ TECHNICAL FORGING WITH THE USE OF TECHNICAL MEANS." Theory and Practice of Forensic Science and Criminalistics 21, no. 1 (December 15, 2020): 309–17. http://dx.doi.org/10.32353/khrife.1.2020_20.

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Taking into account a significant scientific and technological progress, use of modern monochrome or multicolor photocopiers in the office administration, and the increase in this regard in the number of signatures received for examination by forensic expert units, relevance for continuing to study signatures as one of the essential requisites of many types of documents and methods of their technical forgery becomes obvious. The purpose of the article is to highlight certain issues of current methods of signatures technical forgery. Research methods: general (formal-logical methods of cognition: analysis, synthesis, generalization, analogy), general scientific (observation, measurement, description, comparison), special (visual, microscopic, photographic). Since the fact of handwritten signature execution possesses legal value, it often becomes the object of forgery. For this purpose, a variety of techniques is used. Criminals often resort to the so-called technical forgery of signatures on documents (performing signatures on behalf of another person using various techniques and technical means which helps to achieve the greatest similarity with the original) along with the usual graphic forgery. The essence of signatures technical forgery usually lies in mechanical copying, instead of imitating features of writing-motor skills and the signature-original. By such a signature, you can not measure the level of writing-motor skills of the performer. Each of the methods of signature technical forgery leaves in the signature the signs inherent only to this method. Studying such methods and their features collectively will allow to diagnose the fact of handwritten or technical execution of a signature with a sufficiently high degree of probability.

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15

Luo, Yin, Shouqi Yuan, Jianping Yuan, and Hui Sun. "Induction motor current signature for centrifugal pump load." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 11 (May 4, 2015): 1890–901. http://dx.doi.org/10.1177/0954406215585596.

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Sensorless monitoring technology based on motor current signature analysis is a nonintrusive and economical technique to monitor motor-driven equipment. Sensorless monitoring technology can be applied to a centrifugal pump system. This technology is also based on the motor current signature of centrifugal pump load; however, systematic research regarding motor current signature in overall normal operation points which is the applied basic for sensorless monitoring technology has been rarely performed. As such, we partially examined the motor current signature of a centrifugal pump load by experimental observation, theoretical analysis, and numerical simulation. Results show that stator current is a sinusoidal alternating current that strictly follows sine law associated with the cycle of the fundamental frequency of supply power. The trend of the root mean square and peak–peak of current is the same as flow–shaft power characteristics; hence, this trend could be used as indicator of the pump operational point monitor. The frequency characteristics of a centrifugal pump, such as blade passing frequency, rotation frequency, and broadband noise, could be reflected as sidebands around the fundamental frequency. The stator current spectrum is composed of fundamental frequency component, harmonics component, and noise. The fundamental frequency component is directly related to the pump load in which changes associated with the law of fundamental frequency component are relatively similar to flow–shaft power characteristics. Harmonics component and noise are caused by load fluctuation in which the amount of energy of these two components exhibits a lower value at the preferred operation point. By contrast, the amount of energy likely increases when pump operation is at an unstable operation point. These results further indicate that motor current signature analysis is a feasible and cost-effective method to monitor centrifugal pump operation status. Therefore, motor current signature analysis can be applied to monitor-related flow phenomena.

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16

Diao, Junjie, Yuan Tian, Yuelong Zhang, Shisheng Huang, Yijun Xin, and Yulong Guo. "Fault Detection and Diagnosis of Gear Train Based on Motor Current Signature Analysis." Journal of Physics: Conference Series 2364, no. 1 (November 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2364/1/012003.

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Abstract Gear fault diagnosis generally uses oscillation signals to extract fault features, but the oscillation identify technique is inconvenient to set up sensors and is easily affected by the surrounding and noise. The motor itself has the property of a sensor, and signals such as motor current is able to reflect the change of the load torque. This paper proposes a gear fault detection approach, which applies the Motor Current Signature Analysis (MCSA) to exploit spectral characteristics to recognize malfunction in servo motor drive gears. First, we calculate the nominal revolutions per minute (rpm) to explore frequencies of interest. Second, frequency bands where faulty signals would be appear are constructed. Next, we extract power spectral density (PSD) feature data. Finally, the paper computes spectral metrics for the frequency band of interest. By using two spectral metrics, gear health and failure data are clearly grouped in different areas of the scatter chart. The experimental results give evidence of the effectiveness of analyzing current characteristics of servo motors to classify fault and health data.

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17

Podduturi, Sai Sharath. "Condition Monitoring of Three Phase Induction Motor using Current Signature Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1946–55. http://dx.doi.org/10.22214/ijraset.2021.35491.

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In this paper we are going to see how Gabor transform is used to analyze the signal and to determine the inner and outer race of bearing faults by monitoring the condition of Induction motor using Motor Current Signature Analysis. Among the various faults bearing faults is the major problem, which cause a huge damage to induction motor, when unnoticed at developing stage. So, monitoring of bearing faults is very important and it can done by several conditions monitoring methods like thermal monitoring, vibration monitoring and more but these methods require expensive sensors or specified tools, whereas current monitoring methods doesn’t require any additional tools. Usually, this condition monitoring is used to detect the various faults like bearing faults, load faults by MCSA. If the fault is present in the motor, the frequency spectrum of the line current is different from healthy ones, the Gabor analysis detects the fault signature generated in the induction motor, by using mathematical expressions and calculate the RMS and Standard deviation values, these fault values are different from healthy ones. Through this we can identify faults.

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18

Eren, Levent, Murat Aşkar, and Michael J. Devaney. "Motor current signature analysis via four-channel FIR filter banks." Measurement 89 (July 2016): 322–27. http://dx.doi.org/10.1016/j.measurement.2016.04.025.

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19

Idowu, Peter, and Christopher Root. "Real-time motor current signature analysis tool for undergraduate laboratory." Computer Applications in Engineering Education 18, no. 4 (November 24, 2010): 634–39. http://dx.doi.org/10.1002/cae.20263.

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20

Krichen, Manel, Elhoussin Elbouchikhi, Naourez Benhadj, Mohamed Chaieb, Mohamed Benbouzid, and Rafik Neji. "Motor Current Signature Analysis-Based Permanent Magnet Synchronous Motor Demagnetization Characterization and Detection." Machines 8, no. 3 (June 29, 2020): 35. http://dx.doi.org/10.3390/machines8030035.

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Neodymium-boron (NdFeB) permanent magnets (PMs) have been widely studied in the past years since they became the material of choice in permanent magnet synchronous machines (PMSMs). Although NdFeB PMs have a better energy density than other types of magnets and are cost-effective, their magnetization is very sensitive to the PMSM operating conditions, in particular temperature, where the irreversible demagnetization degree increases over time. Therefore, it is important to characterize and diagnose demagnetization at an early stage. In this context, this paper proposes a two-step analysis study dealing with both uniform and partial demagnetization. A 2D finite element method-based (FEM) approach is used for demagnetization characterization, and then a PMSM motor current signature analysis (MCSA) approach, based on fast Fourier transform (FFT), is considered where fault cases harmonics are considered as faults indices to detect demagnetization. In some situations, the proposed two-step approach achieved results that clearly allow distinguishing and characterizing demagnetization. Indeed, a local demagnetization introduces specific sub-harmonics while a uniform demagnetization leads to the current amplitude increase for a given torque.

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21

Seera, Manjeevan, Chee Peng Lim, and Dahaman Ishak. "Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network." International Journal of Natural Computing Research 3, no. 1 (January 2012): 44–55. http://dx.doi.org/10.4018/jncr.2012010104.

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In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

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22

Prakasam, K., and S. Ramesh. "Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis." Circuits and Systems 07, no. 09 (2016): 2651–62. http://dx.doi.org/10.4236/cs.2016.79229.

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23

Ágoston, Katalin. "Studying and Simulating the Influence of the Rotor Fault on Stator Current." Acta Marisiensis. Seria Technologica 17, no. 1 (June 1, 2020): 17–21. http://dx.doi.org/10.2478/amset-2020-0004.

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AbstractThis paper presents fault detection techniques, especially the motor current signature analysis (MCSA) which consists of the phase current measurement of the electrical motor’s stator and/or rotor. The motor current signature analysis consists in determining the frequency spectrum (FFT) of the stator current signal and evaluating the relative amplitude of the current harmonics. Sideband frequencies appear in the frequency spectrum of the current, corresponding to each fault. The broken bar is a frequent fault in induction motors with squirrel-cage rotor. It is presented the equivalent circuit for induction motors and the equivalence between the squirrel-cage rotor and the rotor windings. It is also presented an equivalent circuit model for induction motors with squirrel cage rotor, and based on this a Simulink model was developed. It is shown how a broken rotor bar influences the magnetic field around the rotor and through this the stator current. This modification is highlighted through the developed model.

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24

Yang, Jiang Tian, Wen Yu Zhao, and Jay Lee. "Stator Current-Based Locomotive Traction Motor Bearing Fault Detection." Advanced Materials Research 819 (September 2013): 186–91. http://dx.doi.org/10.4028/www.scientific.net/amr.819.186.

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Rolling-element bearings are critical components in locomotive traction motors. A reliable online bearing fault-diagnostic technique is critically needed to prevent motor systems performance degradation and malfunction. Motor bearing failure induces vibration, resulting in the modulation of the stator current. Compared with conventional monitoring techniques such as vibration monitoring or temperature monitoring, stator current-based monitoring offers significant economic benefits and implementation advantages. In this paper, a novel approach to locomotive traction motor current signature analysis based on wavelet packet decomposition (WPD) of stator current is presented. The effectiveness and practicability of the proposed method is verified by locomotive running tests.

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25

Jung, Junyeong, Yonghyun Park, Sang Bin Lee, Chang-Hee Cho, Kwonhee Kim, Ernesto J. Wiedenbrug, and Mike Teska. "Monitoring Journal-Bearing Faults: Making Use of Motor Current Signature Analysis for Induction Motors." IEEE Industry Applications Magazine 23, no. 4 (July 2017): 12–21. http://dx.doi.org/10.1109/mias.2016.2600725.

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26

Pillay, P., and Z. Xu. "Labview implementation of speed detection for mains-fed motors using motor current signature analysis." IEEE Power Engineering Review 18, no. 6 (June 1998): 47–48. http://dx.doi.org/10.1109/mper.1998.1236742.

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27

Sun, Hui, Shouqi Yuan, Yin Luo, Yihang Guo, and Jiangnan Yin. "Unsteady characteristics analysis of centrifugal pump operation based on motor stator current." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 231, no. 8 (May 19, 2017): 689–705. http://dx.doi.org/10.1177/0957650917710554.

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Motor current signature analysis technology is a cost-effective, non-intrusive and reliable technique used for monitoring of motor-driven devices. In this method, the motor is regarded as a torque sensor. Theoretical analysis, numerical simulation, and experiment were conducted to do research on time–frequency analysis of current signature for unsteady characteristic extraction of pump operation. Results show that most energy of current is concentrated on the first order of Intrinsic Mode Function (IMF) component. Torque related oscillation could be reflected by energy amplitude and its variation. The root mean square and kurtosis value of transient energy in Hilbert spectrum could be regarded as the indicators of centrifugal pump operation point detection. Hilbert marginal spectrum could depict the stability of centrifugal pump operation and suggest the proper operation points to ensure high efficiency and reliability.

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28

Richards, Reginald D. "Acoustic motor current signature analysis system with audio amplified speaker output." Journal of the Acoustical Society of America 104, no. 1 (July 1998): 26. http://dx.doi.org/10.1121/1.424022.

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29

Sivertsen, L., B. T. Hjertaker, T. E. Kjenner, and S. Stjernberg. "Condition Monitoring of Subsea Electrical Equipment Using Motor Current Signature Analysis." EPE Journal 22, no. 1 (March 2012): 28–36. http://dx.doi.org/10.1080/09398368.2012.11463815.

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30

Prakash, R. B. R., Madhusudana rao Ranga, A. Pandian, and P. Srinivasa Varma. "Induction Machine Stator winding Failure Detection Using Motor Current Signature Analysis." IOP Conference Series: Materials Science and Engineering 993 (December 31, 2020): 012084. http://dx.doi.org/10.1088/1757-899x/993/1/012084.

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31

Singh, Sukhjeet, Amit Kumar, and Navin Kumar. "Motor Current Signature Analysis for Bearing Fault Detection in Mechanical Systems." Procedia Materials Science 6 (2014): 171–77. http://dx.doi.org/10.1016/j.mspro.2014.07.021.

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32

Kar, Chinmaya, and A. R. Mohanty. "Monitoring gear vibrations through motor current signature analysis and wavelet transform." Mechanical Systems and Signal Processing 20, no. 1 (January 2006): 158–87. http://dx.doi.org/10.1016/j.ymssp.2004.07.006.

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33

Douglas, H., P. Pillay, and A. K. Ziarani. "A New Algorithm for Transient Motor Current Signature Analysis Using Wavelets." IEEE Transactions on Industry Applications 40, no. 5 (September 2004): 1361–68. http://dx.doi.org/10.1109/tia.2004.834130.

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34

Wang, Jin Jiang, Robert X. Gao, and Ru Qiang Yan. "Concordia Transform-Based Current Analysis for Induction Motor Diagnosis." Key Engineering Materials 569-570 (July 2013): 481–88. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.481.

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This paper presents a new approach for bearing defect diagnosis in induction motor by taking advantage of three-phase stator current analysis based on Concordia transform. The current signature caused by bearing defect is firstly analyzed using an analytic model. Concordia transform is performed to extract the instantaneous frequency based on phase demodulation. The bearing defect feature is then identified via spectrum analysis of the variation of current instantaneous frequency. Both simulation and experimental studies are performed to demonstrate the effectiveness of proposed method in identifying bearing defects. The method is inherently low cost, non-invasive, and computational efficient, making it a good candidate for various applications.

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35

Firdhana, Niko Riza, Tejo Sukmadi, and Karnoto Karnoto. "ANALISIS KERUSAKAN BATANG ROTOR PADA MOTOR INDUKSI TIGA FASA MENGGUNAKAN METODE MOTOR CURRENT SIGNATURE ANALYSIS." Transmisi 19, no. 4 (November 14, 2017): 168. http://dx.doi.org/10.14710/transmisi.19.4.168-176.

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36

Lau, Enzo C. C., and H. W. Ngan. "Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis." IEEE Transactions on Instrumentation and Measurement 59, no. 10 (October 2010): 2683–90. http://dx.doi.org/10.1109/tim.2010.2045927.

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37

Babu, G. Sreedhar, A. Lingamurthy, and A. S. Sekhar. "Condition monitoring of brushless DC motor-based electromechanical linear actuators using motor current signature analysis." International Journal of Condition Monitoring 1, no. 1 (June 1, 2011): 20–32. http://dx.doi.org/10.1784/204764211798089066.

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38

Zhongming Ye, Bin Wu, and A. Sadeghian. "Current signature analysis of induction motor mechanical faults by wavelet packet decomposition." IEEE Transactions on Industrial Electronics 50, no. 6 (December 2003): 1217–28. http://dx.doi.org/10.1109/tie.2003.819682.

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39

Choi, ByeongKeun, HakEun Kim, DongSik Gu, HyoJung Kim, and HanEul Jeong. "Diagnosis of cryogenic pump-motor systems using vibration and current signature analysis." Journal of Mechanical Science and Technology 20, no. 7 (July 2006): 972–80. http://dx.doi.org/10.1007/bf02915996.

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Idowu, P., J. Atiyeh, E. Schmitt, and A. Morales. "A Matlab® Tool for Introducing Basics of Induction Motor Current Signature (IMCS) Analysis." International Journal of Electrical Engineering & Education 47, no. 1 (January 2010): 1–10. http://dx.doi.org/10.7227/ijeee.47.1.1.

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Induction machines are among the most widely used devices in industrial processes because they are robust and well suited for a wide range of applications. This critical role underscores the level of attention given to the early detection of potentially damaging faults. Given this important role, one would expect that undergraduate curricula in electrical engineering would devote some attention to the subject, but this is typically not the case. This paper presents an innovative way of presenting induction motor current signature (IMCS) analysis to undergraduate students within very limited time constraints. The signature analysis tool is developed in Matlab® and features two diagnostic methods. It offers electrical engineering undergraduates a very convenient environment in which to learn the basics of induction motor fault diagnosis.

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Kim, Ki Dong, and Young Il Kim. "Predictive Maintenance and Fault Diagnosis of Three-Phase Induction Motor Using MCSA(Motor Current Signature Analysis)." Korean Journal of Air-Conditioning and Refrigeration Engineering 33, no. 12 (December 31, 2021): 656–69. http://dx.doi.org/10.6110/kjacr.2021.33.12.656.

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Areias, Borges da Silva, Bonaldi, de Lacerda de Oliveira, Lambert-Torres, and Bernardes. "Evaluation of Current Signature in Bearing Defects by envelope analysis of the vibration in induction motors." Energies 12, no. 21 (October 23, 2019): 4029. http://dx.doi.org/10.3390/en12214029.

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

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Dhomad, Thoalfaqqar Ali, and Alaa Abdulhady Jaber. "Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network." International Journal on Advanced Science, Engineering and Information Technology 10, no. 1 (February 8, 2020): 70. http://dx.doi.org/10.18517/ijaseit.10.1.10629.

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Mohanty, Amiya Ranjan, Prasanta Kumar Pradhan, Nitaigour P. Mahalik, and Sabyasachi G. Dastidar. "Fault detection in a centrifugal pump using vibration and motor current signature analysis." International Journal of Automation and Control 6, no. 3/4 (2012): 261. http://dx.doi.org/10.1504/ijaac.2012.051884.

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Kar, Chinmaya, and A. R. Mohanty. "Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov–Smirnov test." Journal of Sound and Vibration 290, no. 1-2 (February 2006): 337–68. http://dx.doi.org/10.1016/j.jsv.2005.04.020.

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Azamfar, Moslem, Xiaodong Jia, Vibhor Pandhare, Jaskaran Singh, Hoseein Davari, and Jay Lee. "Detection and diagnosis of bottle capping failures based on motor current signature analysis." Procedia Manufacturing 34 (2019): 840–46. http://dx.doi.org/10.1016/j.promfg.2019.06.165.

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Pita, H., G. Zurita, and A. Villarroel. "Software development firmware system for broken rotor bar detection and diagnosis of induction motor through current signature analysis." Journal of Mechanical Engineering and Sciences 14, no. 2 (June 23, 2020): 6917–33. http://dx.doi.org/10.15282/jmes.14.2.2020.30.0542.

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The induction motors (IMs) are undoubtedly the most used machines in industries because of the advantages they offer such as simplicity, service continuity and low cost. Due to wear and tear, the motor suffers different types of mechanical and electrical failures. Depending on the criticality of the plant motors, it could be necessary to implement predictive techniques in order to detect the faults before they can cause unnecessary downtime. Therefore, in this paper, the research approach was to develop a low cost measurement system based on a micro controller platform for machine diagnosis. The FRDM K64F developing board was selected as the most suitable for satisfying the system conditions, and it was used to collect induction motor`s current data. In order to validate the accuracy of the developed system, the Frequency Transfer Functions (FRF) of the developed measurement system and the standard system (NI USB-6009) were compare. It showed a flat frequency spectrum from 0 to 1 KHz, with small fluctuations of about 0.25 dB standard deviation. A fully automated test bench was implemented, which allows to perform all the measurement tests with the IMs, and in this case, the detection and diagnosis of broken bars. Around 240 tests were performed with varying loads, different rotation speeds, and with different severity damage levels in the rotor. The data analysis procedure for broken rotor bar detection and motor diagnosis was performed by the Motor Current Signature Analysis (MCSA), FFT and Enveloped Analysis (EA). Finally, the research approach was successfully accomplished, by the team by developing a software firmware measurement ultra-low cost development platform for machine diagnosis. It was also developed a proper antialiasing filter to reduce industrial noise. The effectiveness of the proposed system is detecting a weak fault in a noise signal. It was found out a new consistent and robust parameter called the pole pass frequency (fpsf), which could be used as a diagnosis parameter for detection of broken rotor bars faults, with their damage severity degree. The detected parameter can be found around 2.6 Hz, and it increases in amplitude with increasing damage severity.

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Qin, Guo Jun, Jun Yao Li, Jian Mei Chen, and Niao Qing Hu. "Motor Faults Detection Based on Vector Transformation and Amplitude Demodulation." Applied Mechanics and Materials 628 (September 2014): 219–24. http://dx.doi.org/10.4028/www.scientific.net/amm.628.219.

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A method to analyze and detect the features of rotor and stator asymmetric faults for AC motor is put forward in this paper. After modeling the damaged asynchronous motor, stator’s three-phase current signals of the motor at normal status, as well as that with stator and rotor bar asymmetric faults, are simulated and analyzed qualitatively. In view of the shortcomings of Park’s vector transformation in the analyzing motor current signature, a method is put forward by combining it with signal amplitude demodulation. Based on this new method, the asymmetric features are expected to be extracted completely through the amplitude relative normalization and spectrum analysis. Finally, the availability of this method is verified by detecting and diagnosing the faults in actual AC motors.

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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 (December 24, 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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Halem, Noura, Kamel Srairi, and Salah Eddine Zouzou. "Stator Current Signature Analysis of Healthy Induction Motor Using Time Stepping Finite Element Method." International Journal on Electrical Engineering and Informatics 6, no. 1 (March 30, 2014): 144–54. http://dx.doi.org/10.15676/ijeei.2014.6.1.10.

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