The Experts below are selected from a list of 16872 Experts worldwide ranked by ideXlab platform
Adam Glowacz - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of single Phase Induction Motor based on acoustic signals
Mechanical Systems and Signal Processing, 2019Co-Authors: Adam GlowaczAbstract:Abstract The paper presents description of bearing, stator and rotor fault diagnostic methods of a single-Phase Induction Motor. The presented methods use acoustic signals. Five states of the single-Phase Induction Motor were analysed: healthy Motor, Motor with shorted coils of auxiliary winding and main winding, Motor with shorted coils of auxiliary winding, Motor with broken rotor bar and faulty ring of squirrel-cage, Motor with faulty bearing. A method of feature extraction of acoustic signals – SMOFS-22-MULTIEXPANDED (Shortened Method of Frequencies Selection Multiexpanded) was developed and implemented. The SMOFS-22-MULTIEXPANDED was implemented as feature extraction method of acoustic signals. Classification step was performed using the NN (the Nearest Neighbour) classifier. The proposed methods had good results for diagnosis of bearing, stator and rotor faults of the single-Phase Induction Motor. The developed approach can find applications for fault diagnosis of other types of rotating machines.
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acoustic based fault diagnosis of three Phase Induction Motor
Applied Acoustics, 2018Co-Authors: Adam GlowaczAbstract:Abstract The article describes acoustic based fault diagnosis techniques of a three-Phase Induction Motor. Four real states of the three-Phase Induction Motor were analysed: healthy three-Phase Induction Motor, three-Phase Induction Motor with broken rotor bar, three-Phase Induction Motor with 2 broken rotor bars, three-Phase Induction Motor with faulty ring of squirrel-cage. Two feature extraction methods of acoustic signals of the Induction Motor - SMOFS-32-MULTIEXPANDED-2-GROUPS (Shortened Method of Frequencies Selection Multiexpanded 2 Groups) and SMOFS-32-MULTIEXPANDED-1-GROUP were described. The Nearest Neighbour classifier, backpropagation neural network and modified classifier based on words coding were used for recognition of acoustic signals. Results of recognition were very good for the real data and developed fault diagnosis techniques based on acoustic signals. The described fault diagnosis approach can find applications in the industry.
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early fault diagnosis of bearing and stator faults of the single Phase Induction Motor using acoustic signals
Measurement, 2018Co-Authors: Adam Glowacz, Z Glowacz, W Glowacz, J KozikAbstract:Abstract An article describes an early fault diagnostic technique based on acoustic signals. The presented technique was used for a single-Phase Induction Motor. The authors measured and analysed following states of the Motor: healthy single-Phase Induction Motor, single-Phase Induction Motor with faulty bearing, single-Phase Induction Motor with faulty bearing and shorted coils of auxiliary winding. A feature extraction method called MSAF-20-MULTIEXPANDED (Method of Selection of Amplitudes of Frequency – Multiexpanded) was discussed. The MSAF-20-MULTIEXPANDED was used to create feature vectors. The obtained vectors were classified by NN (Nearest Neighbour classifier), NM (Nearest Mean classifier) and GMM (Gaussian Mixture Models). The proposed technique can be used for diagnosis of the single-Phase Induction Motors. It can be also used for other types of rotating electric Motors.
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diagnosis of the three Phase Induction Motor using thermal imaging
Infrared Physics & Technology, 2017Co-Authors: Adam Glowacz, Z GlowaczAbstract:Abstract Three-Phase Induction Motors are used in the industry commonly for example woodworking machines, blowers, pumps, conveyors, elevators, compressors, mining industry, automotive industry, chemical industry and railway applications. Diagnosis of faults is essential for proper maintenance. Faults may damage a Motor and damaged Motors generate economic losses caused by breakdowns in production lines. In this paper the authors develop fault diagnostic techniques of the three-Phase Induction Motor. The described techniques are based on the analysis of thermal images of three-Phase Induction Motor. The authors analyse thermal images of 3 states of the three-Phase Induction Motor: healthy three-Phase Induction Motor, three-Phase Induction Motor with 2 broken bars, three-Phase Induction Motor with faulty ring of squirrel-cage. In this paper the authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences). This method compares many training sets together and it selects the areas with the biggest changes for the recognition process. Feature vectors are obtained with the use of mentioned MoASoID and image histogram. Next 3 methods of classification are used: NN (the Nearest Neighbour classifier), K-means, BNN (the back-propagation neural network). The described fault diagnostic techniques are useful for protection of three-Phase Induction Motor and other types of rotating electrical Motors such as: DC Motors, generators, synchronous Motors.
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diagnosis of stator faults of the single Phase Induction Motor using acoustic signals
Applied Acoustics, 2017Co-Authors: Adam Glowacz, Z GlowaczAbstract:Abstract An early diagnosis of faults prevents financial loss and downtimes in the industry. In this paper the authors presented the early fault diagnostic technique of stator faults of the single-Phase Induction Motor. The proposed technique was based on recognition of acoustic signals. The authors measured and analysed 3 states of the single-Phase Induction Motor: a healthy single-Phase Induction Motor, a single-Phase Induction Motor with shorted coils of auxiliary winding, a single-Phase Induction Motor with shorted coils of auxiliary winding and main winding. In this paper an original method of feature extraction called MSAF-RATIO30-MULTIEXPANDED (Method of Selection of Amplitudes of Frequency - Ratio 30% of maximum of amplitude Multiexpanded) was described. This method was used to form feature vectors. A classification of obtained vectors was performed by the KNN (K-Nearest Neighbour classifier), the K-Means clustering and the Linear Perceptron. The early fault diagnostic technique can find application for protection of the single-Phase Induction Motors. It can be also used for other rotating electrical machines.
Gu Jian-xin - One of the best experts on this subject based on the ideXlab platform.
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RAM - Modeling for A Dual Three-Phase Induction Motor Based On A Winding Transformation
2008 IEEE Conference on Robotics Automation and Mechatronics, 2008Co-Authors: Wang Bu-lai, Gong Zhe-song, Gu Jian-xinAbstract:A novel mathematic model for a dual three-Phase Induction Motor was proposed. A set of equivalent three-Phase windings was built based on same magnetomotive force and power. A dual three-Phase Induction Motor was substituted equivalently by a three-Phase Induction Motor after a winding transformation. Then with the mathematical model of the three-Phase Phase Induction Motor in the stationary reference frame, the model of a dual three-Phase Induction Motor was determined. A 4 pole dual three-Phase Induction Motor was simulated and tested. Compared the simulation results with corresponding test results, the maximum error was lower than 2%. So this winding transformation and modeling method are validated and correct.
S Chandra Kiran - One of the best experts on this subject based on the ideXlab platform.
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Proposed Vector-Controlled Two-Phase Induction Motor as A Replacement for Single Phase Induction Motor
International Journal of Research, 2016Co-Authors: Eeram Krishna Murthy, S Chandra KiranAbstract:This paper presents vector controlled of single Phase Induction Motor. some problems are with vector controlled SPIM.As SPIM’s are typically to maintain speed and also about the complex implementation of vector controlled SPIM.the implemantion of the proposed vector controlled TPIM compared to the vector controlled SPIM. The general modal sutable for vector control of the unsymmentrical two Phase Induction Motor and also stator flux oriented controlled strategies are analized. the comparative performance of both has been presented in this work with help of a practical three Phase Motor.
A J M Cardoso - One of the best experts on this subject based on the ideXlab platform.
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fault tolerant operating strategies applied to three Phase Induction Motor drives
IEEE Transactions on Industrial Electronics, 2006Co-Authors: A M S Mendes, A J M CardosoAbstract:This paper presents a comparative analysis involving several fault-tolerant operating strategies, applied to three-Phase Induction-Motor drives, that intend to compensate for inverter faults. The results presented show the advantages and the inconveniences of several fault-tolerant drive structures, under different control techniques, such as the field-oriented control and the direct torque control. Experimental results concerning the performance of the three-Phase Induction Motor, based on the analysis of some key parameters, like Induction-Motor efficiency, Motor power factor, and harmonic distortion of both Motor line currents and Phase voltages, will be presented
Wang Bu-lai - One of the best experts on this subject based on the ideXlab platform.
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RAM - Modeling for A Dual Three-Phase Induction Motor Based On A Winding Transformation
2008 IEEE Conference on Robotics Automation and Mechatronics, 2008Co-Authors: Wang Bu-lai, Gong Zhe-song, Gu Jian-xinAbstract:A novel mathematic model for a dual three-Phase Induction Motor was proposed. A set of equivalent three-Phase windings was built based on same magnetomotive force and power. A dual three-Phase Induction Motor was substituted equivalently by a three-Phase Induction Motor after a winding transformation. Then with the mathematical model of the three-Phase Phase Induction Motor in the stationary reference frame, the model of a dual three-Phase Induction Motor was determined. A 4 pole dual three-Phase Induction Motor was simulated and tested. Compared the simulation results with corresponding test results, the maximum error was lower than 2%. So this winding transformation and modeling method are validated and correct.