Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645699
M. Pineda-Sánchez, J. Pérez-Cruz, J. Roger-Folch, M. Riera-Guasp, Á. Sapena-Bañó, R. Puche-Panadero
On-line diagnosis of induction motors faults requires special, high speed hardware, such as DSP or FPGAs. Practical implementation of diagnosis algorithms in such a device must take into account the limited amount of memory available for storing sampled data, and for performing spectral analysis using the FFT. Another practical problem is the need to filter the mains component, whose leakage can hide fault harmonics, prior to compute the FFT of the current's signal. This requires the use of digital filters, that must be tuned in case of using variable speed drives that can operate the motor at different speeds. In this paper, an advanced demodulation technique that is able to eliminate the mains component with an extremely low memory requirement, based on the Teager- Kaiser energy operator, is presented. The demodulated current is footprint is down sampled, so that only 2kb of memory are needed to perform the diagnosis process. The proposed method is implemented in a DSP commercial board online diagnosis system and tested on commercial induction motors with broken bars. Finally, the results are compared with the results obtained offline using conventional Motor Current Signature Analysis method.
{"title":"Diagnosis of induction motor faults using a DSP and advanced demodulation techniques","authors":"M. Pineda-Sánchez, J. Pérez-Cruz, J. Roger-Folch, M. Riera-Guasp, Á. Sapena-Bañó, R. Puche-Panadero","doi":"10.1109/DEMPED.2013.6645699","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645699","url":null,"abstract":"On-line diagnosis of induction motors faults requires special, high speed hardware, such as DSP or FPGAs. Practical implementation of diagnosis algorithms in such a device must take into account the limited amount of memory available for storing sampled data, and for performing spectral analysis using the FFT. Another practical problem is the need to filter the mains component, whose leakage can hide fault harmonics, prior to compute the FFT of the current's signal. This requires the use of digital filters, that must be tuned in case of using variable speed drives that can operate the motor at different speeds. In this paper, an advanced demodulation technique that is able to eliminate the mains component with an extremely low memory requirement, based on the Teager- Kaiser energy operator, is presented. The demodulated current is footprint is down sampled, so that only 2kb of memory are needed to perform the diagnosis process. The proposed method is implemented in a DSP commercial board online diagnosis system and tested on commercial induction motors with broken bars. Finally, the results are compared with the results obtained offline using conventional Motor Current Signature Analysis method.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645701
N. Jirasuwankul, C. Manop
This paper presents a technique of induction motor's efficiency monitoring based on air-gap power and rotor loss estimation by Kalman filtering. A simplified model of three phase induction motor, with equivalent circuit of five elements, has been tested by the proposed technique with varying load torque and power to represent practical operations. Good agreement between the simulation and experimental results are found in a normal operating range of load torque and power.
{"title":"Air-gap power and rotor loss estimation for induction motor efficiency monitoring based on Kalman filtering","authors":"N. Jirasuwankul, C. Manop","doi":"10.1109/DEMPED.2013.6645701","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645701","url":null,"abstract":"This paper presents a technique of induction motor's efficiency monitoring based on air-gap power and rotor loss estimation by Kalman filtering. A simplified model of three phase induction motor, with equivalent circuit of five elements, has been tested by the proposed technique with varying load torque and power to represent practical operations. Good agreement between the simulation and experimental results are found in a normal operating range of load torque and power.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127935437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645730
D. A. Asfani, M. Purnomo, D. Sawitri
This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.
{"title":"Naïve Bayes classifier for temporary short circuit fault detection in stator winding","authors":"D. A. Asfani, M. Purnomo, D. Sawitri","doi":"10.1109/DEMPED.2013.6645730","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645730","url":null,"abstract":"This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"46 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645768
M. Delgado, G. Cirrincione, A. Garcia Espinosa, J. Ortega, H. Henao
Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.
{"title":"Dedicated hierarchy of neural networks applied to bearings degradation assessment","authors":"M. Delgado, G. Cirrincione, A. Garcia Espinosa, J. Ortega, H. Henao","doi":"10.1109/DEMPED.2013.6645768","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645768","url":null,"abstract":"Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127144969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645746
R. Sedlácek, J. Vedral, J. Tomlain
This paper describes a hardware design of a fully digital wideband PD meter based on application of FPGA as well as design of coupling device required for PD measurements. The designed coupling device has frequency bandwidth of 1 kHz-10 MHz. The PD signal is digitalized by a fast 14-bit AID convertor sampling at frequency of 50 MSa/s. The digital samples of PD signal are read by the FPGA, subsequently filtered by a number of digital FIR filter banks and stored in a 32 MB DDR memory. On request from PC software, the FPGA send samples in reduced form through Ethernet interface for the next signal processing and evaluation all important parameters of PD analysis. The paper also describes a design of smart charge calibrator especially developed for the PD meter testing and calibration.
{"title":"A wideband partial discharge meter using FPGA","authors":"R. Sedlácek, J. Vedral, J. Tomlain","doi":"10.1109/DEMPED.2013.6645746","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645746","url":null,"abstract":"This paper describes a hardware design of a fully digital wideband PD meter based on application of FPGA as well as design of coupling device required for PD measurements. The designed coupling device has frequency bandwidth of 1 kHz-10 MHz. The PD signal is digitalized by a fast 14-bit AID convertor sampling at frequency of 50 MSa/s. The digital samples of PD signal are read by the FPGA, subsequently filtered by a number of digital FIR filter banks and stored in a 32 MB DDR memory. On request from PC software, the FPGA send samples in reduced form through Ethernet interface for the next signal processing and evaluation all important parameters of PD analysis. The paper also describes a design of smart charge calibrator especially developed for the PD meter testing and calibration.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126084428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645716
F. Villalobos-Pina, R. Álvarez-Salas, E. Cabal-Yépez, A. García-Perez
This paper presents a comparative validation for electric stator and rotor faults through an induction-motor modeling utilizing Park Instantaneous Space Phasor (ISP) during stator current analysis and fast Fourier Transform (FFT2) to identify the fault spectrum and the band spectral density of wavelet coefficients using multi-resolution analysis (MRA). The spectral analysis identifies the fault signature modifying the sample frequency in the data acquisition system. The wavelet analysis maintains a constant sample frequency using MRA, which provides redundant information to identify the faults. Furthermore, the MRA analysis of ISP stator currents helps to identify small incipient faults choosing a threshold between the healthy and faulty machine. The cases considered are stator and rotor electric faults, which are modeled by means of parametric variations. In Spite of its simplicity, the model provides useful information for fault identification.
{"title":"Induction motor model validation using fast fourier transform and wavelet tools","authors":"F. Villalobos-Pina, R. Álvarez-Salas, E. Cabal-Yépez, A. García-Perez","doi":"10.1109/DEMPED.2013.6645716","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645716","url":null,"abstract":"This paper presents a comparative validation for electric stator and rotor faults through an induction-motor modeling utilizing Park Instantaneous Space Phasor (ISP) during stator current analysis and fast Fourier Transform (FFT2) to identify the fault spectrum and the band spectral density of wavelet coefficients using multi-resolution analysis (MRA). The spectral analysis identifies the fault signature modifying the sample frequency in the data acquisition system. The wavelet analysis maintains a constant sample frequency using MRA, which provides redundant information to identify the faults. Furthermore, the MRA analysis of ISP stator currents helps to identify small incipient faults choosing a threshold between the healthy and faulty machine. The cases considered are stator and rotor electric faults, which are modeled by means of parametric variations. In Spite of its simplicity, the model provides useful information for fault identification.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125885500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645763
M. Salehifar, M. Moreno-Eguilaz, V. Sala, Ramin Salehi Arashloo, L. Romeral
A new open switch fault detection method based on normalized current analysis is proposed for application in multiphase fault tolerant PMSM drives. Performance characteristics of proposed method are single diagnostic variable, ability to detect open phase fault without using auxiliary variable, ability to detect multiple switch fault, simple diagnostic variable, generality, and robustness in case of high unbalanced current waveforms. Theory of diagnostic method with special multiphase drive application is developed; simulation results using Matlab/Simulink and experimental waveforms are shown to validate effectiveness of the presented fault detection method.
{"title":"Improved open switch fault detection based on normalized current analysis in multiphase fault tolerant converters","authors":"M. Salehifar, M. Moreno-Eguilaz, V. Sala, Ramin Salehi Arashloo, L. Romeral","doi":"10.1109/DEMPED.2013.6645763","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645763","url":null,"abstract":"A new open switch fault detection method based on normalized current analysis is proposed for application in multiphase fault tolerant PMSM drives. Performance characteristics of proposed method are single diagnostic variable, ability to detect open phase fault without using auxiliary variable, ability to detect multiple switch fault, simple diagnostic variable, generality, and robustness in case of high unbalanced current waveforms. Theory of diagnostic method with special multiphase drive application is developed; simulation results using Matlab/Simulink and experimental waveforms are shown to validate effectiveness of the presented fault detection method.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121664499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEMPED.2013.6645725
V. Fireteanu, Alexandru-Ionel Constantin, R. Romary, R. Pusca, S. Ait-Amar
Based on a time domain finite element analysis of the electromagnetic field, the paper studies effects of the short-circuit fault in the stator winding of an induction motor and the influence of this fault on the magnetic field outside the motor. The detection of the short-circuit fault through the magnetic field in the motor neighboring is based on the comparison of harmonics of the output voltage of coil sensors in the healthy and faulty motor states. The influence of the motor frame on the efficiency of fault detection is studied.
{"title":"Finite element investigation of the short-circuit fault in the stator winding of induction motors and harmonics of the neighboring magnetic field","authors":"V. Fireteanu, Alexandru-Ionel Constantin, R. Romary, R. Pusca, S. Ait-Amar","doi":"10.1109/DEMPED.2013.6645725","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645725","url":null,"abstract":"Based on a time domain finite element analysis of the electromagnetic field, the paper studies effects of the short-circuit fault in the stator winding of an induction motor and the influence of this fault on the magnetic field outside the motor. The detection of the short-circuit fault through the magnetic field in the motor neighboring is based on the comparison of harmonics of the output voltage of coil sensors in the healthy and faulty motor states. The influence of the motor frame on the efficiency of fault detection is studied.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116541701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-24DOI: 10.1109/DEMPED.2013.6645733
A. Mabrouk, S. Zouzou, M. Sahraoui, S. Khelif
Diagnosis of electrical machines are becoming more and more important issues in the field of electrical machines as new data processing technique. Motor Current Stator Analysis (MCSA) are usually used to detect the broken bars. In several industrial applications, the motor is subjected to load torque variations of low frequencies, which have effects similar to rotor faults in the current spectrum and result of diagnostic procedure may be ambigues. Discriminating rotor cage fault from oscillating load effects in Induction motors must be considered. In this paper, we present a study based on the application of the active and reactive power signature analyses for discriminating broken rotor bars from mechanical load oscillation effects in operating three-phase squirrel cage induction motors. This method is attractive because it does not need to interrupt the operating system. Finite element method was used to perform dynamical simulation, which leads to more precise results than other models, as the reel geometry and winding layout of the machine are used. The computer simulations and laboratory experiments results show the interest and the efficiency of this technique for the correct distinction between broken rotor bars and load oscillations.
{"title":"Discriminating time-varying loads and rotor cage fault in induction motors","authors":"A. Mabrouk, S. Zouzou, M. Sahraoui, S. Khelif","doi":"10.1109/DEMPED.2013.6645733","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645733","url":null,"abstract":"Diagnosis of electrical machines are becoming more and more important issues in the field of electrical machines as new data processing technique. Motor Current Stator Analysis (MCSA) are usually used to detect the broken bars. In several industrial applications, the motor is subjected to load torque variations of low frequencies, which have effects similar to rotor faults in the current spectrum and result of diagnostic procedure may be ambigues. Discriminating rotor cage fault from oscillating load effects in Induction motors must be considered. In this paper, we present a study based on the application of the active and reactive power signature analyses for discriminating broken rotor bars from mechanical load oscillation effects in operating three-phase squirrel cage induction motors. This method is attractive because it does not need to interrupt the operating system. Finite element method was used to perform dynamical simulation, which leads to more precise results than other models, as the reel geometry and winding layout of the machine are used. The computer simulations and laboratory experiments results show the interest and the efficiency of this technique for the correct distinction between broken rotor bars and load oscillations.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131205735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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