| 1. |
- Antonino-Daviu, José Alfonso, et al.
(författare)
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Multi-harmonic tracking for diagnosis of rotor asymmetries in wound rotor induction motors
- 2013
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Ingår i: IECON Proceedings (Industrial Electronics Conference). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781479902248 ; , s. 5555-5560
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Konferensbidrag (refereegranskat)abstract
- Most of the research work hitherto carried out in the induction motors fault diagnosis area has been focused on squirrel-cage motors in spite of the fact that wound-rotor motors are typically less robust, having a more delicate maintenance. Over recent years, wound-rotor machines have drawn an increasing attention in the fault diagnosis community due to the advent of wind power technologies for electricity generation and the widely spread use of its generator variant, the Doubly-Fed Induction Generators (DFIGs) in that specific context. Nonetheless, there is still a lack of reliable techniques suited and properly validated in wound-rotor industrial induction motors. This paper proposes an integral methodology to diagnose rotor asymmetries in wound-rotor motors with high reliability. It is based on a twofold approach; the Empirical Mode Decomposition (EMD) method is employed to track the low-frequency fault-related components, while the Wigner-Ville Distribution (WVD) is used for detecting the high-frequency failure harmonics during a startup. Experimental results with real wound-rotor motors demonstrate that the combination of both perspectives enables to correctly diagnose the failure with higher reliability than alternative techniques relying on a unique informational source
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| 2. |
- Georgoulas, Georgios, et al.
(författare)
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A Multi-label Classification Approach for the Detection of Broken Bars and Mixed Eccentricity Faults Using the Start-up Transient
- 2017
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Ingår i: IEEE International Conference on Industrial Informatics (INDIN). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509028702 ; , s. 430-433
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Konferensbidrag (refereegranskat)abstract
- In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.
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| 3. |
- Georgoulas, Georgios, et al.
(författare)
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Automatic pattern identification based on the complex empirical mode decomposition of the startup current for the diagnosis of rotor asymmetries in asynchronous machines
- 2014
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Ingår i: IEEE Transactions on Industrial Electronics. - : Institution of Electrical Engineers of Japan (IEEJ). - 0278-0046 .- 1557-9948. ; 61:9, s. 4937-4946
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology
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| 4. |
- Georgoulas, Georgios, et al.
(författare)
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Automatizing the broken bar detection process via Short Time Fourier Transform and two-dimensional Piecewise Aggregate Approximation representation
- 2014
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Konferensbidrag (refereegranskat)abstract
- This work presents an automated approach for detecting broken rotor bars in induction machines using the stator current during startup operation. The currents are analyzed using the well-known Short Time Fourier Transform (STFT) producing a two-dimensional time-frequency representation. This representation contains information regarding the presence of a characteristic transient component but requires further processing before it can be fed into a standard classification algorithm. In this work, this part is performed using the two dimensional extension of Piecewise Aggregate Approximation (PAA) that can deal with the two dimensional representation of STFT. The results (with both simulated and experimental data) suggest that the method can be used for the automatic detection of broken bars and even for determining the fault severity. Moreover, its low computational burden makes it ideal for its future use in online, unsupervised systems, as well as in portable condition monitoring devices.
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| 5. |
- Georgoulas, George, et al.
(författare)
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Automatizing the detection of rotor failures in induction motors operated via soft-starters
- 2016
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Ingår i: Annual Conference of the IEEE Industrial Electronics Society, IECON 2015. - Piscataway, NJ : IEEE Communications Society. - 9781479917624 ; , s. 3743-3748
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Konferensbidrag (refereegranskat)abstract
- Implementation of unsupervised induction motor condition monitoring systems has drawn an increasing attention recently among motor drives manufacturers. In the case of soft- starters the possibility of incorporating fault detection features to their conventional functions provides an added value to those elements. Design and development of advanced algorithms that are able to automatically detect and alert about possible failures without requiring continuous human inspection is an especially challenging research goal. In this paper, an algorithm for the automatic detection of rotor damages in induction motors in the case of soft starting is proposed. The twofold approach relies, first, on the application of a time-frequency transform to the starting current signal and, second, on a pattern recognition stage based on the treatment of the time-frequency representation as a symbolic sequence. The innovation of this work is the implementation of the proposed approach for the automatic detection of rotor cage faults in soft-started motors. The experimental results prove the usefulness of the approach for the automatic detection of such faults and its potential for possible future implementation in soft-started machines.
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| 6. |
- Georgoulas, Georgios, et al.
(författare)
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Start-up analysis methods for the diagnosis of rotor asymmetries in induction motors-seeing is believing
- 2016
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Ingår i: 24th Mediterranean Conference on Control and Automation (MED). - Piscataway, NJ : IEEE Communications Society. - 9781467383455 ; , s. 372-377
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Konferensbidrag (refereegranskat)abstract
- This article presents a qualitative analysis of different methods proposed for the diagnosis of broken rotor bars using the stator current during start-up operation. The slip dependent components, caused by the asymmetry, which is created by the breakage of rotor bar(s) and especially the left sideband harmonic (LSH) component, can create a distinctive pattern in a time- frequency plane. Short Time Fourier Transform, Wavelet analysis, and Winger-Ville Distribution are evaluated by using signals coming from motors operating in real industrial settings. The corresponding analysis presents the pros and the cons of these approaches for their potential application under realistic industrial conditions using the larger number of real life cases encountered in the literature.
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| 7. |
- Georgoulas, Georgios, et al.
(författare)
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The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient
- 2017
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 13:2, s. 625-634
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three 'problem transformation' methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation
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| 8. |
- Karvelis, Petros, et al.
(författare)
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A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
- 2015
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 11:5, s. 1028-1037
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Tidskriftsartikel (refereegranskat)abstract
- One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.
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| 9. |
- Karvelis, Petros, et al.
(författare)
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An automated thermographic image segmentation method for induction motor fault diagnosis
- 2015
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Ingår i: IECON 2014. - Piscataway, NJ : IEEE Communications Society. - 9781479940332 ; , s. 3396-3402
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Konferensbidrag (refereegranskat)abstract
- Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.
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| 10. |
- Karvelis, Petros, et al.
(författare)
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An intelligent icons approach for rotor bar fault detection
- 2013
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Ingår i: IECON Proceedings (Industrial Electronics Conference). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781479902248 ; , s. 5526-5531
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose the use of Intelligent Icons for both automatic assessment and representation of asynchronous machines' condition. The method focuses on the analysis of the start-up current for the isolation of a component that is able to pinpoint faulty signatures. The analysis is based on the application of Empirical Mode Decomposition (EMD) which acts as an adaptive filter during the start up and subsequently on the application of Symbolic Aggregate approXimation (SAX) for the transformation of the extracted component into a symbolic representation. Using this symbolic representation, an automated detection procedure can be developed that discriminates between faulty and normal conditions using an intelligent Icons approach while at the same time the information can be presented to the user in a more intuitive way
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| 11. |
- Karvelis, Petros, et al.
(författare)
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Symbolic time series analysis of the soft starting transient in induction machines
- 2015
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Ingår i: 2015 IEEE International Conference on Industrial Technology (ICIT 2015) to be held in Seville, Spain, March 17-19, 2015.. - Piscataway, NJ : IEEE Communications Society. - 9781479978007 ; , s. 3243-3248
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Konferensbidrag (refereegranskat)abstract
- Induction motors are in the heart of almost every production line especially due to their robustness under harsh environments. Nevertheless, even induction machines are prone to faults. Among them, the faults related to the breakage of rotor bars have received special attention by the research community with a number of methods proposed both for the case of steady state as well as for transient operation. For the latter, methods relying on the analysis of the start-up transient have proven to be able to effectively isolate the faulty component that is created by the asymmetry caused by the bar breakage. However, very little work has been done concerning the soft starting of induction machines. In this work, preliminary results of the application of a symbolic time series technique for the analysis of the transient, when the motor is controlled by a soft starter, will be presented and experimentally evaluated.
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