| 1. |
- 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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| 2. |
- Fairley, Jacqueline A., et al.
(författare)
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Wavelet analysis for detection of phasic electromyographic activity in sleep : of mother wavelet and dimensionality reduction
- 2014
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 48:1, s. 77-84
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Tidskriftsartikel (refereegranskat)abstract
- Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets
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| 3. |
- Fanti, Maria Pia, et al.
(författare)
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A simulation based Decision Support System for logistics management
- 2015
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Ingår i: Journal of Computational Science. - : Elsevier BV. - 1877-7503 .- 1877-7511. ; 10, s. 86-96
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Tidskriftsartikel (refereegranskat)abstract
- This paper deals with designing and developing a Decision Support System (DSS) that will be able to manage the flow of goods and the business transactions between a port and a dry port. An integrated DSS architecture is proposed and specified and the main components are designed on the basis of simulation and optimization modules. In order to show the use and implementation of the DSS, this work tests and analyzes the case of the area of the Trieste port and manages the export flows of freights between a dry port and a seaport. An integrated approach is designed mainly at tactical and operational decision level exploiting simulation and optimization approaches and especially metaheuristic approaches
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| 4. |
- 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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| 5. |
- Georgoulas, Georgios, et al.
(författare)
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A three class treatment of the FHR classification problem using latent class analysis labeling
- 2014
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Ingår i: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781424479290 ; , s. 46-49
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Konferensbidrag (refereegranskat)abstract
- Electronic Fetal Monitoring in the form of cardiotocography is routinely used for fetal assessment both during pregnancy and delivery. However its interpretation requires a high level of expertise and even then the assessment is somewhat subjective as it has been proven by the high inter and intra-observer variability. Therefore the scientific community seeks for more objective methods for its interpretation. Along this path, presented work proposes a classification approach, which is based on a latent class analysis method that attempts to produce more objective labeling of the training cases, a step which is vital in a classification problem. The method is combined with a simple logistic regression approach under two different schemes: a standard multi-class classification formulation and an ordinal classification one. The results are promising suggesting that more effort should be put in this proposed approach
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| 6. |
- Georgoulas, George, et al.
(författare)
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An exploratory approach to fetal heart rate–pH-based systems
- 2021
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Ingår i: Signal, Image and Video Processing. - : Springer. - 1863-1703 .- 1863-1711. ; 15:1, s. 43-51
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an exploratory approach of the fetal heart rate (FHR) analysis, aiming to highlight potential limitations of the current predictive modeling attempts. To do so, a set of features that are usually encountered in FHR analysis as well as features extracted using a variant of symbolic aggregate approximation were projected onto a lower-dimensional space where patterns can easily be discerned. The results show, both in a qualitative and a quantitative manner, that there is high overlap between the classes that are formed using solely the umbilical cord pH information, irrespective of the selected dimensionality reduction method. These findings suggest that there is probably a limit to the performance expectation of the current pH-based systems and that alternative approaches should be also pursued to enhance the utility of computer-based decision support technologies.
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| 7. |
- Georgoulas, Georgios, et al.
(författare)
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An ordinal classification approach for CTG categorization
- 2017
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Ingår i: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). - Piscataway, NJ : IEEE. - 9781509028092 ; , s. 2642-2645
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Konferensbidrag (refereegranskat)abstract
- Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- Georgoulas, Georgios, et al.
(författare)
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Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition
- 2013
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Ingår i: Mechanical systems and signal processing. - : Elsevier. - 0888-3270 .- 1096-1216. ; 41:1-2, s. 510-525
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Tidskriftsartikel (refereegranskat)abstract
- Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.
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