| 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
-
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
-
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)
-
Automatizing the broken bar detection process via Short Time Fourier Transform and two-dimensional Piecewise Aggregate Approximation representation
- 2014
-
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)
-
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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| 11. |
- Georgoulas, George G., 1976-, et al.
(författare)
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Harmony search augmented with optimal computing budget allocation capabilities for noisy optimization
- 2013
-
Ingår i: IAENG International Journal of Computer Science. - 1819-656X .- 1819-9224. ; 40:4, s. 285-290
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Tidskriftsartikel (refereegranskat)abstract
- In this work we introduce the combinatory use of Harmony Search (HS) with Optimal Computing Budget Allocation (OCBA) as a means to tackle noisy optimization situations as those that occur during the execution of Discrete Event Systems (DES) for modeling complex systems. The OCBA procedure is employed for the exclusion of the worst harmony during the memory updating process in order to minimize the computational cost and at the same time retain a pool of promising solutions. The proposed hybrid approach is tested on real valued test functions as a proof of concept and the results are promising in case of small computational budgets.
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| 12. |
- Georgoulas, Georgios, et al.
(författare)
-
Investigating pH based evaluation of fetal heart rate (FHR) recordings
- 2017
-
Ingår i: Health and Technology. - : Springer. - 2190-7188 .- 2190-7196. ; 7:2/3, s. 241-254
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Tidskriftsartikel (refereegranskat)abstract
- Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.
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| 13. |
- Georgoulas, George, et al.
(författare)
-
Principal component analysis of the start-up transient and hidden Markov modeling for broken rotor bar fault diagnosis in asynchronous machines
- 2013
-
Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 40:17, s. 7024-7033
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMM, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.
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| 14. |
- Georgoulas, Georgios, et al.
(författare)
-
Rolling element bearings diagnostics using the Symbolic Aggregate approXimation
- 2015
-
Ingår i: Mechanical systems and signal processing. - : Elsevier. - 0888-3270 .- 1096-1216. ; 60, s. 229-242
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Tidskriftsartikel (refereegranskat)abstract
- Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults
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| 15. |
- Georgoulas, Georgios, et al.
(författare)
-
The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient
- 2017
-
Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 13:2, s. 625-634
-
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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| 16. |
- Karvelis, Petros, et al.
(författare)
-
A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
- 2015
-
Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 11:5, s. 1028-1037
-
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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| 17. |
- Karvelis, Petros, et al.
(författare)
-
An automated thermographic image segmentation method for induction motor fault diagnosis
- 2015
-
Ingår i: IECON 2014. - Piscataway, NJ : IEEE Communications Society. - 9781479940332 ; , s. 3396-3402
-
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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| 18. |
- Karvelis, Petros, et al.
(författare)
-
An intelligent icons approach for rotor bar fault detection
- 2013
-
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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| 19. |
- Karvelis, Petros, et al.
(författare)
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Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization
- 2015
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Ingår i: Physiological Measurement. - : Institute of Physics (IOP). - 0967-3334 .- 1361-6579. ; 36:5, s. 1001-1024
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Tidskriftsartikel (refereegranskat)abstract
- The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions - the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.
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| 20. |
- Karvelis, Petros, et al.
(författare)
-
Semi-automated annotation of phasic electromyographic activity
- 2014
-
Konferensbidrag (refereegranskat)abstract
- Recent research on manual/visual identification of phasic muscle activity utilizing the phasic electromyographic metric (PEM) in human polysomnograms (PSGs) cites evidence that PEM is a potentially reliable quantitative metric to assist in distinguishing between neurodegenerative disorder populations and age-matched controls. However, visual scoring of PEM activity is time consuming-preventing feasible implementation within a clinical setting. Therefore, here we propose an assistive/semi-supervised software platform designed and tested to automatically identify and characterize PEM events in a clinical setting that will be extremely useful for sleep physicians and technicians. The proposed semi-automated approach consists of four levels: A) Signal Parsing, B) Calculation of quantitative features on candidate PEM events, C) Classification of PEM and non-PEM events using a linear classifier, and D) Post-processing/Expert feedback to correct/remove automated misclassifications of PEM and Non-PEM events. Performance evaluation of the designed software compared to manual labeling is provided for electromyographic (EMG) activity from the PSG of a control subject. Results indicate that the semi-automated approach provides an excellent benchmark that could be embedded into a clinical decision support system to detect PEM events that would be used in neurological disorder identification and treatment.
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| 21. |
- Kolios, Stavros, et al.
(författare)
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Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks
- 2013
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Ingår i: International Journal of Remote Sensing. - : Taylor & Francis. - 0143-1161 .- 1366-5901. ; 34:21, s. 7706-7722
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Tidskriftsartikel (refereegranskat)abstract
- This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth's surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.
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| 22. |
- Simioni, Fabrizio, et al.
(författare)
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A software architecture for integrated logistic management system
- 2013
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Konferensbidrag (refereegranskat)abstract
- This paper presents an integrated software architecture for the management of transfer goods (trucks and containers) between port and dry-port facilities. This system is a large scale system and it has to deal with huge and continuously updated set of information. The required information is gathered from other information systems managing business activities within the involved areas. This is the reason why there was required the development of software components that are able to manage the processes of acquiring and sending information to the other systems. In this paper the technological choices and the main information flows managed are described.
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| 23. |
- Spilka, Jiří, et al.
(författare)
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Discriminating normal from "abnormal" pregnancy cases using an automated FHR evaluation method
- 2014
-
Konferensbidrag (refereegranskat)abstract
- Electronic fetal monitoring has become the gold standard for fetal assessment both during pregnancy as well as during delivery. Even though electronic fetal monitoring has been introduced to clinical practice more than forty years ago, there is still controversy in its usefulness especially due to the high inter- and intra-observer variability. Therefore the need for a more reliable and consistent interpretation has prompted the research community to investigate and propose various automated methodologies. In this work we propose the use of an automated method for the evaluation of fetal heart rate, the main monitored signal, which is based on a data set, whose labels/annotations are determined using a mixture model of clinical annotations. The successful results of the method suggest that it could be integrated into an assistive technology during delivery.
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| 24. |
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