Detection of short-circuits of dc motor using thermographic images, binarization and K-NN classifier

Zadnjih je godina otkriveno mnogo metoda za otkrivanje greške. Jedna od njih je termografija, sigurna i neinvazivna metoda. U radu se opisuje otkrivanje početnog stanja greške u istosmjernom motoru. Analizirane su termografske slike ispravljača istosmjernog motora. Analizirane su dvije vrste termografskih slika: termografska slika ispravljača ispravnog istosmjernog motora i termografska slika ispravljača istosmjernog motora s pregorjelim zavojnicama rotora. Analiza je provedena za metode obrade slike kao što su: ekstrakcija grimizno ljubičaste boje, binarizacija, zbir vertikalnih piksela i zbir svih piksela na slici. Klasifikacija se provela za klasifikator K-Najbliži Susjed (K-Nearest Neighbour classifier). Rezultati analize pokazuju da je predložena metoda učinkovita. Može se također koristiti u dijagnostičke svrhe u industrijskim pogonima.Many fault diagnostic methods have been developed in recent years. One of them is thermography. It is a safe and non-invasive method of diagnostic. Fault diagnostic method of incipient states of Direct Current motor was described in the article. Thermographic images of the commutator of Direct Current motor were used in an analysis. Two kinds of thermographic images were analysed: thermographic image of commutator of healthy DC motor, thermographic image of commutator of DC motor with shorted rotor coils. The analysis was carried out for image processing methods such as: extraction of magenta colour, binarization, sum of vertical pixels and sum of all pixels in the image. Classification was conducted for K-Nearest Neighbour classifier. The results of analysis show that the proposed method is efficient. It can be also used for diagnostic purposes in industrial plants

Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Predlaže se sustav dijagnostike greške opterećenog sinkronog motora. Predloženi se sustav zasniva na akustičkim signalima opterećenog sinkronog motora. Predlaže se nova metoda određivanja karakteristika SMOFS-25-EXPANDED (shorted method of frequencies selection-25-expaned – skraćena metoda za izbor frekvencija-25-proširena). Predložena se metoda analizirala u odnosu na tri klasifikatora: LDA (Linear Discriminant Analysis – analiza linearnog diskriminatora), NN (Nearest Neighbour – najbliži susjed), SOM (Self-organizing Map – samoorganizirajuća mapa). Analiza je provedena za stvarno početna stanja opterećenog sinkronog motora. U analizi su iskorišteni akustički signali koje je stvarao motor. Analizirala su se sljedeća stanja motora: ispravan motor, motor sa skraćenim namotajem statora, motor sa skraćenim namotajem statora i prekinutim namotajem, motor sa skraćenim namotajem statora i dva prekinuta namotaja. Do takvih je stanja došlo zbog prirodne degradacije rotirajućeg sinkronog motora. Rezultati analize su dobri. Predložena metoda prepoznavanja akustičkog signala može se primijeniti za zaštitu opterećenih sinkronih motora.A system of fault diagnostics of loaded synchronous motor was proposed. Proposed system was based on acoustic signals of loaded synchronous motor. A new method of feature extraction SMOFS-25-EXPANDED (shorted method of frequencies selection-25-Expanded) was proposed. Presented method was analysed for 3 classifiers: LDA (Linear Discriminant Analysis), NN (Nearest Neighbour), SOM (Self-organizing Map). Analysis was carried out for real incipient states of loaded synchronous motor. Acoustic signals generated by motor were used in analysis. The following states of motor were analysed: healthy motor, motor with shorted stator coil, motor with shorted stator coil and broken coil, motor with shorted stator coil and two broken coils. These states are caused by natural degradation of rotating synchronous motor. The results of recognition were good. Proposed method of acoustic signal recognition can be used to protect loaded synchronous motors

Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip

[EN] The router plays an important role in communication among different processing cores in on-chip networks. Technology scaling on one hand has enabled the designers to integrate multiple processing components on a single chip; on the other hand, it becomes the reason for faults. A generic router consists of the buffers and pipeline stages. A single fault may result in an undesirable situation of degraded performance or a whole chip may stop working. Therefore, it is necessary to provide permanent fault tolerance to all the components of the router. In this paper, we propose a mechanism that can tolerate permanent faults that occur in the router. We exploit the fault-tolerant techniques of resource sharing and paring between components for the input port unit and routing computation (RC) unit, the resource borrowing for virtual channel allocator (VA) and multiple paths for switch allocator (SA) and crossbar (XB). The experimental results and analysis show that the proposed mechanism enhances the reliability of the router architecture towards permanent faults at the cost of 29% area overhead. The proposed router architecture achieves the highest Silicon Protection Factor (SPF) metric, which is 24.4 as compared to the state-of-the-art fault-tolerant architectures. It incurs an increase in latency for SPLASH2 and PARSEC benchmark traffics, which is minimal as compared to the baseline router.This work was supported by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D d Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).Hussain, A.; Irfan, M.; Baloch, NK.; Draz, U.; Ali, T.; Glowacz, A.; Dunai, L.... (2020). Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip. Electronics. 9(11):1-18. https://doi.org/10.3390/electronics9111783S118911Borkar, S. (1999). Design challenges of technology scaling. IEEE Micro, 19(4), 23-29. doi:10.1109/40.782564Latif, K., Rahmani, A.-M., Nigussie, E., Seceleanu, T., Radetzki, M., & Tenhunen, H. (2013). Partial Virtual Channel Sharing: A Generic Methodology to Enhance Resource Management and Fault Tolerance in Networks-on-Chip. Journal of Electronic Testing, 29(3), 431-452. doi:10.1007/s10836-013-5389-5Borkar, S. (2005). Designing Reliable Systems from Unreliable Components: The Challenges of Transistor Variability and Degradation. IEEE Micro, 25(6), 10-16. doi:10.1109/mm.2005.110Ali, T., Noureen, J., Draz, U., Shaf, A., Yasin, S., & Ayaz, M. (2018). Participants Ranking Algorithm for Crowdsensing in Mobile Communication. ICST Transactions on Scalable Information Systems, 5(16), 154476. doi:10.4108/eai.13-4-2018.154476Ali, T., Draz, U., Yasin, S., Noureen, J., shaf, A., & Zardari, M. (2018). An Efficient Participant’s Selection Algorithm for Crowdsensing. International Journal of Advanced Computer Science and Applications, 9(1). doi:10.14569/ijacsa.2018.090154Poluri, P., & Louri, A. (2016). Shield: A Reliable Network-on-Chip Router Architecture for Chip Multiprocessors. IEEE Transactions on Parallel and Distributed Systems, 27(10), 3058-3070. doi:10.1109/tpds.2016.2521641Valinataj, M., & Shahiri, M. (2016). A low-cost, fault-tolerant and high-performance router architecture for on-chip networks. Microprocessors and Microsystems, 45, 151-163. doi:10.1016/j.micpro.2016.04.009Kim, J., Nicopoulos, C., Park, D., Narayanan, V., Yousif, M. S., & Das, C. R. (2006). A Gracefully Degrading and Energy-Efficient Modular Router Architecture for On-Chip Networks. ACM SIGARCH Computer Architecture News, 34(2), 4-15. doi:10.1145/1150019.1136487Polian, I., & Hayes, J. P. (2011). Selective Hardening: Toward Cost-Effective Error Tolerance. IEEE Design & Test of Computers, 28(3), 54-63. doi:10.1109/mdt.2010.120Mohammed, H., Flayyih, W., & Rokhani, F. (2019). Tolerating Permanent Faults in the Input Port of the Network on Chip Router. Journal of Low Power Electronics and Applications, 9(1), 11. doi:10.3390/jlpea9010011Wang, L., Ma, S., Li, C., Chen, W., & Wang, Z. (2017). A high performance reliable NoC router. Integration, 58, 583-592. doi:10.1016/j.vlsi.2016.10.016Shafique, M. A., Baloch, N. K., Baig, M. I., Hussain, F., Zikria, Y. B., & Kim, S. W. (2020). NoCGuard: A Reliable Network-on-Chip Router Architecture. Electronics, 9(2), 342. doi:10.3390/electronics9020342Poluri, P., & Louri, A. (2015). A Soft Error Tolerant Network-on-Chip Router Pipeline for Multi-Core Systems. IEEE Computer Architecture Letters, 14(2), 107-110. doi:10.1109/lca.2014.2360686Feng, C., Lu, Z., Jantsch, A., Zhang, M., & Xing, Z. (2013). Addressing Transient and Permanent Faults in NoC With Efficient Fault-Tolerant Deflection Router. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 21(6), 1053-1066. doi:10.1109/tvlsi.2012.2204909Liu, J., Harkin, J., Li, Y., & Maguire, L. P. (2016). Fault-Tolerant Networks-on-Chip Routing With Coarse and Fine-Grained Look-Ahead. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35(2), 260-273. doi:10.1109/tcad.2015.2459050Runge, A. (2015). FaFNoC: A Fault-tolerant and Bufferless Network-on-chip. Procedia Computer Science, 56, 397-402. doi:10.1016/j.procs.2015.07.226Binkert, N., Beckmann, B., Black, G., Reinhardt, S. K., Saidi, A., Basu, A., … Wood, D. A. (2011). The gem5 simulator. ACM SIGARCH Computer Architecture News, 39(2), 1-7. doi:10.1145/2024716.202471

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method

Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction

[EN] Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to success or failure of a project. The risk should be predicted earlier to make a software project successful. A Model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. Also, a comparison is done between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/ Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) to achieve best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew¿s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB and CDT achieve better results.This work was supported by by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224)Naseem, R.; Shaukat, Z.; Irfan, M.; Shah, MA.; Ahmad, A.; Muhammad, F.; Glowacz, A.... (2021). Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics. 10(2):1-19. https://doi.org/10.3390/electronics1002016811910

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

[EN] Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.This research work was funded by the Ministry of Education and the Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabia, under code number NU/ESCI/19/001.Hussain, A.; Draz, U.; Ali, T.; Tariq, S.; Glowacz, A.; Irfan, M.; Antonino Daviu, JA.... (2020). Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 13(15):1-22. https://doi.org/10.3390/en13153930S1221315Lionetto, M. G., Guascito, M. R., Caricato, R., Giordano, M. E., De Bartolomeo, A. R., Romano, M. P., … Contini, D. (2019). Correlation of Oxidative Potential with Ecotoxicological and Cytotoxicological Potential of PM10 at an Urban Background Site in Italy. Atmosphere, 10(12), 733. doi:10.3390/atmos10120733Wiedinmyer, C., Yokelson, R. J., & Gullett, B. K. (2014). Global Emissions of Trace Gases, Particulate Matter, and Hazardous Air Pollutants from Open Burning of Domestic Waste. Environmental Science & Technology, 48(16), 9523-9530. doi:10.1021/es502250zYan, F., Zhu, F., Wang, Q., & Xiong, Y. (2016). Preliminary Study of PM2.5 Formation During Municipal Solid Waste Incineration. Procedia Environmental Sciences, 31, 475-481. doi:10.1016/j.proenv.2016.02.054Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., & Pan, Y. (2006). Adverse health effects of outdoor air pollutants. Environment International, 32(6), 815-830. doi:10.1016/j.envint.2006.03.012Gollakota, A. R. K., Gautam, S., & Shu, C.-M. (2020). Inconsistencies of e-waste management in developing nations – Facts and plausible solutions. Journal of Environmental Management, 261, 110234. doi:10.1016/j.jenvman.2020.110234Anitha, A. (2017). Garbage monitoring system using IoT. IOP Conference Series: Materials Science and Engineering, 263, 042027. doi:10.1088/1757-899x/263/4/042027Sirsikar, S., & Karemore, P. (2015). Review Paper on Air Pollution Monitoring system. IJARCCE, 218-220. doi:10.17148/ijarcce.2015.4147Tavares Neto, R. F., & Godinho Filho, M. (2013). Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26(1), 150-161. doi:10.1016/j.engappai.2012.03.011Ali, T., Irfan, M., Alwadie, A. S., & Glowacz, A. (2020). IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering, 45(12), 10185-10198. doi:10.1007/s13369-020-04637-wSilva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697-713. doi:10.1016/j.scs.2018.01.053Gutierrez, J. M., Jensen, M., Henius, M., & Riaz, T. (2015). Smart Waste Collection System Based on Location Intelligence. Procedia Computer Science, 61, 120-127. doi:10.1016/j.procs.2015.09.170Carbon Monoxide Dangers in the Boiler Room www.pmmag.com/articles/97528-carbonmonoxide-danger-in-the-boiler-roomDe Vito, S., Massera, E., Piga, M., Martinotto, L., & Di Francia, G. (2008). On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sensors and Actuators B: Chemical, 129(2), 750-757. doi:10.1016/j.snb.2007.09.060Guiry, J., van de Ven, P., & Nelson, J. (2014). Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices. Sensors, 14(3), 5687-5701. doi:10.3390/s140305687Ali, T., Draz, U., Yasin, S., Noureen, J., shaf, A., & Zardari, M. (2018). An Efficient Participant’s Selection Algorithm for Crowdsensing. International Journal of Advanced Computer Science and Applications, 9(1). doi:10.14569/ijacsa.2018.090154Ali, T., Noureen, J., Draz, U., Shaf, A., Yasin, S., & Ayaz, M. (2018). Participants Ranking Algorithm for Crowdsensing in Mobile Communication. ICST Transactions on Scalable Information Systems, 5(16), 154476. doi:10.4108/eai.13-4-2018.15447

Vibration-Based Fault Diagnosis of Commutator Motor

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries

Thermographic Fault Diagnosis of Shaft of BLDC Motor

A technique of thermographic fault diagnosis of the shaft of a BLDC (Brushless Direct Current Electric) motor is presented in this article. The technique works for the shivering of the thermal imaging camera in the range of 0–1.5 [m/s2]. An electric shaver was used as the source of the BLDC motor. The following states of the BLDC motor were analyzed: Healthy BLDC motor (HB), BLDC motor with one faulty shaft (1FSB), BLDC motor with two faulty shafts (2FSB), and BLDC motor with three faulty shafts (3FSB). A new method of feature extraction named PNID (power of normalized image difference) was presented. Deep neural networks were used for the analysis of thermal images of the faulty shaft of the BLDC motor: GoogLeNet, ResNet50, and EfficientNet-b0. The results of the proposed technique were very good. PNID, GoogLeNet, ResNet50, and EfficientNet-b0 have an efficiency of recognition equal to 100% for four classes

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TED = 96%, TECG-A = 97%, TECG-B = 100%)