Identification of Babbitt Damage and Excessive Clearance in Journal Bearings through an Intelligent Recognition Approach

Journal bearings play an important role on many rotating machines placed on industrial environments, especially in steam turbines of thermoelectric power plants. Babbitt damage (BD) and excessive clearance (C) are usual faults of steam turbine journal bearings. This paper is focused on achieving an effective identification of these faults through an intelligent recognition approach. The work was carried out through the processing of real data obtained from an industrial environment. In this work, a feature selection procedure was applied in order to choose the features more suitable to identify the faults. This feature selection procedure was performed through the computation of typical testors, which allows working with both quantitative and qualitative features. The classification tasks were carried out by using Nearest Neighbors, Voting Algorithm, Naïve Associative Classifier and Assisted Classification for Imbalance Data techniques. Several performance measures were computed and used in order to assess the classification effectiveness. The achieved results (e.g., six performance measures were above 0.998) showed the convenience of applying pattern recognition techniques to the automatic identification of BD and C

Recent advances in the theory and practice of logical analysis of data

Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods

Applicability and Interpretability of Logical Analysis of Data in Condition Based Maintenance

Résumé Cette thèse étudie l’applicabilité et l’adaptabilité d’une approche d’exploration de données basée sur l’intelligence artificielle proposée dans [Hammer, 1986] et appelée analyse logique de données (LAD) aux applications diagnostiques dans le domaine de la maintenance conditionnelle CBM). La plupart des technologies utilisées à ce jour pour la prise de décision dans la maintenance conditionnelle ont tendance à automatiser le processus de diagnostic, sans offrir aucune connaissance ajoutée qui pourrait être utile à l’opération de maintenance et au personnel de maintenance. Par comparaison à d’autres techniques de prise de décision dans le domaine de la CBM, la LAD possède deux avantages majeurs : (1) il s’agit d’une approche non statistique, donc les données n’ont pas à satisfaire des suppositions statistiques et (2) elle génère des formes interprétables qui pourraient aider à résoudre les problèmes de maintenance. Une étude sur l’application de la LAD dans la maintenance conditionnelle est présentée dans cette recherche dont l’objectif est (1) d’étudier l’applicabilité de la LAD dans des situations différentes qui nécessitent des considérations particulières concernant les types de données d’entrée et les décisions de maintenance, (2) d’adapter la méthode LAD aux exigences particulières qui se posent à partir de ces applications et (3) d’améliorer la méthodologie LAD afin d’augmenter l’exactitude de diagnostic et d’interprétation de résultats. Les aspects innovants de la recherche présentés dans cette thèse sont (1) l’application de la LAD dans la CBM pour la première fois dans des applications qui bénéficient des propriétés uniques de cette technologie et (2) les modifications innovatrices de la méthodologie de la LAD, en particulier dans le domaine de la génération des formes, afin d’améliorer ses performances dans le cadre de la CBM et dans le domaine de classification multiclasses. La recherche menée dans cette thèse a suivi une approche évolutive afin d’atteindre les objectifs énoncés ci-dessus. La LAD a été utilisée et adaptée à trois applications : (1) la détection des composants malveillants (Rogue) dans l’inventaire de pièces de rechange réparables d’une compagnie aérienne commerciale, (2) la détection et l’identification des défauts dans les transformateurs de puissance en utilisant la DGA et (3) la détection des défauts dans les rotors en utilisant des signaux de vibration. Cette recherche conclut que la LAD est une approche de prise de décision prometteuse qui ajoute d’importants avantages à la mise en oeuvre de la CBM dans l’industrie.----------Abstract This thesis studies the applicability and adaptability of a data mining artificial intelligence approach called Logical Analysis of Data (LAD) to diagnostic applications in Condition Based Maintenance (CBM). Most of the technologies used so far for decision support in CBM tend to automate the diagnostic process without offering any added knowledge that could be helpful to the maintenance operation and maintenance personnel. LAD possesses two key advantages over other decision making technologies used in CBM: (1) it is a non-statistical approach; as such no statistical assumptions are required for the input data, and (2) it generates interpretable patterns that could help solve maintenance problems. A study on the implementation of LAD in CBM is presented in this research whose objective are to study the applicability of LAD in different CBM situations requiring special considerations regarding the types of input data and maintenance decisions, adapt the LAD methodology to the particular requirements that arise from these applications, and improve the LAD methodology in line with the above two objectives in order to increase diagnosis accuracy and result interpretability. The novelty of the research presented in this thesis is (1) the application of LAD to CBM for the first time in applications that stand to benefit from the advantages that this technology provides; and (2) the innovative modifications to LAD methodology, particularly in the area of pattern generation, in order to improve its performance within the context of CBM. The research conducted in this thesis followed an evolutionary approach in order to achieve the objectives stated in the Introduction. The research applied LAD in three applications: (1) the detection of Rogue components within the spare part inventory of reparable components in a commercial airline company, (2) the detection and identification of faults in power transformers using DGA, and (3) the detection of faults in rotor bearings using vibration signals. This research concludes that LAD is a promising decision making approach that adds important benefits to the implementation of CBM in the industry

Fault Prognostics Using Logical Analysis of Data and Non-Parametric Reliability Estimation Methods

RÉSUMÉ : Estimer la durée de vie utile restante (RUL) d’un système qui fonctionne suivant différentes conditions de fonctionnement représente un grand défi pour les chercheurs en maintenance conditionnelle (CBM). En effet, il est difficile de comprendre la relation entre les variables qui représentent ces conditions de fonctionnement et la RUL dans beaucoup de cas en pratique à cause du degré élevé de corrélation entre ces variables et leur dépendance dans le temps. Il est également difficile, voire impossible, pour des experts d’acquérir et accumuler un savoir à propos de systèmes complexes, où l'échec de l'ensemble du système est vu comme le résultat de l'interaction et de la concurrence entre plusieurs modes de défaillance. Cette thèse présente des méthodologies pour le pronostic en CBM basé sur l'apprentissage automatique, et une approche de découverte de connaissances appelée Logical Analysis of Data (LAD). Les méthodologies proposées se composent de plusieurs implémentations de la LAD combinées avec des méthodes non paramétriques d'estimation de fiabilité. L'objectif de ces méthodologies est de prédire la RUL du système surveillé tout en tenant compte de l'analyse des modes de défaillance uniques ou multiples. Deux d’entre elles considèrent un mode de défaillance unique et une autre considère de multiples modes de défaillance. Les deux méthodologies pour le pronostic avec mode unique diffèrent dans la manière de manipuler les données. Les méthodologies de pronostique dans cette recherche doctorale ont été testées et validées sur la base d'un ensemble de tests bien connus. Dans ces tests, les méthodologies ont été comparées à des techniques de pronostic connues; le modèle à risques proportionnels de Cox (PHM), les réseaux de neurones artificiels (ANNs) et les machines à vecteurs de support (SVMs). Deux ensembles de données ont été utilisés pour illustrer la performance des trois méthodologies: l'ensemble de données du turboréacteur à double flux (turbofan) qui est disponible au sein de la base de données pour le développement d'algorithmes de pronostic de la NASA, et un autre ensemble de données obtenu d’une véritable application dans l'industrie. Les résultats de ces comparaisons indiquent que chacune des méthodologies proposées permet de prédire avec précision la RUL du système considéré. Cette recherche doctorale conclut que l’approche utilisant la LAD possède d’importants mérites et avantages qui pourraient être bénéfiques au domaine du pronostic en CBM. Elle est capable de gérer les données en CBM qui sont corrélées et variantes dans le temps. Son autre avantage et qu’elle génère un savoir interprétable qui est bénéfique au personnel de maintenance.----------ABSTRACT : Estimating the remaining useful life (RUL) for a system working under different operating conditions represents a big challenge to the researchers in the condition-based maintenance (CBM) domain. The reason is that the relationship between the covariates that represent those operating conditions and the RUL is not fully understood in many practical cases, due to the high degree of correlation between such covariates, and their dependence on time. It is also difficult or even impossible for the experts to acquire and accumulate the knowledge from a complex system, where the failure of the system is regarded as the result of interaction and competition between several failure modes. This thesis presents systematic CBM prognostic methodologies based on a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The proposed methodologies comprise different implementations of the LAD approach combined with non-parametric reliability estimation methods. The objective of these methodologies is to predict the RUL of the monitored system while considering the analysis of single or multiple failure modes. Three different methodologies are presented; two deal with single failure mode and one deals with multiple failure modes. The two methodologies for single mode prognostics differ in the way of representing the data. The prognostic methodologies in this doctoral research have been tested and validated based on a set of widely known tests. In these tests, the methodologies were compared to well-known prognostic techniques; the proportional hazards model (PHM), artificial neural networks (ANNs) and support vector machines (SVMs). Two datasets were used to illustrate the performance of the three methodologies: the turbofan engine dataset that is available at NASA prognostic data repository, and another dataset collected from a real application in the industry. The results of these comparisons indicate that each of the proposed methodologies provides an accurate prediction for the RUL of the monitored system. This doctoral research concludes that the LAD approach has attractive merits and advantages that add benefits to the field of prognostics. It is capable of dealing with the CBM data that are correlated and time-varying. Another advantage is its generation of an interpretable knowledge that is beneficial to the maintenance personnel

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Higher-order spectral analysis of stray flux signals for faults detection in induction motors

[EN] This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.This work has been supported by Generalitat Valenciana, Conselleria d'Educació, Cultura i Esport in the framework of the "Programa para la promoción de la investigación científica, el desarrollo tecnológico y la innovación en la Comunitat Valenciana", Subvenciones para grupos de investigación consolidables (ref: AICO/2019/224). J. Alberto Conejero is also partially supported by MEC Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2020). Higher-order spectral analysis of stray flux signals for faults detection in induction motors. Applied Mathematics and Nonlinear Sciences. 5(2):1-14. https://doi.org/10.2478/amns.2020.1.00032S11452H. Akçay and E. Germen. Subspace-based identification of acoustic noise spectra in induction motors. IEEE Transactions on Energy Conversion, 30(1):32–40, 2015.J. Antonino-Daviu, M. Riera-Guasp, J. Roger-Folch, F. Martínez-Giménez, and A. Peris. Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Applied and Computational Harmonic Analysis, 21(2):268–279, 2006.N. Arthur and J. Penman. Induction machine condition monitoring with higher order spectra. IEEE Transactions on Industrial Electronics, 47(5):1031–1041, 2000.T. P. Banerjee and S. Das. Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217:96–107, 2012.G. Bin, J. Gao, X. Li, and B. Dhillon. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27:696–711, 2012.B. Boashash, E. J. Powers, and A. M. Zoubir. Higher-order statistical signal processing. Longman Cheshire, 1995.A. Ceban, R. Pusca, and R. Romary. Eccentricity and broken rotor bars faults-effects on the external axial field. In The XIX International Conference on Electrical Machines-ICEM 2010, pages 1–6. IEEE, 2010.I. Chernyavska and O. Vítek. Analysis of broken rotor bar fault in a squirrel-cage induction motor by means of stator current and stray flux measurement. In 2016 IEEE International Power Electronics and Motion Control Conference (PEMC), pages 532–537. IEEE, 2016.T. Chow and G. Fei. Three phase induction machines asymmetrical faults identification using bispectrum. IEEE Transactions on Energy Conversion, 10(4):688–693, 1995.X. Dai and Z. Gao. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4):2226–2238, 2013.J. de Jesus Rangel-Magdaleno, H. Peregrina-Barreto, J. M. Ramirez-Cortes, P. Gomez-Gil, and R. Morales-Caporal. Fpga-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphology. IEEE Transactions on Instrumentation and Measurement, 63(5):1032–1040, 2013.P. A. Delgado-Arredondo, D. Morinigo-Sotelo, R. A. Osornio-Rios, J. G. Avina-Cervantes, H. Rostro-Gonzalez, and R. de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017.M. Drif and A. J. M. Cardoso. Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Transactions on Industrial Informatics, 10(2):1348–1360, 2014.L. Frosini, C. Harlişca, and L. Szabó. Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Transactions on Industrial Electronics, 62(3):1846–1854, 2014.Z. Gao, C. Cecati, and S. X. Ding. A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6):3757–3767, 2015.M. Geethanjali and H. Ramadoss. Fault diagnosis of induction motors using motor current signature analysis: A review. In Advanced Condition Monitoring and Fault Diagnosis of Electric Machines, pages 1–37. IGI Global, 2019.T. Ghanbari and A. Farjah. A magnetic leakage flux-based approach for fault diagnosis in electrical machines. IEEE Sensors Journal, 14(9):2981–2988, 2014.A. Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018.A. Glowacz, W. Glowacz, Z. Glowacz, and J. Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1–9, 2018.T. Goktas, M. Zafarani, K. W. Lee, B. Akin, and T. Sculley. Comprehensive analysis of magnet defect fault monitoring through leakage flux. IEEE Transactions on Magnetics, 53(4):1–10, 2016.K. C. Gryllias and I. A. Antoniadis. A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2):326–344, 2012.F. Gu, Y. Shao, N. Hu, A. Naid, and A. Ball. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mechanical Systems and Signal Processing, 25(1):360–372, 2011.C. Harlişca, L. Szabó, L. Frosini, and A. Albini. Diagnosis of rolling bearings faults in electric machines through stray magnetic flux monitoring. In 2013 8TH International Symposium on Advanced Topics in Electrical Engineering (Atee), pages 1–6. IEEE, 2013.R. Hoppler and R. A. Errath. Motor bearings, not must a piece of metal. In 2007 IEEE Cement Industry Technical Conference Record, pages 214–233. IEEE, 2007.R. M. Howard. Principles of random signal analysis and low noise design: The power spectral density and its applications. John Wiley & Sons, 2004.J.-N. Hwang and Y. H. Hu. Handbook of neural network signal processing. CRC press, 2001.M. E. Iglesias-Martínez, J. A. Antonino-Daviu, P. Fernández de Córdoba, and J. A. Conejero. Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals. Energies, 12(4):597, 2019.M. E. Iglesias-Martinez, P. F. de Cordoba, J. Antonino-Daviu, and J. A. Conejero. Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications, 55(5):4585–4594, 2019.M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of bar breakages in induction motor via spectral subtraction of stray flux signals. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 1796–1802. IEEE, 2018.M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals. Preprint, 2020.F. Immovilli, A. Bellini, R. Rubini, and C. Tassoni. Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, 46(4):1350–1359, 2010.C. Jiang, S. Li, and T. G. Habetler. A review of condition monitoring of induction motors based on stray flux. In 2017 IEEE Energy Conversion Congress and Exposition (ECCE), pages 5424–5430. IEEE, 2017.L. Jiang, Y. Liu, X. Li, and S. Tang. Using bispectral distribution as a feature for rotating machinery fault diagnosis. Measurement, 44(7):1284–1292, 2011.Q. Jiang and F. Chang. A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. Journal of Mechanical Science and Technology, 33(4):1535–1543, 2019.X. Jin and T. W. Chow. Anomaly detection of cooling fan and fault classification of induction motor using mahalanobis–taguchi system. Expert Systems with Applications, 40(15):5787–5795, 2013.J. Józwik. Identification and monitoring of noise sources of CNC machine tools by acoustic holography methods. Advances in Science and Technology Research Journal, 10(30), 2016.S. M. Kay. Fundamentals of statistical signal processing. Prentice Hall PTR, 1993.R. Liu, B. Yang, E. Zio, and X. Chen. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108:33–47, 2018.Z. Liu, H. Cao, X. Chen, Z. He, and Z. Shen. Multi-fault classification based on wavelet svm with pso algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 99:399–410, 2013.J. M. Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE, 79(3):278–305, 1991.M. Mrugalski, M. Witczak, and J. Korbicz. Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Engineering Applications of Artificial Intelligence, 21(6):895–906, 2008.V. Muralidharan and V. Sugumaran. A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8):2023–2029, 2012.Y. Ono, Y. Onishi, T. Koshinaka, S. Takata, and O. Hoshuyama. Anomaly detection of motors with feature emphasis using only normal sounds. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 2800–2804. IEEE, 2013.R. H. C. Palácios, I. N. da Silva, A. Goedtel, and W. F. Godoy. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Systems Research, 127:249–258, 2015.P. Panagiotou, I. Arvanitakis, N. Lophitis, J. A. Antonino-Daviu, and K. N. Gyftakis. Analysis of stray flux spectral components in induction machines under rotor bar breakages at various locations. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 2345–2351. IEEE, 2018.P. A. Panagiotou, I. Arvanitakis, N. Lophitis, J. Antonino-Daviu, and K. N. Gyftakis. A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signals. IEEE Transactions on Industry Applications, 2019.K. Pandey, P. Zope, and S. Suralkar. Review on fault diagnosis in three-phase induction motor. MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA), 2012.J. Rafiee, F. Arvani, A. Harifi, and M. Sadeghi. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical systems and signal processing, 21(4):1746–1754, 2007.A. Sadeghian, Z. Ye, and B. Wu. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 58(7):2253–2263, 2009.L. Saidi, J. B. Ali, and F. Fnaiech. Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions, 54:193–206, 2015.L. Saidi, F. Fnaiech, G. Capolino, and H. Henao. Stator current bi-spectrum patterns for induction machines multiple-faults detection. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pages 5132–5137. IEEE, 2012.L. Saidi, F. Fnaiech, H. Henao, G. Capolino, and G. Cirrincione. Diagnosis of broken-bars fault in induction machines using higher order spectral analysis. ISA Transactions, 52(1):140–148, 2013.M. Salah, K. Bacha, and A. Chaari. An improved spectral analysis of the stray flux component for the detection of air-gap irregularities in squirrel cage motors. ISA transactions, 53(3):816–826, 2014.B. Samanta. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical systems and signal processing, 18(3):625–644, 2004.P. Sangeetha and S. Hemamalini. Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor. IET Signal Processing, 11(5):604–612, 2017.J. Sanz, R. Perera, and C. Huerta. Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Applied Soft Computing, 12(9):2867–2878, 2012.Z. Shen, X. Chen, X. Zhang, and Z. He. A novel intelligent gear fault diagnosis model based on emd and multi-class tsvm. Measurement, 45(1):30–40, 2012.A. Singhal and M. A. Khandekar. Bearing fault detection in induction motor using fast fourier transform. In IEEE Int. Conf. on Advanced Research in Engineering & Technology, 2013.A. Soualhi, K. Medjaher, and N. Zerhouni. Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1):52–62, 2014.A. Swami, G. B. Giannakis, and G. Zhou. Bibliography on higher-order statistics. Signal processing, 60(1):65–126, 1997.O. Vitek, M. Janda, and V. Hajek. Effects of eccentricity on external magnetic field of induction machine. In Melecon 2010–2010 15th IEEE Mediterranean Electrotechnical Conference, pages 939–943. IEEE, 2010.H. Wang, X. Bao, C. Di, and Z. Cheng. Detection of eccentricity fault using slot leakage flux monitoring. In 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), pages 2188–2193. IEEE, 2015.Y. Wang, J. Xiang, R. Markert, and M. Liang. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 66:679–698, 2016.Z. Wang and C. Chang. Online fault detection of induction motors using frequency domain independent components analysis. In 2011 IEEE International Symposium on Industrial Electronics, pages 2132–2137. IEEE, 2011.Z. Wang, C. Chang, and Y. Zhang. A feature based frequency domain analysis algorithm for fault detection of induction motors. In 2011 6th IEEE Conference on Industrial Electronics and Applications, pages 27–32. IEEE, 2011.W. Wenbing and X. Jinquan. The application of coupled three order cumulants’ differential feature in fault diagnosis. In 2017 International Conference on Virtual Reality and Visualization (ICVRV), pages 374–375. IEEE, 2017.I. Zamudio-Ramirez, R. A. Osornio-Rios, M. Trejo-Hernandez, R. d. J. Romero-Troncoso, and J. A. Antonino-Daviu. Smart-sensors to estimate insulation health in induction motors via analysis of stray flux. Energies, 12(9):1658, 2019.X. Zhang and J. Zhou. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing, 41(1–2):127–140, 2013.W. Zhao, T. Tao, and E. Zio. System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection. Applied Soft Computing, 30:792–802, 2015.W. Zhao, Y. Zhang, and Y. Zhu. Diagnosis for transformer faults based on combinatorial Bayes Network. In 2009 2nd International Congress on Image and Signal Processing, pages 1–3. IEEE, 2009.F. Zidat, J.-P. Lecointe, F. Morganti, J.-F. Brudny, T. Jacq, and F. Streiff. Non invasive sensors for monitoring the efficiency of ac electrical rotating machines. Sensors, 10(8):7874–7895, 2010

Classifier Ensemble Feature Selection for Automatic Fault Diagnosis

"An efficient ensemble feature selection scheme applied for fault diagnosis is proposed, based on three hypothesis: a. A fault diagnosis system does not need to be restricted to a single feature extraction model, on the contrary, it should use as many feature models as possible, since the extracted features are potentially discriminative and the feature pooling is subsequently reduced with feature selection; b. The feature selection process can be accelerated, without loss of classification performance, combining feature selection methods, in a way that faster and weaker methods reduce the number of potentially non-discriminative features, sending to slower and stronger methods a filtered smaller feature set; c. The optimal feature set for a multi-class problem might be different for each pair of classes. Therefore, the feature selection should be done using an one versus one scheme, even when multi-class classifiers are used. However, since the number of classifiers grows exponentially to the number of the classes, expensive techniques like Error-Correcting Output Codes (ECOC) might have a prohibitive computational cost for large datasets. Thus, a fast one versus one approach must be used to alleviate such a computational demand. These three hypothesis are corroborated by experiments. The main hypothesis of this work is that using these three approaches together is possible to improve significantly the classification performance of a classifier to identify conditions in industrial processes. Experiments have shown such an improvement for the 1-NN classifier in industrial processes used as case study.

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering