Advanced anomaly detection algorithms based on virtual sensors and one-class techniques

La presente investigación aborda el análisis e implementación de sistemas de detección de anomalías basados en técnicas inteligentes. Concretamente, se lleva a cabo el estudio de dos de las estrategias más comúnmente empleadas. La primera consiste en el desarrollo de un sensor virtual a partir de un modelo híbrido e inteligente capaz de detectar situaciones anómalas. La segunda de las estrategias, se basa en el uso de técnicas \emph{one-class}, a partir de las cuales se implementan clasificadores capaces de determinar la aparición de anomalías en base al comportamiento esperado. Se realizan, por tanto, un análisis y una comparativa de ambas estrategias, poniendo de relieve el desempeño de cada una. Este trabajo, realizado de acuerdo a la modalidad de compendio de publicaciones, presenta un hilo conductor de acuerdo a la investigación efectuada, en el cual se reflejan el avance y las aportaciones sucesivas y concatenadas, con los tres artículos presentados. El primero de los trabajos, aborda la implementación de un sensor virtual, empleado para detectar anomalías en una máquina de obtención del material bicomponente, utilizado en la fabricación de palas de aerogenerador. En este caso, el sensor virtual se desarrolla a través de un modelo de regresión híbrido e inteligente. La aparición de desviaciones entre el valor predicho y real de la lectura del sensor, se presenta como criterio para detectar la anomalía. Esta aportación conlleva la necesidad de disponer de un usuario con cierto conocimiento acerca del umbral que determine la aparición de una anomalía. En consecuencia, en el segundo trabajo, se decide emplear sistemas inteligentes de tipo \emph{one-class}. Se propone la aplicación de este tipo de técnicas sobre una planta de laboratorio, cuyo objetivo es controlar el nivel de agua en un depósito, a la que se le provocan anomalías durante el correcto funcionamiento. Los resultados son altamente satisfactorios, consiguiendo que el sistema implementado detecte los fallos provocados sobre la planta. Como consecuencia del buen rendimiento de este tipo de técnicas en esta aportación, el tercero de los trabajos aborda, con ellas, la detección de fallos sobre la planta de mezclado de compuesto bicomponente del primero de los trabajos, cuya complejidad es notablemente superior. La aplicación de esta estrategia ofrece muy buenos resultados

Detection of DoS Attacks in an IoT Environment with MQTT Protocol Based on Intelligent Binary Classifiers

[Abstract] The present work deals with the problem of detecting Denial of Service attacks in an IoT environment. To achieve this goal, a dataset registered in an MQTT protocol network is used, applying dimension reduction techniques combined with classification algorithms. The final classifiers presents successful results.Xunta de Galicia; ED431G 2019/0

A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

[Abstract] The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results

Virtual Sensor for Fault Detection, Isolation and Data Recovery for Bicomponent Mixing Machine Monitoring

[Abstract] The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results

A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques

[Abstract ]:This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.Junta de Castilla y León; LE078G18. UXXI2018/000149. U-220.Ministerio de Economía, Industria y Competitividad; DPI2016-79960-C3-2-

A novel method for anomaly detection using beta hebbian learning and principal component analysis

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high.CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund and the Secretaría Xeral de Universidades (ref. ED431G 2019/01).info:eu-repo/semantics/publishedVersio

A hybrid one-class approach for detecting anomalies in industrial systems

Financiado para publicación en aberto: Universidade da Coruña/CISUG[Abstract]: The significant advance of Internet of Things in industrial environments has provided the possibility of monitoring the different variables that come into play in an industrial process. This circumstance allows the supervision of the current state of an industrial plant and the consequent decision making possibilities. Then, the use of anomaly detection techniques are presented as a powerful tool to determine unexpected situations. The present research is based on the implementation of one-class classifiers to detect anomalies in two industrial systems. The proposal is validated using two real datasets registered during different operating points of two industrial plants. To ensure a better performance, a clustering process is developed prior the classifier implementation. Then, local classifiers are trained over each cluster, leading to successful results when they are tested with both real and artificial anomalies. Validation results present in all cases, AUC values above 90%.Xunta de Galicia. Consellería de Educación, Universidade e Formación Profesional; ED431G 2019/0

Experiencia de docencia basada en proyectos usando la música como elemento principal para la asignatura de Fundamentos de Electrónica

[Resumen] La tendencia actual y futura en las carreras técnicas y en especial en las ingenierías, es que el número de alumnos se ve cada vez más reducido, bien por falta de vocación o motivación para afrontar una carrera si bien compleja, con una alta demanda laboral. Parte de esa falta de motivación proviene muchas veces de la necesidad de convertir muchos de los contenidos en temáticas más atractivas para los estudiantes. Por ello en esta experiencia de innovación docente, se ha planteado el uso de la música como principal elemento motivador para convertir el mismo contenido de asignaturas, en especial de los primeros cursos, en contenidos más atractivos a los estudiantes y conseguir de este modo, no solo un mayor efecto de motivación sino también que esto se refleje en los resultados finales obtenidos por los estudiantes. En este caso en concreto, se propone el desarrollo e implementación de un circuito electrónico capaz de “buscar” una canción “escondida” entre otras muchas, de modo que se incluye además un aspecto competitivo entre los alumnos. Tras la experiencia, los resultados obtenidos han sido muy positivos, tanto en el aspecto motivacional con un aumento de la participación de los estudiantes en un 22%, así como en los resultados académicos obtenidos.[Abstract] The current and future trend in technical careers and especially in engineering, is that the number of students is increasingly reduced, either due to lack of vocation or motivation to face a career although complex, with a high labor demand. Part of that lack of motivation often comes from the lack of converting many of the content into more attractive topics for students. For this reason, in this experience of teaching innovation, it has proposed the use of music as a main motivating element to convert the same content of subjects, especially the first courses, into more specific content to students and obtain in this way, not only a mayor motivational effect but also that this is reflected in the final students results. In this particular case, it is proposed the development and implementation of an electronic circuit capable of "searching" for a song "hidden" among many others, so that a competitive aspect among students is also included. After the experience, the results obtained have been very positive, both in the motivational aspect with an increase in student participation in more than 22%, as well as in the academic results obtained

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

This paper is the extension of the conference paper: Casteleiro-Roca, J.-L.; Gómez-González, J.F.; Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Acosta Martín, J.F.; Gonzalez Perez, S.; Gonzalez Diaz, B.; Calero-Garcia, F. and Méndez-Perez, J.A. Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model. In Proceedings of the 13th International Conference, Hybrid Artificial Intelligent Systems (HAIS), Oviedo, Spain, 20–22 June 2018.[Abstract] The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resortsFundación CajaCanarias; grant number PR70575

A distributed topology for identifying anomalies in an industrial environment

The devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed.CITIC, as a Research Center of the university System of Galicia, is funded by Conselleria de Education, Universidade e Formacion Profesional of the Xunta de Galicia through the European regional Development Fund (ERDF) and the Secretaria Xeral de Universidades (Ref. ED431G 2019/01)