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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An Introduction to MultiAgent Systems.

Thermoelectrics: a review of present and potential applications

https://www.iit.comillas.edu/docs/IIT-06-055A-Preview.pdf

SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a windturbine gearbox

Food Track & Trace ontology for helping the food traceability control

https://www.iit.comillas.edu/docs/IIT-11-172A-Preview.pdf

General asset management model in the context of an electric utility: Application to power transformers

https://www.iit.comillas.edu/docs/IIT-14-030A-Preview.pdf

Ontology matching system for future energy smart grids

This paper presents a new method able to estimate the health condition of components in a wind turbine based on the on-line information collected about their observable lives The proposed method uses the information coming in real-time to characterize risk indicators for failure modes of the main components of a wind turbine operating under different normal conditions The estimation of these risk indicators is based on normal behaviour models previously fitted with real data about the typical life of a component carrying out its functions within its own environment The maintenance plan applied to the components of a wind turbine can be dynamically rescheduled according to the observed values of the risk indicators in a component using the resources that are really needed Two approaches are presented to determine thresholds for alerting about risky health conditions: a maximum limit that the risk indicator should not overpass according to its life condition, and technical and economical feasibility These approaches are the main foundations for a new maintenance model able to integrate in a natural way different information coming from the operation and maintenance of a component, and so capable of maximising the lifespan of the asset Some real examples of the application of these new concepts in components of a wind turbine will be described

https://www.iit.comillas.edu/docs/IIT-13-129A-Preview.pdf

Miguel A. Sanz-Bobi

Papers

SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a windturbine gearbox

Food Track & Trace ontology for helping the food traceability control

General asset management model in the context of an electric utility: Application to power transformers

Ontology matching system for future energy smart grids

Failure Risk Indicators for a Maintenance Model Based on Observable Life of Industrial Components With an Application to Wind Turbines