Pablo H. Ibargüengoytia

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ion and Refinement for Solving Continuous Markov Decision Processes Alberto Reyesand Pablo Ibargüengoytia Inst. de Inv. Eléctricas Av. Reforma 113, Palmira, Cuernavaca, Mor., México {areyes,pibar},mx L. Enrique Sucar and Eduardo Morales INAOE Luis Enrique Erro 1, Sta. Ma. Tonantzintla, Pue., México {esucar,emorales}
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is(More)
Power transformers are some of the most important equipment for the transmission and distribution of electric power. A single failure in a transformer causes disturbances in the electric network and may cause severe conflicts in hospitals, banks, industrial installations or urban areas in general. In Mexico, the transmission network is composed by 350 power(More)
The validation of data from sensors has be­ come an important issue in the operation and control of modern industrial plants. One ap­ proach is to use know ledge based techniques to detect inconsistencies in measured data. This article presents a probabilistic model for the detection of such inconsistencies. Based on probability propagation, this method is(More)
For many real time applications, it is impor­ tant to validate the information received from the sensors before entering higher levels of reasoning. This paper presents an any time probabilistic algorithm for validating the in­ formation provided by sensors. The sys­ tem consists of two Bayesian network mod­ els. The first one is a model of the dependen­(More)
This paper introduces a novel approach for fault diagnosis based on probabilistic models. This approach is suitable for applications where reliable measurements are unlikely to occur or where a deterministic analytical model is difficult to obtain. In particular, a combination of two Bayesian networks is used to detect and isolate faulty components. One(More)
Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware(More)
Modern control systems and other monitoring systems require the acquisition of values of most of the parameters involved in the process. Examples of processes are industrial procedures or medical treatments or financial forecasts. However, sometimes some parameters are inaccessible through the use of traditional instrumentation. One example is the blades(More)