Sergio Luis Martínez

Fault diagnosis for a MSF using neural networks

Abstract This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.

Formulation of an optimal academic exam

espanolEl objetivo de este trabajo es formular un examen academico optimo para una materia dada. Para ello, primero, se modela la probabilidad de que un estudiante apruebe el examen en funcion del numero de unidades que estudia y de las que el profesor evalua. Ese modelo de simulacion es desarrollado realizando un analisis probabilistico. Un examen optimo es luego definido como aquel que asigna la nota que el estudiante merece. Por lo tanto, en un examen optimo, aprueban quienes merecen aprobar, y desaprueban quienes no merecen aprobar. Ademas, el examen debe respetar las limitaciones de tiempo y esfuerzo que el profesor impone. En base a esta definicion y usando el modelo de simulacion, se formula un modelo de optimizacion del tipo INLP. Este modelo de optimizacion determina el numero de unidades que el profesor debe evaluar para maximizar la probabilidad de conseguir un examen optimo. EnglishThe aim of this paper is to formulate an optimal academic exam for a given subject. To do this, the probability is first modelled of a student passing the exam according to the number of units he studies and the professor evaluates. That simulation model is developed by performing a probabilistic analysis. An optimal exam is then defined as the one that awards the grade that the student deserves. Therefore, in an optimal exam, approve those who deserve to approve, and disapprove those that do not deserve to approve. Besides, this exam must respect the limitations of time and effort that the professor imposes. Based on this definition and using the simulation model, an INLP type optimization model is formulated. This optimization model determines the number of units the professor must evaluate to maximize the probability of getting an optimal exam.