Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions.

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En la actualidad la detección de anomalías en procesos industriales es clave para optimizarlos y generar una mayor eficiencia en el proceso productivo, reportando unos mayores beneficios a las empresas. Por ello, en el presente artículo se implementan cinco técnicas de clasificación supervisadas para la detección de anomalías en sistemas industriales. Estas técnicas han sido entrenadas y validadas empleando un conjunto de datos que incluían datos etiquetados de funcionamiento normal y anómalo de una planta de control de nivel de líquido en un depósito. Finalmente, los resultados obtenidos fueron analizados y comparados para obtener el modelo con el que se obtiene un mayor rendimiento.