Siemens gas turbines are monitored and controlled by a large number of sensors and actuators. Process information is stored in a database and used for offline calculations and analyses. Before storing the sensor readings, a compression algorithm checks the signal and skips the values that explain no significant change. Compression of 90 % is not unusual. Since data from the database is used for analyses and decisions are made upon results from these analyses it is important to have a system for validating the data in the database. Decisions made on false information can result in large economic losses. When this project was initiated no sensor validation system was available. In this thesis the uncertainties in measurement chains are revealed. Methods for fault detection are investigated and finally the most promising methods are put to the test. Linear relationships between redundant sensors are derived and the residuals form an influence structure allowing the faulty sensor to be isolated. Where redundant sensors are not available, a gas turbine model is utilized to state the input-output relationships so that estimates of the sensor outputs can be formed. Linear parametric models and an ANN (Artificial Neural Network) are developed to produce the estimates. Two techniques for the linear parametric models are evaluated; prediction and simulation. The residuals are also evaluated in two ways; direct evaluation against a threshold and evaluation with the CUSUM (CUmulative SUM) algorithm. The results show that sensor validation using compressed data is feasible. Faults as small as 1% of the measuring range can be detected in many cases.

This work presents a black box identification for a continuous methanol-water distillation column setting in open loop and closed loop response. Step changes and Pseudo-Random Binary Signal (PRBS) disturbance were used to excite the plant. The mathematical models candidates to identify were the Artificial Neural Networks (ANN) and the parametric models: ARX(autoregressive with exogenous inputs ), ARMAX (AutoRegressive Moving Average with eXogenous inputs ), OE(Output Error) and the Box-Jenkins (BJ)structure. The closed loop configuration was the R-V. The results showed that for the bottom loop, the best response were given by BJ, OE and RNA for both open and closed loop response. For the top closed loop, the best responses were also given by BJ, OE and RNA while in open loop condition, the RNA was the one that gave satisfactory outcome. It was verified that the pseudo-random binary signal was a good choice of excitation signal in identification for both open loop and closed dynamic systems. / Foi realizado neste trabalho identificação caixa preta do processo de destilação Metanol-Água nas configurações malha aberta e malha fechada, utilizando como sinais de perturbação a função degrau e o Sinal Binário Pseudo-Aleatório (PRBS) para excitar a planta. Os modelos matemáticos candidatos a identificação foram as Redes Neurais Artificiais (RNA), e os modelos paramétricos discretos lineares autorregressivo com entradas externas (ARX do inglês AutoRegressive with eXogenous Inputs), autorregressivo com média móvel e entradas exógenas (ARMAX do inglês AutoRegressive Moving Average with eXogenous Inputs), modelo do tipo erro na saída (OE do inglês Output Error) e a estrutura Box-Jenkins (BJ). Com a disposição dos modelos, foram comparados quais dos modelos matemáticos candidatos à identificação melhor representa o processo coluna de destilação metanol-água. Comparou-se qual configuração do processo no ensaio de identificação para geração de dados apresenta mais vantagens, se em malha aberta ou em malha fechada, nas condições e metodologias utilizadas. Constatou-se a funcionalidade do sinal binário pseudo-aleatório como uma boa opção de excitação na identificação em malha aberta e fechada para sistemas dinâmicos.