Noise filtering from a nonlinear system by using AANN
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In order to run a gas turbine, the operator (be it human or automatic) needs to monitor the conditions of the various parts inside it. Pressures, temperatures, mass flows, vibrations, power output. These properties all need to be controlled in order to run the gas turbine optimally. And in order for the operator to make the necessary adjustments, sensors inside the gas turbine are needed to monitor said properties. As the industry drives towards higher efficiencies and lower emissions, the accuracy of these sensor readings inside the gas turbine become more and more important. The objective of this thesis then, was to see how this accuracy could be improved by the use of autoassociative neural networks (AANN), which is a kind of noise filter. Sensor readings will not be completely accurate, since the technology is not perfect. One problem is something called random noise, meaning sensor measurements that are scattered randomly close to the exact value. A noise filter will take these scattered measurements and move them all closer to the exact value. It is already known that an AANN can perform this task, and in this thesis the main objective was to find some indication of just how effective it is as a noise filter. In order to measure how effective a noise filter is, one would ideally need one set of measurements, which are noisy, and one set of corresponding measurements, which are not noisy at all (perfect measurements). Checking the level of noise reduction then would be to first filter the noisy measurements, and then comparing both the filtered and noisy measurements to the perfect measurements. Such a solution can not be found with real measurements from a gas turbine, because they are never perfect. But if the measurements were calculated using thermodynamic and physics equations, they would not contain noise. They would be completely theoretical, but they would not contain noise. Synthetic measurements like these were generated by the use of a software which can model gas turbines and calculate theoretical properties for various theoretical scenarios. Noise was then added to these noise free measurements in order to emulate the real gas turbine. And with that, two sets of measurements were available: One set of noisy measurements, and one set of perfect measurements. With the use of the MATLAB neural network toolbox, these sets of measurements were used to test the effectiveness of an AANN as a noise filter. The noisy measurements were filtered through an AANN, and the filtered and noisy measurements were then compared to the perfect measurements. Artificial neural networks (ANN), which also have some noise filtering abilities, were also tested this way. But not as extensively as the AANN. Results showed that there was indeed noise reduction, but not for all the individual parameters in the measurements. For some parameters, the AANN achieved very good noise filtering, but for other parameters there was no effect. The reason for this is not entirely clear, but earlier two purely mathematical examples were tested in order for the author to familiarize himself with the methodology. And these examples only had twothree parameters; few enough to visualize in graphs (2and 3 dimensional). In these two examples, there was found a trend which suggested that an AANN does not filter each parameter individually, but rather all parameters together as if they were one. The author can not prove this, but he speculates the same principle could apply to measurements that have more than three parameters as well, which means that an AANN might not be very ideal for noise filtering of individual sensors inside a gas turbine. It the future, it could certainly be interesting to test an AANN on measurements from a real gas turbine. Several conditions would need to be met for such a test to prove useful; like extensive correlations between the different parameters included, and redundant measurements. But it is not unreasonable to assume there would be some reduction of noise.
Master's thesis in Structural engineering