Gas turbine engine and sensor fault diagnosis
Substantial economic and even safety related gains can be achieved if effective gas turbine performance analysis is attained. During the development phase, analysis can help understand the effect on the various components and on the overall engine performance of the modifications applied. During usage, analysis plays a major role in the assessment of the health status of the engine. Both condition monitoring of operating engines and pass off tests heavily rely on the analysis. In spite of its relevance, accurate performance analysis is still difficult to achieve. A major cause of this is measurement uncertainty: gas turbine measurements are affected by noise and biases. The simultaneous presence of engine and sensor faults makes it hard to establish the actual condition of the engine components. To date, most estimation techniques used to cope with measurement uncertainty are based on Kalman filtering. This classic estimation technique, though, is definitely not effective enough. Typical Kalman filter results can be strongly misleading so that even the application of performance analysis may become questionable. The main engine manufactures, in conjunction with research teams, have devised modified Kalman filter based techniques to overcome the most common drawbacks. Nonetheless, the proposed methods are not able to produce accurate and reliable performance analysis. In the present work a different approach has been pursued and a novel method developed, which is able to quantify the performance parameter variations expressing the component faults in presence of noise and a significant number of sensor faults. The statistical basis of the method is sound: the only accepted statistical assumption regards the well known measurement noise standard deviations. The technique is based on an optimisation procedure carried out by means of a problem specific, real coded Genetic Algorithm. The optimisation based method enables to concentrate the steady state analysis on the faulty engine component(s). A clear indication is given as to which component(s) is(are) responsible for the loss of performance. The optimisation automatically carries out multiple sensor failure detection, isolation and accommodation. The noise and biases affecting the parameters setting the operating point of the engine are coped with as well. The technique has been explicitly developed for development engine test bed analysis, where the instrumentation set is usually rather comprehensive. In other diagnostic cases (pass off tests, ground based analysis of on wing engines), though, just few sensors may be present. For these situations, the standard method has been modified to perform multiple operating point analysis, whereby the amount of information is maximised by simultaneous analysis of more than a single test point. Even in this case, the results are very accurate. In the quest for techniques able to cope with measurement uncertainty, Neural Networks have been considered as well. A novel Auto-Associative Neural Network has been devised, which is able to carry out accurate sensor failure detection and isolation. Advantages and disadvantages of Neural Network-based gas turbine diagnostics have been analysed.