AIRCRAFT JET ENGINE CONDITION MONITORING THROUGH SYSTEM IDENTIFICATION BY USING GENETIC PROGRAMMING

In this thesis a new approach for aircraft jet engine condition monitoring is proposed based on system identification and by using Genetic Programming (GP). This approach consists of two fault detection and isolation parts. In the detection part, the relationship between the engine Exhaust Gas Temperature (EGT), as a major indicator of the engine health condition, and other engine parameters and operating conditions corresponding to different phases of the flight is modelled using the GP technique. Towards this end, flight characteristics are divided into several phases such as the take-off and the cruise. The GP scheme is then used to discover the structure of the interrelations among engine variables. The constructed model is then used to detect abrupt faults in the engine performance. For the isolation purpose, a hierarchical approach is proposed which narrows down the number of possible faults toward the target fault. The GP algorithm is then exploited to extract a series of nonlinear functions of the engine variables called fault indices. These indices attempt to magnify the signature of a fault in the engine by combining the effects of a fault on the engine parameters. These indices subsequently provide the necessary residuals for classifying the faults. The approaches developed in this thesis provide an effective strategy for inspecting the aircraft jet engine health condition without requiring any specific information on the engine internal characteristics. The main advantage of the proposed approaches over other data driven methods such as neural networks is that our approaches provide a simple and tangible mathematical model of the engine rather than a black box model. The performance of the proposed algorithms are demonstrated and illustrated by implementing them on a double spool jet engine data that is generated by using the Gas turbine Simulation Program (GSP) software

Detecting discontinuities using nonparametric smoothing techniques in correlated data

There is increasing interest in the detection and estimation of discontinuities in regression problems with one and two covariates, due to its wide variety of applications. Moreover, in many real life applications, we are likely to encounter a certain degree of dependence in observations that are collected over time or space. Detecting changes in dependent data in the presence of a smoothly varying trend, is a much more complicated problem that previously has not been adequately studied. Hence, the aim of this thesis is to respond to the immense need for a nonparametric discontinuity test which is capable of incorporating robust estimation of the underlying dependence structure (if unknown) into the test procedure in one and two dimensions. By means of a difference-based method, using a local linear kernel smoothing technique, a global test of the hypothesis that an abrupt change is present in the smoothly varying mean level of a sequence of correlated data is developed in the one-dimensional setting. Accurate distributional calculations for the test statistic can be performed, using standard results on quadratic forms. Extensive simulations are carried out to examine the performance of the test in the cases both of correlation known and unknown. For the latter, the effectiveness of the different algorithms that have been devised to incorporate the estimation of correlation, for both the equally and unequally spaced designs, is investigated. Various factors that affect the size and power of the test are also explored. In addition, a small simulation study is performed to compare the proposed test with an isotonic regression test proposed by Wu et al. (2001). The utility of the techniques is demonstrated by applying the proposed discontinuity test to three sets of real-life data, namely the Argentina rainfall data, the global warming data and the River Clyde data. The analysis of the results are compared to those using the isotonic regression test of Wu et al. (2001) and the Bayesian test of Thomas (2001). Finally, the test is also extended to detect discontinuities in spatially correlated data. The same differencing principle as in the one-dimensional case is utilised here. However, the discontinuity in this context does not occur only at a point but over a smooth curve. Hence, the test has to take into account the additional element of direction. A two stage algorithm which makes use of a partitioning process to remove observations that are near the discontinuity curve is proposed. A motivating application for the approach is the analysis of radiometric data on cesium fallout in a particular area in Finland after a nuclear reactor accident in Chernobyl. The procedures outlined for both the one and two dimensional settings are particularly useful and relatively easy to implement. Although the main focus of the work is not to identify the exact locations of the discontinuities, useful graphical tools have been employed to infer their likely locations. The dissertation closes with a summary and discussion of the results presented, and proposes potential future work in this area