Gas turbine fault identification has been used worldwide in many aero and land engines. Model based techniques have improved isolation of faults in components and stages’ fault trend monitoring. In this paper a powerful nonlinear fault identification system is developed in order to predict the location and trend of faults in two major components: compressor and turbine. For this purpose Siemens V94.2 gas turbine engine is modeled one dimensionally. The compressor is simulated using stage stacking technique, while a stage by stage blade cooling model has been used in simulation of the turbine. New fault model has been used for turbine, in which a degradation distribution has been considered for turbine stages’ performance. In order to validate the identification system with a real case, a combined fault model (a combination of existing faults models) for compressor is used. Also the first stage of the turbine is degraded alone while keeping the other stages healthy. The target was to identify the faulty stages not faulty components. The imposed faults are one of the most common faults in a gas turbine engine and the problem is one of the most difficult cases. Results show that the fault diagnostic system could isolate faults between compressor and turbine. It also predicts the location of faulty stages of each component. The most interesting result is that the fault is predicted only in the first stage (faulty stage) of the turbine while other stages are identified as healthy. Also combined fault of compressor is well identified. However, the magnitude of degradation could not be well predicted but, using more detailed models as well as better data from gas turbine exhaust temperature, will enhance diagnostic results.

Performance testing of gas turbine packages is becoming increasingly common to assure that the turbine output power and efficiency meet the expected values during the turbine life cycle. In the conventional Performance Test Analysis (PTA), field measurements and calculations are carried out on the basis of standard codes to find the whole engine performance parameters (i.e. power and efficiency) at test conditions and to compare them with the expected values. Recently, regarding the development of Gas Path Analysis (GPA) and diagnostic techniques to investigate the gas turbine health state, performance test capabilities can be improved by using these analyses to perform further examination on the measured test data and to determine the deviation of gas turbine component health parameters from the “new and clean” health state during the engine operation. Determining the mentioned deviations, potentials of engine improvement in the component level can be obtained and subsequently the action-oriented recommendations are reported as guidelines in the overhaul. Also in the case of performance test after the overhaul, the main result of the GPA application in PTA is the verification of the overhaul effectiveness. Using the GPA in the cases studied in this paper indicates that heath state of engine components can be investigated from the performance test data and as the main result, it is show that applying the GPA, it is possible to distinguish the effect of non-recoverable degradation and that of the poor overhaul on the engine performance and finally to assess technically the effectiveness of overhaul.

Simulation and prediction of gas turbine performance is a very important issue in design process or in actual behavior analysis. In these models physical behavior of components such as compressors, combustion chambers and turbines are simulated related to each other. The compressor is the most important part of simulation. This paper presents a model for simulating a compressor using stage stacking procedure with the aid of a genetic algorithm. The most important feature of the proposed method is that qualitative and quantitative rules based on turbo-machinery knowledge of compressors are used as constraints to the genetic algorithm to find the corrected situations of design. This knowledge is evaluated with both industrial and aero gas turbine engines (501F & CF6 (LM2500)). The model is based on an analytical solution and provides an insight into the effects of choices made during the compressor design process on performance and off-design stage matching. The results of the model highlight the capability of the method which accurately reproduces the available data. In addition to obtaining design conditions, this model can find and calculate stages that are highly loaded and this information is vital to control the compressor.