Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern classification techniques for diagnosing gas path faults. In recent investigations many techniques have been applied to classify gas path faults, but recommendations for selecting the best technique for real monitoring systems are still insufficient and often contradictory. In our previous work, three classification techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy for each comparison case. The present contribution considers a new classification technique, Probabilistic Neural Network (PNN), and we compare it with the techniques previously examined. The results for all comparison cases show that the PNN is not inferior to the other techniques. We recommend choosing the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.

The use of Inductive Wireless Power Transfer (IWPT) varies from low-power applications such as mobile phones and tablets chargers to high-power electric vehicles chargers. DC–DC converters are used in IWPT systems, and their design needs to consider the demand of high efficiency in the power transfer. In this paper, a DC–DC power converter for IWPT is proposed. Its topology uses a DC–AC converter in the transmitter circuit and an AC–DC converter in the receptor. The transmitter has an interleaved coupled-Buck converter that integrates two Buck converters connected to a half inverter bridge and a parallel resonant load. The control strategy implemented for the semiconductor switching devices allows two operating modes to obtain a sinusoidal output voltage with a low distortion that makes it suitable in high-efficiency power transfer systems. To obtain a DC output voltage, a full wave bridge rectifier is used in the receptor circuit. The proposed topology and the control strategy are validated with simulation and experimental results for a 15 W prototype.

Gas turbine health monitoring includes the common stages of problem detection, fault identification, and prognostics. To extract useful diagnostic information from raw recorded data, these stages require a preliminary operation of computing differences between measurements and an engine baseline, which is a function of engine operating conditions. These deviations of measured values from the baseline data can be good indicators of engine health. However, their quality and the success of all diagnostic stages strongly depend on the adequacy of the baseline model employed and, in particular, on the mathematical techniques applied to create it. To create a baseline model, we have applied polynomials and the least squares method for computing the coefficients over a long period of time. Methods were proposed to enhance such a polynomial-based model. The resulting accuracy was sufficient for reliable monitoring of gas turbine deterioration effects. The polynomials previously investigated thus far are used in the present study as a standard for evaluating artificial neural networks, a very popular technique in gas turbine diagnostics. The focus of this comparative study is to verify whether the use of networks results in a better description of the engine baseline. Extensive field data for two different industrial gas turbines were used to compare these two techniques under various conditions. The deviations were computed for all available data, and the quality of the resulting deviation plots was compared visually. The mean error of the baseline model was used as an additional criterion for comparing the techniques. To find the best network configurations, many network variations were realised and compared with the polynomials. Although the neural networks studied were found to be close to the polynomials in accuracy, they did not exceed the polynomials in any variation. In this way, it seems that polynomials can be successfully used for engine monitoring, at least for the gas turbines analysed herein.

A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2^® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.

A modern gas turbine engine (GTE) is a complex nonlinear dynamic system with the mutual effect of gas-dynamic and thermal processes in its components. The engine development requires the precise real-time simulation of all main operating modes. One of the most complex operating modes for modeling is “cold stabilization,” which is the rotors acceleration without completely heated up the turbine elements. The dynamic heating problem is a topical practical issue. Solving the problem requires coordinating a gas-path model with heat and stress models, which is also a significant scientific problem. The phenomenon of interest is the radial clearances change during engines operation and its influence on engines static and dynamic performances. To consider the clearance change, it is necessary to synthesize the quick proceeding stress-state models (QPSSM) of a rotor and a casing for the initial temperature and dynamic heating. The unique feature of the QPSSM of GTEs is separate equation sets, which allow the heat exchange between structure elements and the gas (air) and the displacements of the turbine rotor and the casing. This ability appears as a result of determining the effect of each factor on different structural elements of the engine. The presented method significantly simplifies the model identification, which can be performed based on a precise calculation of the unsteady temperature fields of the structural elements and the variation of the radial clearance. Thus, the present paper addresses a new method to model the engine dynamics considering its heating up. The method is based on the integration of three models: the gas-path dynamics model, the clearance dynamics model, and the model of the clearance effect on the efficiency. The paper also comprises the program implementation of the models. The method was tested by applying to a particular turbofan engine.