Abstract
It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.
Highlights
With the deepening of the industrialization process, the revolution of industry 4.0 is emerging in many industry areas [1]
The accuracy of the algorithm should be increased; The diagnostic result based on steady-state data and dynamic data should be compared; The diagnostic method based on transient process data should be used to analyze field data
To promote diagnosis based on the transient process data from theoretical study to commercial application, a novel diagnostic method based on a dynamic simulation model and Cuckoo search
Summary
With the deepening of the industrialization process, the revolution of industry 4.0 is emerging in many industry areas [1]. Many gas turbine performance-analysis-based diagnostic technologies have been developed since Urban [11] introduced the first gas path analysis method in 1967. Li developed a non-linear-model-based diagnostic method, combined with a genetic algorithm, and applied it to a model gas turbine engine to diagnose engine faults by using the accumulated deviation obtained from transient measuring data [8]. Current research results cannot support the fault diagnostic technology for a gas turbine based on transient process data. The accuracy of the algorithm should be increased; The diagnostic result based on steady-state data and dynamic data should be compared; The diagnostic method based on transient process data should be used to analyze field data. Due to the change of input parameters from steady-state to transient process, the three key parts of the diagnostic model, which are calculation flowchart, simulation model, and algorithm, should be modified. This problem will be solved by the proposed calculation flowchart and heuristic algorithm
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