Shuying Li

Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis

Gas-path analysis method has been widespread applied to gas turbine engine health control and has become one of the key techniques in favor of condition-oriented maintenance strategy. Theoretically, gas-path analysis method (especially nonlinear gas-path analysis) can easily quantify gas-path component degradations. However, when the number of components within engine is large which highly expands the dimension of fault coefficient matrix, maybe leading to strong smearing effect (i.e. predicted degradations are located almost in all component health parameters, although some of them are not really degraded), the degraded components may not be accurately identified. In order to improve the robustness of gas turbine gas-path fault diagnosis, a hybrid gas-path analysis approach integrating gray relation algorithm into gas-path analysis method has been proposed in this study. The gray relation algorithm and gas-path analysis method approach includes two steps. First, the faulty components are recognized and isolated by gray relation algorithm, which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the gas-path analysis. The fault classification analyses and case studies have shown that the confidence level of the fault classification can reach more than 95%, when single and multiple components are degraded, and the predicted degradations are almost same as that of implanted fault patterns.

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA) method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA) has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.

Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm

Aiming at the nonlinear and nonstationary characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm was proposed in this article. First, an improved generalized fractal box-counting dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, to offer more useful and distinguishing information imaging different bearing health status in comparison with traditional fractal box-counting dimension. After feature extraction by improved generalized fractal box-counting dimension algorithm, an adaptive gray relation algorithm, in which the concept of weight coefficient and adaptive distinguishing coefficient was introduced into the calculation of the relation degree, was employed to fulfill an intelligent bearing fault diagnosis. The experimental results demonstrate that the proposed approach can more effectively and accurately identify different bearing fault types as well as severities compared with the existing intelligent methods, and it can solve the learning problem with an extremely small number of samples.

Prediction of Viscosity of Nanofluids Using Artificial Neural Networks

The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, a novel viscosity prediction approach using artificial neural networks (ANN) is introduced as an alternative to the model-based viscosity prediction approach to provide a quick and accurate estimation of nanofluids viscosity. Radial basis function (RBF) neural networks has been utilized to form viscosity prediction architectures. Alumina (Al2O3)-water nanofluids from existing literatures were used to test the effectiveness of the proposed method. The results showed that RBF neural network model had a reasonable agreement in predicting experimental data. The findings of this paper indicated that the ANN model was an effective method for prediction of the viscosity of nanofluids and had better prediction accuracy and simplicity compared with the other existing theoretical methods.Copyright

Method of Extrapolating Low Speed Compressor Curves Based on Improved Similarity Laws

Similarity laws for pumps are only applicable to incompressible fluids, and they can’t be directly applied to the extrapolation of compressor characteristic maps. But by varying the exponent of the relative rotational speed ratio to reflect the influence of compressibility, improved similarity laws can be applied to compressors where compressible fluid is considered. This paper proposes a new similarity law for compressor map extrapolation to low speeds by using the two lowest available speed lines and proposes a method of calculating the exponent. A comparison between calculated and real compressor performance of a compressor indicated that this exponent extrapolation method is simple, generic and has acceptable accuracy. The obtained low speed lines of compressors can be applied in gas turbine performance simulations to estimate the starting process of gas turbine engines.Copyright

Research on Simulink/Fluent Collaborative Simulation Zooming of Marine Gas Turbine

Based on the detailed analysis of collaborative running interface of Simulink/Fluent, a system simulation for the rated working condition as well as variable working condition of marine gas turbine has been achieved, which can improve the simulation efficiency of marine gas turbine by developing simulation model of combustor with Fluent and simulation models of other components with Simulink. The result shows that the Simulink/Fluent collaborative simulation zooming can make the inner working conditions of combustor be observed specifically, based on the overall performance matching analysis; thus an effective technical means for the structural optimization design of combustor has been provided.Based on the detailed analysis of collaborative running interface of Simulink/Fluent, a system simulation for the rated working condition as well as variable working condition of marine gas turbine has been achieved, which can improve the simulation efficiency of marine gas turbine by developing simulation model of combustor with Fluent and simulation models of other components with Simulink. The result shows that the Simulink/Fluent collaborative simulation zooming can make the inner working conditions of combustor be observed specifically, based on the overall performance matching analysis; thus an effective technical means for the structural optimization design of combustor has been provided.

Off-Design Behavior Analysis and Operating Curve Design of Marine Intercooled Gas Turbine

The intercooled gas turbine obtained by adopting an indirect heat exchanger into an existing gas turbine is one of the candidates for developing high-power marine power units. To simplify such a strong coupled nonlinear system reasonably, the feasibility and availability of qualifying equivalent effectiveness as the only parameter to evaluate the intercooler behavior are investigated. Regarding equivalent effectiveness as an additional degree of freedom, the steady state model of a marine intercooled gas turbine is developed and its off-design performance is analyzed. With comprehensive considerations given to various phase missions of ships, operational flexibility, mechanical constraints, and thermal constraints, the operating curve of the intercooled gas turbine is optimized based on graphical method in three-dimensional performance space. The resulting operating curve revealed that the control strategy at the steady state conditions for the intercooled gas turbine should be variable cycle control. The necessity of integration optimization design for gas turbine and intercooler is indicated and the modeling and analysis method developed in this paper should be beneficial to it.

The Off-Design Performance Simulation of Marine Gas Turbine Based on Optimum Scheduling of Variable Stator Vanes

As one of the antisurge techniques, the adjusting scheme of VSV under off-design conditions has a significant impact on the performance of gas turbines. In this paper, the one-dimensional characteristic of the compressor calculation program is embedded into the zero-dimensional overall gas turbine model, which replaces the original compressor characteristic module. Based on the assembling relationship of the actual components of the marine gas turbine, the architecture of the modular model library is designed, and an integrated simulation platform of marine gas turbine is developed by using MATLAB/GUI software. The influence of the first 3 rows of variable stator vanes of the 9-stage axial compressor working alone on the performance of the compressor at different speeds and different angles was analyzed by the HARIKA compressor characteristic calculation program. Taking the economics and stability of the gas turbine as the optimization objective, the optimization of the first three-stage stator vanes regulation schemes under different working conditions was carried out. The steady-state performance parameters under each working condition of gas turbine of power generation with or without variable stator vane mode were calculated. The study results can provide references for the adjusting scheme of VSV under gas turbine off-design conditions operating process.