A combined technique of Kalman filter,artificial neural network and fuzzy logic for gas turbines and signal fault isolation

The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively.This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics.     

Particle image velocimetry for combustion measurements: Applications and developments

In the last several decades, Particle Image Velocimetry (PIV) has reached a high degree of maturity as a laser diagnostic technique based on tracer particles, with significant improvements in accuracy, resolution, dynamic range, and as an extension to combustion measurements. To assess the recent developments and to project the future trends of using the PIV technique for combustion measurements, we review many key issues for measuring combustion flow fields. We introduce the representative applications of a supersonic combustor and swirling burner and summarize the promising prospects and further development requirements of PIV measurements in combustion flow fields.     

Potential markets for application of space medicine achievements

The Institute of Biomedical Problems (IBMP) is the lead institution of the Russian Federation in the area of space biology and medicine. It has successfully implemented a set of innovation-based activities and projects to develop and introduce promising space products and technologies into the practices of Earth health care.     

电弧加热发动机喷出等离子体静电单探针测量

应用静电单探针测量了kW级电弧加热发动机喷出等离子体参数分布。发动机以氮气为推进剂,测量范围为距离发动机喷口1.25～15cm,实验中真空舱压力为3～5Pa。实验获得了电子温度和电子数密度沿不同轴向位置和径向位置的变化规律,电子温度为0.4～0.8eV,电子数密度为1.2×1017～1.25×1018m-3。对比研究发现,N2为推进剂产生的等离子体的电子温度和电子数密度均大于N2-H2为推进剂的情况,但前者发散角较小。     

A review of uncertainty in flight vehicle structural damage monitoring,diagnosis and control: Challenges and opportunities

This paper presents a comprehensive review of uncertainties involved in flight vehicle structural damage monitoring, diagnosis, prognosis and control. Uncertainties can cause infeasibilities, false diagnosis and very imprecise prognosis if not correctly taken into account. The purpose of this paper is to review existing methods that have been developed to address the problem of uncertainty in the area of damage sensing, diagnosis, prognosis and control in flight vehicles. The mathematical and statistical methods in analyzing uncertainty are first presented and compared. Then, the different sources and perspectives of uncertainties in the damage assessment process are presented and classified. Following this, diagnosis and prognosis methods are reviewed. Final review section covers the control of damaged structure under uncertainty. In each section and in the concluding remarks section the research challenges in the field of flight vehicle structural damage sensing, diagnosis and prognosis methods as well as control under uncertainty are identified and promising new ideas are discussed.