Gas turbine performence based creep life estimation using soft computing technique

Accurate and simple prediction system has become an urgent need in most disciplines. Having the accurate prediction system for gas turbine components will allow the user to produce reliable creep life prediction. Focusing on the turbine blades and its life, the current method to calculate its creep life is complex and consumes a lot of time. For this reason, the aim of this research is to use an alternative performance–based creep life estimation that is able to provide a quick solution and obtain accurate creep life prediction. By the use of an artificial neural network to predict creep life, a neural network architecture called Sensor Life Based (SLB) architecture that produces a direct mapping from gas path sensor to predict the blade creep life was created by using the gas turbine simulation performance software. The performance of gas turbine and the effects of multiple operations on the blade are studied. The result of the study is used to establish the input and output to train the Sensor Life Based network. The result shows that the Sensor Life-Based architecture is able to produce accurate creep life predictions yet performing rapid calculations. The result also shows that the accuracy of prediction depends on the way, how the gas path sensor is grouped together

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

A fibre optic sensor for the measurement of surface roughness and displacement using artificial neural networks

This paper presents a fiber optic sensor system, artificial neural networks (fast back-propagation) are employed for the data processing. The use of the neural networks makes it possible for the sensor to be used both for surface roughness and displacement measurement at the same time. The results indicate 100% correct surface classification for ten different surfaces (different materials, different manufacturing methods, and different surface roughnesses) and displacement errors less then ±5 μm. The actual accuracy was restricted by the calibration machine. A measuring range of ±0.8 mm for the displacement measurement was achieved