Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

Abstract

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

Gas turbine gas path diagnostics: A review

“…He stated that the diagnostic model based on GA is better than the model based on GPA particularly when sensor noise and bias are considered. In a similar manner, Kong [25] investigated the diagnostic effectiveness of GA in comparison with GPA and fuzzyneuro techniques based on case studies.…”

“…The diagnosis effectiveness of three different GPA methods have been investigated using different test fault cases for double shaft gas turbine engine by Stamatis [24]. Similarly, the effectiveness of GPA methods in comparison with advanced artificial intelligence approaches have been examined and their pros and cons identified based on case studies for twin shaft gas turbine by Kong [25]. Also, Emil Larsson [26] developed a systematic design procedure to construct non-linear model based fault diagnosis method for industrial gas turbines.…”

“…More recently, Chen et al [28] proposed an approach that can select the optimal number of engine measurements for diagnostics. However, GPA techniques can diagnose gas turbine faults if and only if noise and bias doesn't exist and they cannot classify clearly the gas path component faults [25]. …”

“…Fuzzy logic approach for diagnostics application consists of four basic components: fuzzy rules (sets of if-then statements), fuzzifier (the mechanism which maps numbers of input signals into the fuzzy set), inference engine (the technique used to determine the ways in which the fuzzy sets are combined with each other) and defuzzifier (the mechanism used to calculate the output values) [25]. The schematic representation of rule based fuzzy logic system shown in Figure 3.…”

“…The architecture of expert systems consist of four basic elements, user interface (used to acquire information and display results), inference engine (deals with all the reasoning operations of the system based on known facts and rules), knowledge base (contains facts and rules about the problem to offer the appropriate decision) and developer (stores information about current education) [25]. The configuration of an expert system is shown in Figure 4.…”