Rough Set Diagnostic Frameworks for Gas Turbine Fault Classification

Abstract

Fault classification has become one of the main features in gas turbine health monitoring. Hence techniques such as gas path analysis, artificial neural networks, expert systems, fuzzy logic and many others have been developed for this purpose in the past. In this paper, an alternative rough set based diagnostic method using enhanced fault signatures combined with three fault classification frameworks for gas turbine fault classification have been introduced, i.e. Framework 1 with a single step to classify single and dual component faults, Framework 2 with the first step to identify weather it is a single or dual component faults and the second step to identify the individual faults, and Framework 3 with the first step to identify faults associated with component types and the second step to identify the individual faults. Such frameworks have been applied to the fault classification of a model two-spool turbofan gas turbine engine implemented with single and dual component faults to test the effectiveness of the frameworks. It has been demonstrated in the application that all three framework configurations can provide satisfactory fault classification and that Framework 1 has higher fault classification success rate than that of Frameworks 2 and 3. In addition, Frameworks 2 and 3 have better performance in identifying fault types than Framework 1.

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

“…From the linear GPA method developed by Urban in the late 1960's [13], a series of GPA methods were proposed, such as an adaptive nonlinear GPA method [7], artificial neural networks [3], rule-based expert system and rule-based fuzzy expert system [11], and genetic algorithm [4,12,17]. The merit of artificial intelligence methods, such as neural network, rough set [14], Bayesian network [9,10] and rule based expert system, is that they do not need a gas turbine performance model, as only the relation information between fault symptom and degradation is needed. They can easily isolate the faulty component.…”

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

“…Many gas-path analysis approaches have been proposed to estimate the performance and health status for gas turbine. From the linear GPA method developed by Urban in the late 1960s [2], a series of GPA methods were proposed, such as thermodynamic model based GPA methods (e.g., adaptive linear and nonlinear GPA methods [3][4][5] and genetic algorithm based GPA method [6][7][8][9]) and artificial intelligence based GPA methods (e.g., artificial neural networks [10,11], rule based expert system [12][13][14], and rule based fuzzy expert system [15]).…”

A study on fault diagnostic method for the lube oil system of gas turbine based on rough sets theory

“…As a method of data analysis, the biggest advantage of rough sets theory is only to provide the data set that need to be processed, and other priori information is not essential, and the algorithm of the theory is simple and easy to operate [13] . Rough sets theory is widely used in artificial strategy decision system, pattern recognition, knowledge mining and other fields [14][15][16] .…”