Accurate gas turbine fault detection and diagnosis (FDD) is essential to improving airline safety as well as in reducing airline costs associated with delays and cancellations. In this paper, we present FDD methods based on feature extraction methods using nonlinear principal component analysis (NLPCA) and curvilinear component analysis (CCA). The underlying principle of both methods is to find the most representative feature space corresponding to gas turbine normal and faulty operations. During operation, new sensor data is located in this feature space and then it is determined whether a particular fault is indicated. NLPCA is an extension of linear PCA methods to the nonlinear domain; therefore, it is intrinsically better suited to nonlinear domains such as the gas turbine engine. The CCA method is another approach to clustering having superior properties for determining cluster manifolds automatically compared to the popular selforganizing map (SOM) method of clustering. The developed methods are tested with snapshot data collected at takeoff, both normal and faulty, from a turbofan gas turbine propulsion engine and the results are presented.
Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.