Similarity measure application to fault detection of flight system

Abstract

Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data (pitching moment coefficients and lift coefficients) are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.

Keywords

similarity measure / fuzzy number, distance / non-convex membership function

References

[1] RonaldR. Y.. Monitored heavy fuzzy measures and their role in decision making under uncertainty [J]. Fuzzy Sets and Systems, 2003, 139(3): 491-513

[2] RébilléY.. Decision making over necessity measures through the Choquet integral criterion [J]. Fuzzy Sets and Systems, 2006, 157(23): 3025-3039

[3] SugumaranV., SabareeshG. R., RamachandranK. I.. Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine [J]. Expert Systems with Applications, 2008, 34(4): 3090-3098

[4] KangW. S., ChoiJ. Y.. Domain density description for multiclass pattern classification with reduced computational load [J]. Pattern Recognition, 2008, 41(6): 1997-2009

[5] ShihF. Y., ZhangK.. A distance-based separator representation for pattern classification [J]. Image and Vision Computing, 2008, 26(5): 667-672

[6] ChenS.-ming.. New methods for subjective mental workload assessment and fuzzy risk analysis [J]. Cybern Syst, 1996, 27(5): 449-472

[7] ChenS. J., ChenS. M.. Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers [J]. IEEE Trans on Fuzzy Systems, 2003, 11(1): 45-56

[8] LeeS. H., CheonS. P., KimJ. H.. Measure of certainty with fuzzy entropy function [J]. Lecture Notes in Artificial Intelligence, 2006, 4114: 134-139

[9] LeeS. H., KimJ. M., ChoiY. K.. Similarity measure construction using fuzzy entropy and distance measure [J]. Lecture Notes in Artificial Intelligence, 2006, 4114: 952-958

[10] LiuX.-cheng.. Entropy, distance measure and similarity measure of fuzzy sets and their relations [J]. Fuzzy Sets and Systems, 1992, 52(2): 305-318

[11] BaoZ.-j., ZhongW.-m., PiD.-y., SunY.-xian.. Robustly stable model predictive control based on parallel support vector machines with linear kernel [J]. Journal of Central South University of Technology, 2007, 14(5): 701-707

[12] MORELLI E A. High accuracy evaluation of the finite Fourier transform using sampled data [R]. NASA-TM-110340, National Aeronautics and Space Administration, 1997.

[13] MORELLI E A. Identification of low order equivalent system models from flight test data [R]. NASA-TM-210117, National Aeronautics and Space Administration, 2000.

[14] ParkW. J., KimW. T., SeongK. J., KimY. C.. A study on the parameter estimation of DURUMI-II for the fixed right elevator using flight test data [J]. Journal of Mechanical Science and Technology, 2006, 20(8): 1224-1231

[15] ParkW. J., KimE. T., SongY. K., KoB. J.. A study on the real-time parameter estimation of DURUMI-II for control surface fault using flight test data [J]. International Journal of Control, Automation, and Systems, 2007, 5(4): 410-418