Pembangkitan Decoupled Residual untuk Isolasi Kesalahan Aktuator Pesawat Terbang Bergerak Lateral

Samiadji Herdjunanto

Abstract


Implementation of time scheduled maintenance is not suitable if it is applied for systems with many varieties of heavy workload and harsh environment, since on that condition components degrade earlier than those under normal condition. Therefore, it has been shifted to condition-based maintenance (CBM). One important aspect, among others, toward successfull implementation of CBM method is fault isolation. The problem to be investigated is related to generate decoupled residual for actuator fault isolation of an aircraft on lateral movement. The proposed solution for that problem is to implement combination of transformation matrix and special filter. Transformation matrix is used to convert feature locations of actuator faults to signature vectors. Moreover, the signature vectors will be processed further by the special filter to generate decoupled residuals. It is assumed that the actuator is the only fault when the aircraft is on lateral movement. The results show that special filter and transformation matrix can be designed so that the residual of aileron actuator fault is decoupled from the residual of rudder actuator fault.

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References


Barlow, R.E & Proschan, F., Statistical Theory of Reliability and Life Testing : Holt ,Reinhart and Winston, 1981.

Ambani,S.,Li,L.&Ni,J.,”Condition-Based Maintenance Decision-Making for Multiple Machines Systems”,Journal of Manufacturing Science and Engineering,vol 131,June 2009.

Donca,G., Mihaila, I. & Nica, M.,”Aspects of Model-Based Diagnostics in Condition Based Maintenance”,Fasicle of Management and Technological Eng,vol VII, 2008.

Trunov, A. B. and Polycarpou, M.M., “Automated Fault Diagnosis in Nonlinear Multivariable Systems Using a Learning Methodology”, IEEE Trans. On Neural Networks, vol.11, no. 1, pp. 91-101, 2000.

Vemuri, A.T., Polycarpou, M.M., “Neural-Network-Based Robust Fault Diagnosis in Robotic Systems”, IEEE Trans. on Neural Networks, vol.8, no.6, pp. 1410-1420, 1997.

Reppa, V., Polycarpou, M.M., Panayiotou, C.G., “Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems”, IEEE Trans. on Neural Networks and Learning Systems, vol.20, no. 1, pp. 137-153, 2014.

Schneider, H., Frank, P.M., “Observer-Based Supervision and Fault Detection in Robots Using Nonlinear and Fuzzy Logic Residual Evaluation”, IEEE Trans. on Control Systems Technology, vol. 19, no. 5, pp. 1260-1268, 1996.

Thumati, B.T., Feinstein, M.A., Jagannathan, S., “A Model-Based Fault Detection and Prognostics Scheme for Takagi-Sugeno Fuzzy Systems”, IEEE Trans. on Fuzzy Systems, vol. 22, no. 4, pp. 736-748, 2014.

Herdjunanto,S., Susanto,A. & Wahyunggoro, O., “Robust Residual Generation for Sensor Fault Isolation in Systems with Structured Uncertainty. A Case Study: MIMO Web Winding System”, International Conference on Information Technology and Electrical Engineering , 2014.

Tsui, C.C., “A Complete Analytical Solution to the Equation TA-FT=LC and Its Applications”, IEEE Trans on Automatic Control,vol.AC-32.no.8,pp 742-744, 1987.

Klema, V.C. and Laub, A.J., “The Singular Value Decomposition:Its Computation and Some Applications”, IEEE Trans on Automatic Control, vol.AC-25,no.2,pp 164-176, 1980.

McLean D., Automatic Flight Control Systems, Prentice Hall, 1990.




DOI: http://dx.doi.org/10.22146/jnteti.v5i3.263

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