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Journal of Failure Analysis and Prevention

, Volume 11, Issue 4, pp 417–431 | Cite as

Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

  • S. K. Yang
Technical Article---Peer-Reviewed

Abstract

A 25 kVA generator is one of the most important components in the power system of an indigenous defensive fighter (IDF). However, the generators have failed frequently since being equipped on IDFs. The poor reliability and long repair time of a 25 kVA generator have seriously impacted the availability of IDFs. This study aims to build a failure-processing mechanism for the 25 kVA generators based on the state-estimation function of the Kalman filter. By way of this mechanism, the left- or right-side load of the fighter can be switched from the supplied generator that is going to fail to the other generator before the abnormal generator actually fails. Therefore, this mechanism not only mitigates the impact resulting from the irregular power on systems of the fighter, but also obtains reaction time to shutdown the generator that is going to fail so as to perform a preventive maintenance or other effective actions on the generator such that the further failures can be avoided. In the simulations of this study, the failure times predicted by the Kalman filter are compared with the results obtained by Monte Carlo simulation for judging the prediction accuracy. In order to simulate more accurately, an exponential attenuator is placed at each end of the outputs of both the generator and the Kalman filter so as to conform to the actual aging model. The prediction errors are acceptable. Besides, reliabilities of the generators in four different configurations are compared to know the effect of redundancy design.

Keywords

25 kVA generator Kalman filter Monte Carlo simulation Failure prediction 

List of Symbols

Ac, Bc, Cc, Dc

Coefficient matrix of the state equation for a continuous system

AD, BD, CD, DD

Coefficient matrix of the state equation for a discrete system

AT

Transpose matrix of A

A−1

Inverse matrix of A

B

Damping coefficient

Bk

Coefficient matrix for the input term of a discrete state equation

Hk

Measurement sensitivity matrix

Kk

Kalman gain

Pk/k−1

Estimation error covariance matrix

Qk

Plant noise covariance matrices

Rk

Measurement noise covariance matrices

Uk

Control input of a discrete state equation at state k

Vk

Noise, measurement error vector

Wk

Disturbance, system stochastic input vector

\( X_{{D_{0} }} \)

Initial states resulting from deterministic input

Xk

System state vector at state k

\( X_{{S_{0} }} \)

Initial states resulting from stochastic input

Yk

System output vector at state k

Zk

Output measurement vector

Φk

State transition matrix

ωr

Angular speed of the rotor, electrical rad/s

ω0

Rated speed of the rotor, electrical rad/s

δ

Rotation angle of the rotor, electrical radians

Tm

Mechanical torque, N·m

Te

Electromagnetic torque, N·m

KS

Synchronizing torque coefficient, pu torque/rad

KD

Damping torque coefficient, pu torque/pu speed deviation

H

Inertia constant, MW·s/MVA

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Copyright information

© ASM International 2011

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Chin Yi University of TechnologyTaichung 411Taiwan, R.O.C

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