Fault detection in the electrohydraulic actuator using Extended Kalman Filter
Date
2004-03
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ORCID
Type
Degree Level
Doctoral
Abstract
In this thesis a fault detection technique for a high performance hydrostatic
actuation system was developed and evaluated. The Extended Kalman Filter (EKF)
was used for parameter identification and was applied to an Electrohydraulic Actuator
(ERA) and the performance of the technique is discussed. The ERA is a high
performance, closed loop actuation system consisting of an AC variable speed electric
motor, a bi-directional gear pump, an accumulator, check valves, a cross-over relief
valve, connecting tubes and a custom made symmetrical actuator. The ERA has
potential applications in the aerospace industry for flight surface actuation and in
robotics. Failures in the ERA can pose a safety hazard and unscheduled maintenance
can result in costly downtime. Fault detection in the ERA will increase its safety and
efficiency.
The proposed preventive maintenance approach involves monitoring the ERA
by estimating two parameters of interest, namely the effective bulk modulus and the
viscous damping coefficient. Lowering of the effective bulk modulus, as a result of air
entrapment, will affect the response of the ERA and may cause stability issues, by
lowering the bandwidth of the system. Changes in the damping coefficient for the
actuator can indicate deterioration of the oil, wear in the seals or changes in external
friction characteristics. The two parameters were estimated using the EKF and changes
in the estimated values were related to faults in the system.
Prior to applying the EKF to the ERA prototype, an extensive simulation study
was carried out to investigate the feasibility of the approach as well as the level of
accuracy to be expected with the experimental system. The simulation study was used
to verify that changes in the two parameters were detected and accurately estimated. In this study, an attempt was also made to visit some of the problems reported
with the use of the EKF for fault detection purposes, namely the difficulty in setting the
correct values in the matrices to initialize the EKF algorithm and the presence of biases
in the estimates. The problem was believed to be linked to system observability which
was investigated in this research. It was found that using observable state space models
for the EKF improved the ability of the EKF to estimate parameters, both in terms of
accuracy of the estimations and repeatability of experimental results. System
observability was investigated in this work by first using simple mechanical systems
and then using the more complex ERA system. An iterative approach was presented
whereby parameters were not estimated at the same time but iteratively and using
different models. System observability was maintained by reducing the number of states
and by using the correct type and number of system measurements. Also, the use of
observable systems eliminated the need to choose parameter values, in the initial state
vector of the EKF, close to the desired parameter values, as was very often done in
previous research. No a-priori knowledge about the parameters was assumed in this
research. Biases in the estimates (this has been reported in previous studies) are
believed to be due to the filter facing a local minima problem. This problem is linked to
the error covariance matrix not converging to a global minimum. In the Kalman Filter,
the main objective of the error covariance matrix is to compute the Kalman gain, which
is in turn used to correct an estimate with the latest sensor measurement. Errors in the
Kalman gain may lead to biases in the estimates. In this study, it was also found that
although the system is not observable, it can be detectable, although the converse is not
true, and as such, changes in parameters can be detected but not necessarily accurately
estimated. Observability ensures uniqueness of the estimate.
The effective bulk modulus and viscous damping coefficient were estimated
successfully, both in simulations and using experimental data. Faults were introduced in
the ERA prototype and changes in the parameters were detected and estimated.
The friction characteristic of the actuator for the ERA was also investigated. A
novel empirical friction model was proposed. The EKF was used to estimate iteratively
(to maintain system observability), the coefficients of that friction function which was
believed to be a realistic representation of friction effect in the prototype. Simulation
and experimental results were presented. In summary, the application of the EKF
technique to the ERA has produced very promising results.
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Degree
Doctor of Philosophy (Ph.D.)
Department
Mechanical Engineering
Program
Mechanical Engineering