Sumislawska, Malgorzata

English

There are two main threads of this thesis, namely, an unknown (unmeasurable) input reconstruction and fault detection and diagnosis. The developed methods are in the form of parity equations, i.e. finite impulse response filters of the available input and output measurements. In the first thread the design of parity equations for the purpose of an unknown input reconstruction of linear, time-invariant, discrete-time, stochastic systems is taken into consideration. An underlying assumption is that both measurable system inputs as well as the outputs can be subjected to noise, which leads to an errors-in-variables framework. The main contribution of the scheme is accommodation of the Lagrange multiplier method in order to minimise the influence of the noise on the unknown input estimate. Two potential applications of the novel input reconstruction method are proposed, which are a control enhancement of a hot strip steel rolling mill and an estimation of a pollutant level in a river. Furthermore, initial research is conducted in the field of the unknown input recon- struction for a class of nonlinear systems, namely, Hammerstein-Wiener systems, where a linear dynamic block is preceded and followed by a static nonlinear function. Many man-made as well as naturally occurring systems can be accurately described using Hammerstein-Wiener models. However, it is considered that not much attention has been paid to Hammerstein-Wiener systems in the errors-in-variables framework and in this thesis it is aimed to narrow this gap. The second thread considers a problem of robust (disturbance decoupled) fault de- tection as well as fault isolation and identification. Unmeasurable external stimuli, parameter variations or discrepancies between the system and the model act as distur- bances, which can obstruct the fault detection process and lead to false alarms. Thus, a fault detection filter needs to be decoupled from the disturbances. In this thesis the right eigenstructure assignment method used for the robust fault detection filter design is extended to systems with unstable invariant zeros. Another contribution regards the design of robust parity equations of any arbitrary order using both left and right eigenstructure assignment. Furthermore, a parity equation-based fault isolation and identification filter is designed which provides an estimate of the fault. A simple method for the calculation of thresholds whose violation indicates a fault occurrence is also proposed for the errors-in-variables framework

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Fault detection and diagnosis and unknown input reconstruction based on parity equations concept

https://pure.coventry.ac.uk/ws/files/41257618/Sumislawska2012.pdf

Fault detection and diagnosis and unknown input reconstruction based on parity equations concept

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