This thesis investigates novel intermittent fault detection and prediction techniques
for complex nonlinear systems.
Aerospace and defence systems are becoming progressively more complex, with
greater component numbers and increasingly complicated components and subcomponents.
At the same time, faults and failures are becoming more challenging to detect
and isolate, and the time that operators and maintenance technicians spend on faults is
rising.
Moreover, a serious problem has recently attracted a lot of attention in health diagnostics
of these complex systems. Detecting intermittent faults that persist for very
short durations and manifest themselves intermittently have become troublesome and
sometimes impossible (also known as “no fault found”).
In response to the above challenges, this thesis focuses on the development of a novel
methodology to detect intermittent faults of these complex systems. It further investigates
various probabilistic approaches to develop efficient fault diagnostic and prognostic
methods.
In the first stage of this thesis, a novel model (observer)-based intermittent fault detection
filter is presented that relies on the creation of a mathematical model of a laboratory scale
aircraft fuel system test rig to predict the output of the system at any given time.
Comparison between this prediction of output and actual output reveals the presence
of a fault. Later, the simulation results demonstrate that the performance of the model
(observer)-based fault detection techniques decrease significantly as system complexity
increases.
In the second stage of this research, a probabilistic data-driven method known as
a Bayesian network is presented. This is particularly useful for diverse problems of
varying size and complexity, where uncertainties are inherent in the system. Bayesian
networks that model sequences of variables are called dynamic Bayesian networks. To
introduce the time variable in the framework of probabilistic models while dealing with
both discrete and continuous variables in the fuel rig system, a hybrid dynamic Bayesian
network is proposed.
The presented results of data-driven fault detection show that the hybrid dynamic
Bayesian network is more effective than the static Bayesian network or model (observer)-
based methods for detecting intermittent faults.
Furthermore, the second stage of the research uses all the information captured from
the fault diagnostic techniques for intermittent fault prediction by using a probabilistic
non-parametric Bayesian method called Gaussian process regression, which is an aid for
decision-making using uncertain information.Engineering and Physical Sciences (EPSRC)PhD in Manufacturin