The complexity of engineered systems has increased remarkably to meet
customer needs. In the continuously growing global market, it is essential for
engineered systems to keep their productivities which can be achieved by higher
reliability and availability. Integrated health management based on diagnostics
and prognostics provides significant benefits, which includes increasing system
safety and operational reliability, with a significant impact on the life-cycle costs,
reducing operating costs and increasing revenues. Characteristics of complex
systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity
make diagnostics and prognostics more challenging tasks and decrease the
application of classic reliability methods remarkably – as they cannot address the
dynamic behaviour of these systems.
This research has focused on detecting, predicting and isolating faults in
engineered systems, using operational data with multifarious data characteristics.
Complexities in the data, including non-Gaussianity and high nonlinearity, impose
stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden
Markov modelling (HMM) and Bayesian network (BN) were employed to detect,
predict and isolate faults in a system. The fault detection and prediction were
based on comparing and exploiting pattern similarity in the data via the loglikelihood
values generated through HMM training. To identify the root cause of
the faults, the probability values obtained from updating the BN were used which
were based on the virtual evidence provided by HMM training and log-likelihood
values. To set up a more accurate data-driven model – particularly BN structure
– engineering analyses were employed in a structured way to explore the causal
relationships in the system which is essential for reliability analysis of complex
engineered systems.
The automotive exhaust gas Aftertreatment system is a complex engineered
system consisting of several subsystems working interdependently to meet
emission legislations. The Aftertreatment system is a highly nonlinear, dynamic
and non-stationary system. Consequently, it has multifarious data characteristics,
where these characteristics raise the challenges of diagnostics and prognostics
for this system, compared to some of the references systems, such as the
Tennessee Eastman process or rolling bearings. The feasibility and effectiveness
of the presented framework were discussed in conjunction with the application to
a real-world case study of an exhaust gas Aftertreatment system which provided
good validation of the methodology, proving feasibility to detect, predict, and
isolate unidentified faults in dynamic processes.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19747 |
| Date | January 2021 |
| Creators | Soleimani, Morteza |
| Contributors | Campean, I. Felician, Neagu, Daniel |
| Publisher | University of Bradford, Faculty of Engineering and Informatics |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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