Contact Tabassom Sedighi
Tabassom gained her BSc in Physics and undertook her MSc in Control Engineering (Coventry University, UK). Her primary research interests span physics, control engineering, mathematical modelling, adaptive controller design, nonlinear observer design, fault detection and isolation, error stability analysis, solving differential equations and Lyapunov equations, data analysis, risk analysis and predicting residual useful life. Her PhD was under the supervision of Prof. Peter Foote and Prof. Ian Jennions at Cranfield University as part of the No Fault Found (NFF) project sponsored by EPSRC and BAE systems. She has been actively involved in the NFF project regarding the intermittent fault detection and prediction of test facilities for assessment of intermittent fault patterns and deployment statistical methods (Dynamic Bayesian Network, Gaussian process, etc.) for intermittent fault prediction.
Tabassom is a research fellow in the Complex System Research Centre. She has responsibility as research fellow for interpreting case studies, collected data, practitioner practices and policies for governance, into a Systems Dynamics model capable of handling the requisite variety of cases and developing insights into the evaluation of different policies. The significance of the tool is that it will enable users to formulate their own systems descriptions avoiding the need for knowledge and skills in systems dynamics programming. The usability of the tool will be critical so it will be developed using participative approaches with representatives from energy, environment and food governance bodies. She is also working with the CECAN team to understand how this SD tool fits into the landscape of methods for complexity evaluation. She will be disseminating findings and helping to develop capacity in policy makers and related stakeholders by supporting their use of the SD tool.
Articles In Journals
- Daneshkhah A, Hosseinian-Far A, Chatrabgoun O, Sedighi T & Farsi M (2018) Probabilistic modeling of financial uncertainties, International Journal of Organizational and Collective Intelligence, 8 (2) Article No. 1 1-11.
- Sedighi T, Foote PD & Sydor P (2017) Feed-forward observer-based intermittent fault detection, CIRP Journal of Manufacturing Science and Technology, 17 (May) 10-17.
- T Sedighi, PD Foote & S Khan (2014) The Performance of Observer-based Residuals for Detecting Intermittent Faults: The Limitations, Procedia CIRP, 22 65-70.
- Sedighi T, Khan S & Foote P. (2018) Intermittent fault detection on an experimental aircraft fuel rig: Reduce the No Fault Found rate. In: 2015 4th International Conference on Systems and Control (ICSC), Sousse, 28-30 April 2015.
- Sedighi T, Phillips P & Foote P. (2013) Model-based intermittent fault detection. In: 2nd International Through-life Engineering Services Conference, Durham University, 5-6 November 2013.
- Sedighi T, Koshkouei AJ & Burnham K. (2010) Nonlinear unknown input observer design for nonlinear systems. In: UKACC International Conference on Control 2010, Coventry, 7-10 September 2010.
- Sedighi T, Koshkouei AJ & Burnham KJ. (2008) Observer-Based Residual Design for Nonlinear Systems. In: UKACC Control Conference, University of Manchester, 2-4 September 2008.
- Sedighi T & Koshkouei AJ. (2007) Optimal-Backstepping Approach for Fault Detection of Nonlinear Systems. In: Intelligent Systems and Control (ISC 2007), Cambridge, MA, 19-21 November 2007.
- Sedighi T, Koshkouei AJ & Burnham K. (2006) Fault detection for massspring-damper systems: backstepping approach. In: 18th International Conference on Systems Engineering (ICSE–06),, Control Theory & Applications Centre, Coventry University, 8-10 September 2006.
- Daneshkhah A, Hosseinian-Far A, Sedighi T & Farsi M (2017) Prior elicitation and evaluation of imprecise judgements for Bayesian analysis of system reliability. In: Strategic Engineering for Cloud Computing and Big Data Analytics. Hosseinian-Far A, Ramachandran M, Sarwar D (ed.), Springer International Publishing, p. 63-79. Dataset/s: 10.1007/978-3-319-52491-7_4
- Farsi M, Hosseinian-Far A, Daneshkhah A & Sedighi T (2017) Mathematical and computational modelling frameworks for integrated sustainability assessment (ISA). In: Strategic Engineering for Cloud Computing and Big Data Analytics. Hosseinian-Far A, Ramachandran M, Sarwar D (ed.), Springer International Publishing, p. 3-27. Dataset/s: 10.1007/978-3-319-52491-7_1