Browsing by Author "Sedighi, Tabassom"

Now showing 1 - 8 of 8
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    Diagnostic and prognostic of intermittent faults (by use of machine learning).
    (Cranfield University, 2020-02) Sedighi, Tabassom; Foote, Peter D.; Starr, Andrew
    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.
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    Economic evaluation of mental health effects of flooding using Bayesian networks
    (MDPI, 2021-07-13) Sedighi, Tabassom; Varga, Liz; Hosseinian-Far, Amin; Daneshkhah, Alireza
    The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.
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    Evaluating the bovine tuberculosis eradication mechanism and its risk factors in England’s cattle farms
    (MDPI, 2021-03-26) Sedighi, Tabassom; Varga, Liz
    Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms.
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    Feed-forward observer-based intermittent fault detection
    (Elsevier, 2017-09-17) Sedighi, Tabassom; Foote, P. D.; Sydor, Piotr
    This paper provided an approach to design feed-forward observer for nonlinear systems with Lipchitz nonlinearity and bounded unknown inputs (disturbances/uncertainties) to ensure the sensitivity against intermittent faults. The proposed observer design guarantees the system error stability. Some variables and scalars are also introduced to design observer's parameters, which bring more degrees of flexibility available to the designer. The designed observer is used to propose a precision fault detection scheme including adaptive threshold design to detect intermittent faults. The efficiency of the considered approach is examined by the intermittent failure case in the suspension system of a vehicle. Simulation results show that the accurate state estimation and fault detection are achieved successfully.
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    Mathematical and computational modelling frameworks for integrated sustainability assessment (ISA)
    (Springer, 2017-02-15) Farsi, Maryam; Hosseinian-Far, Amin; Daneshkhah, Alireza; Sedighi, Tabassom
    Sustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g., the Earth’s global climate, social organisations, Boeing’s supply chain, automotive products and plants’ operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems’ responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system’s responses experiencing interaction change or interruption (i.e., interactive disruption). Having this knowledge will allow the optimisation of systems’ efficiency and would ultimately reduce the system’s total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system following interactive changes and interruptions. Moreover, a dynamic adaptive response of the global system over time should be taken into account. This dynamic behaviour can capture the interactive behaviour of components and sub-systems within a complex global system. Such assessment would benefit many systems including information systems. Due to emergence and expansion of big data analytics and cloud computing systems, such life-cycle assessments can be considered as a strategic planning framework before implementation of such information systems.
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    Probabilistic modeling of financial uncertainties
    (IGI Global, 2018-04-30) Daneshkhah, Alireza; Hosseinian-Far, Amin; Chatrabgoun, Omid; Sedighi, Tabassom; Farsi, Maryam
    Since the global financial crash, one of the main trends in the financial engineering discipline has been to enhance the efficiency and flexibility of financial probabilistic risk assessments. Creditors could immensely benefit from such improvements in analysis hoping to minimise potential monetary losses. Analysis of real world financial scenarios require modeling of multiple uncertain quantities with a view to present more accurate, near future probabilistic predictions. Such predictions are essential for an informed decision making. In this article, the authors extend Bayesian Networks Pair-Copula Construction (BN-PCC) further using the minimum information vine model which results in a more flexible and efficient approach in modeling multivariate dependencies of heavy-tailed distribution and tail dependence as observed in the financial data. The authors demonstrate that the extended model based on minimum information Pair-Copula Construction (PCC) can approximate any non-Gaussian BN to any degree of approximation. The proposed method has been applied to the portfolio data derived from a Brazilian case study. The results show that the fitting of the multivariate distribution approximated using the proposed model has been improved compared to other previously published approaches.
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    A review of methods to study resilience of complex engineering and engineered systems
    (IEEE, 2020-05-13) Naghshbandi, S. Neda; Varga, Liz; Purvis, Alan; McWilliam, Richard; Minisci, Edmondo; Vasile, Massimiliano; Troffaes, Matthias; Sedighi, Tabassom; Guo, Weisi; Manley, Ed; Jones, David H.
    Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.
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    Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients
    (MDPI, 2021-06-09) Vepa, Abhinav; Saleem, Amer; Rakhshan, Kambiz; Daneshkhah, Alireza; Sedighi, Tabassom; Shohaimi, Shamarina; Omar, Amr; Salari, Nader; Chatrabgoun, Omid; Dharmaraj, Diana; Sami, Junaid; Parekh, Shital; Ibrahim, Mohamed; Raza, Mohammed; Kapila, Poonam; Chakrabarti, Prithwiraj
    Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.