Feed-forward observer-based intermittent fault detection

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

Feed-forward observer-based intermittent fault detection

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

Evaluating the bovine tuberculosis eradication mechanism and its risk factors in England’s cattle farms

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

Economic Evaluation of Mental Health Effects of Flooding using Bayesian Networks

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

Probabilistic Modeling of Financial Uncertainties

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 paper, we 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. We demonstrate that the extended model based on minimum information Pair-Copula Construction (PCC) can approximate any non-Gaussian BN to any degree of approximation. Our 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

Effective refractive error coverage in adults aged 50 years and older - estimates to monitor progress towards the World Health Organisation's 2030 target

Background In 2021, WHO Member States endorsed a global target of a 40-percentage-point increase in effective refractive error coverage (eREC; with a 6/12 visual acuity threshold) by 2030. This study models global and regional estimates of eREC as a baseline for the WHO initiative. Methods The Vision Loss Expert Group analysed data from 565 448 participants of 169 population-based eye surveys conducted since 2000 to calculate eREC (met need/[met need + undermet need + unmet need]). A binary logistic regression model was used to estimate eREC by Global Burden of Disease (GBD) Study super region among adults aged 50 years and older. Findings In 2021, distance eREC was 79·1% (95% CI 72·4–85·0) in the high-income super region; 62·1% (54·7–68·8) in north Africa and Middle East; 49·5% (45·0–54·0) in central Europe, eastern Europe, and central Asia; 40·0% (31·7–48·2) in southeast Asia, east Asia, and Oceania; 34·5% (29·4–40·0) in Latin America and the Caribbean; 9·0% (6·5–12·0) in south Asia; and 5·7% (3·1–9·0) in sub-Saharan Africa. eREC was higher in men and reduced with increasing age. Global distance eREC increased from 2000 to 2021 by 19·0%. Global near vision eREC for 2021 was 20·5% (95% CI 17·8–24·4). Interpretation Over the past 20 years, distance eREC has increased in each super region yet the WHO target will require substantial improvements in quantity and quality of refractive services in particular for near vision impairment

Global estimates on the number of people blind or visually impaired by cataract:a meta-analysis from 2000 to 2020

BACKGROUND: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals.METHODS: A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity &lt;6/18, ≥3/60) and blindness (presenting visual acuity &lt;3/60). Estimates are age-standardized using the GBD standard population.RESULTS: In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by -27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%).CONCLUSIONS: The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.</p

Effective refractive error coverage in adults aged 50 years and older: estimates from population-based surveys in 61 countries

Background: In 2021, WHO Member States endorsed a global target of a 40-percentage-point increase in effective refractive error coverage (eREC; with a 6/12 visual acuity threshold) by 2030. This study models global and regional estimates of eREC as a baseline for the WHO initiative. Methods: The Vision Loss Expert Group analysed data from 565 448 participants of 169 population-based eye surveys conducted since 2000 to calculate eREC (met need/[met need + undermet need + unmet need]). A binary logistic regression model was used to estimate eREC by Global Burden of Disease (GBD) Study super region among adults aged 50 years and older. Findings: In 2021, distance eREC was 79·1% (95% CI 72·4–85·0) in the high-income super region; 62·1% (54·7–68·8) in north Africa and Middle East; 49·5% (45·0–54·0) in central Europe, eastern Europe, and central Asia; 40·0% (31·7–48·2) in southeast Asia, east Asia, and Oceania; 34·5% (29·4–40·0) in Latin America and the Caribbean; 9·0% (6·5–12·0) in south Asia; and 5·7% (3·1–9·0) in sub-Saharan Africa. eREC was higher in men and reduced with increasing age. Global distance eREC increased from 2000 to 2021 by 19·0%. Global near vision eREC for 2021 was 20·5% (95% CI 17·8–24·4). Interpretation Over the past 20 years, distance eREC has increased in each super region yet the WHO target will require substantial improvements in quantity and quality of refractive services in particular for near vision impairmen

Diagnostic and prognostic of intermittent faults (by use of machine learning).

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