Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Markov-based performance evaluation and availability optimization of the boiler–furnace system in coal-fired thermal power plant using PSO

The appropriate maintenance strategy is essential for maintaining the thermal power plant highly reliable. The thermal power plant is a complex system that consists of various subsystems connected either in series or parallel configuration. The boiler–furnace (BF) system is one of the most critical subsystems of the thermal power plant. This paper presents availability based simulation modeling of the boiler–furnace system of thermal power plant with capacity (500MW). The Markov based simulation model of the system is developed for performance analysis. The differential equations are derived from a transition diagram representing various states with full working capacity, reduced capacity, and failed state. The normalizing condition is used for solving the differential equations. Furthermore, the performance of the system is analyzed for a possible combination of failure rate and repair rate, which revealed that failure of the boiler drum affects the system availability at most, and the failure of reheater affects the availability at least. Based on the criticality ranking, the maintenance priority has been provided for the system.The availability of the boiler–furnace system is optimized using particle swarm optimization method by varying the number of particles. The study results revealed that the maximum system availability level of 99.9845% is obtained. In addition, the optimized failure rate and repair rate parameters of the subsystem are used for suggesting an appropriate maintenance strategy for the boiler–furnace system of the plant. The finding of the study assisted the decision-makers in planning the maintenance activity as per the criticality level of subsystems for allocating the resources

Reliability Modelling For Asset Management in South East Water

Over the years, the reliability modelling of water assets has generated increasing interest among both researchers and practitioners. Statistical methods and software packages for assessing asset reliability have been developed in order to improve asset availability, indirectly reduce water losses, and hence improve the efficiency of water assets. OFWAT, which is the economic regulator of the water sector in England and Wales, aims to ensure that water companies operate under their statutory functions and have sufficient financial means to perform these functions adequately. Water companies need to prepare a five-year business plan for OFWAT, in order to certify they have enough capital and are transparent when carrying out their statutory functions. Hence, this thesis aims to analyse the reliability of two selected types of assets at South East Water to help plan their future investments on vehicles and future maintenance costs on borehole assets. This thesis will provide an extensive literature review on reliability modelling in water distribution networks. An MS Excel-based decision support system will be developed for both vehicles and borehole assets, using data collected from South East Water. For the transport model, a block replacement policy will be developed by using Visual Basic, to obtain the optimum time of replacing a vehicle. Performance analysis will be conducted on the borehole data to pinpoint the worst performers among the 16 boreholes under analysis

The Management of Power System Reliability in the Offshore Oil and Gas Field

The reliability of electric power systems is needed in offshore oil and gas field operations because disruptions can have a direct impact on oil production, costs, and company profits.Objectives: to determine the evaluation of reliability performance and propose a model for managing the power system reliability of the PHE OSES offshore oil and gas field.Methodology: A case study with data collected through observation, interviews, and analysis of relevant documents.Finding: Reliability in 2015-2016 has not reached the target but from 2017-2021 has reached the target of 97.5%, as well as availability in 2015, 2018, 2019 & 2020 has not reached the target but in 2016, 2017 & 2021 has reached the target of 95%. This resulted in the highest production losses in 2015 at 266,965 and the lowest in 2021 at 21,388 barrels of oil. The model is proposed by combining elements in risk-based asset management method, RCM method, and redesign.Conclusion: In recent years, reliability has been on target but availability is volatile. The model is proposed to maintain reliability & availability on target so that production losses can be minimized

Quantitative methods for data driven reliability optimization of engineered systems

Particle accelerators, such as the Large Hadron Collider at CERN, are among the largest and most complex engineered systems to date. Future generations of particle accelerators are expected to increase in size, complexity, and cost. Among the many obstacles, this introduces unprecedented reliability challenges and requires new reliability optimization approaches. With the increasing level of digitalization of technical infrastructures, the rate and granularity of operational data collection is rapidly growing. These data contain valuable information for system reliability optimization, which can be extracted and processed with data-science methods and algorithms. However, many existing data-driven reliability optimization methods fail to exploit these data, because they make too simplistic assumptions of the system behavior, do not consider organizational contexts for cost-effectiveness, and build on specific monitoring data, which are too expensive to record. To address these limitations in realistic scenarios, a tailored methodology based on CRISP-DM (CRoss-Industry Standard Process for Data Mining) is proposed to develop data-driven reliability optimization methods. For three realistic scenarios, the developed methods use the available operational data to learn interpretable or explainable failure models that allow to derive permanent and generally applicable reliability improvements: Firstly, novel explainable deep learning methods predict future alarms accurately from few logged alarm examples and support root-cause identification. Secondly, novel parametric reliability models allow to include expert knowledge for an improved quantification of failure behavior for a fleet of systems with heterogeneous operating conditions and derive optimal operational strategies for novel usage scenarios. Thirdly, Bayesian models trained on data from a range of comparable systems predict field reliability accurately and reveal non-technical factors' influence on reliability. An evaluation of the methods applied to the three scenarios confirms that the tailored CRISP-DM methodology advances the state-of-the-art in data-driven reliability optimization to overcome many existing limitations. However, the quality of the collected operational data remains crucial for the success of such approaches. Hence, adaptations of routine data collection procedures are suggested to enhance data quality and to increase the success rate of reliability optimization projects. With the developed methods and findings, future generations of particle accelerators can be constructed and operated cost-effectively, ensuring high levels of reliability despite growing system complexity

A case study material flow simulation based analysis for maintenance network improvement

The competitiveness of enterprises operating in complex environments depends on how well their value creation factors can adapt to disruptions caused by unanticipated events. Building this resilience requires the ability to identify uncertainties and to model their impact on operations, which is difficult to achieve. Thus, increasing adaptability in maintenance and repair networks calls for an adequate approach to address uncertainties. It is necessary to consider the maintenance activities within and outside the company as well as those affecting all equipment supplier partners. Enhancement in simulation technique has opened the opportunity to analyse this complex system. This paper presents a comprehensive analysis introducing a potential approach using material flow simulation that models and simulates the impact of existing maintenance and repair activities to identify the uncertainties to increase the flexibility of the network while ensuring profitability and continuity