Bayesian network approach to fault diagnosis of a hydroelectric generation system

This study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic-mechanical-electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time-based maintenance to transform to the condition-based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy-Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system

APPLICATION OF FORMAL SAFETY ASSESSMENT FOR DRY DOCKING EVOLUTION

This research has evaluated the rules, guidelines and regulations related to docking a ship in floating-graving yards. Historical failure data analysis is carried out to identify associated components, equipment and the area of defects related to ship docking evolution problems. The current status of ship docking evolution is reviewed and possible sources which cause accidents are recognised. The major problems identified in this research are associated with risk modelling under circumstances where high levels of uncertainty exist. Following the identification of research needs, this work has developed several analytical models for the application of Formal Safety Assessment (FSA). Such models are subsequently demonstrated by their corresponding case studies with regards to application of FSA for ship docking evolution. Firstly, in this research a generic floating-graving docking model is constructed for the purpose of hazard identification and risk estimation. The hazards include various scenarios, identified from literature reviewed as the major contributors to ship docking failures. Then risk estimation is carried out utilising fault tree (FT) – FSA where there is sufficient data. Secondly, with increased lack of data, risk estimation is carried out using FT-Bayesian network (BN) where interdepencies exists amongst identified hazards. This risk estimation method is validated with the appropriate case study identified. Thirdly, fuzzy rule base and evidential reasoning approaches are used for risk estimation in terms of three risk parameters to select the major causes of component failure that can lead to pontoon deck failure in a floating dock. Possible risk control options (RCOs) are introduced, based on their effectiveness, to select the best RCO for minimising the risks. Finally, a cost benefit assessment is conducted to select the best risk control option using BN, where selections are based on economic terms. The four subjective novel FSA application methodologies in ship docking evolution are constructed from existing theoretical techniques and applied to real situations where data collection is otherwise not possible. The construction of the novel methodologies and the case study applications are the major contribution to knowledge in this thesis. It is concluded that the methodologies proposed possess significant potential for the application of FSA for ship docking evolution based on the validations of their corresponding case studies, which may also be applied with domain specification knowledge tailored to facilitate FSA application in other shipping industry sectors

Artificial intelligence models for refrigeration, air conditioning and heat pump systems

Artificial intelligence (AI) models for refrigeration, heat pumps, and air conditioners have emerged in recent decades. The universal approximation accuracy and prediction performances of various AI structures like feedforward neural networks, radial basis function neural networks, adaptive neuro�fuzzy inference and recurrent neural networks are encouraging interest. This review discusses existing topographies of neural network models for RHVAC system modelling, energy prediction and fault(s), and detection and diagnosis. Studies show that AI structures require standardization and improvement for tuning hyperparameters (like weight, bias, activation functions, number of hidden layers and neurons). The selection of activation functions, validation, and learning algorithms depends on author’s suitability for a particular application. Backpropagation, error trial selection of the number of hidden layer, and hidden layers’ neurons, and Levenberg–Marquardt learning algorithms, remain prevalent methodologies for developing AI structures. The major limitations to the application of AI models in RHVAC systems include exploding or/and vanishing gradients, interpretability, and accuracy trade off, and training saturation and limited sensitivity. This review aims to give up-to-date applications of different AI architectures in RHVAC systems and to identify the associated limitations and prospect

Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation

Process hazard analysis, hazard identification and scenario definition: are the conventional tools sufficient, or should and can we do much better?

Hazard identification is the first and most crucial step in any risk assessment. Since the late 1960s it has been done in a systematic manner using hazard and operability studies (HAZOP) and failure mode and effect analysis (FMEA). In the area of process safety these methods have been successful in that they have gained global recognition. There still remain numerous and significant challenges when using these methodologies. These relate to the quality of human imagination in eliciting failure events and subsequent causal pathways, the breadth and depth of outcomes, application across operational modes, the repetitive nature of the methods and the substantial effort expended in performing this important step within risk management practice. The present article summarizes the attempts and actual successes that have been made over the last 30 years to deal with many of these challenges. It analyzes what should be done in the case of a full systems approach and describes promising developments in that direction. It shows two examples of how applying experience and historical data with Bayesian network, HAZOP and FMEA can help in addressing issues in operational risk management

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

A framework for aerospace vehicle reasoning (FAVER)

Airliners spend over 9% of their total revenue in Maintenance, Repair, and Overhaul (MRO) and working to bring down the cost and time involved. The prime focus is on unexpected downtime and extended maintenance leading to delays in the flights, which also reduces the trustworthiness of the airliners among the customers. One of the effective solutions to address this issue is Condition based Maintenance (CBM), in which the aircraft systems are monitored frequently, and maintenance plans are customized to suit the health of these systems. Integrated Vehicle Health Management (IVHM) is a capability enabling CBM by assessing the current condition of the aircraft at component/ Line Replaceable Unit/ system levels and providing diagnosis and remaining useful life calculations required for CBM. However, there is a lack of focus on vehicle level health monitoring in IVHM, which is vital to identify fault propagation between the systems, owing to their part in the complicated troubleshooting process resulting in prolonged maintenance. This research addresses this issue by proposing a Framework for Aerospace Vehicle Reasoning, shortly called FAVER. FAVER is developed to enable isolation and root cause identification of faults propagating between multiple systems at the aircraft level. This is done by involving Digital Twins (DTs) of aircraft systems in order to emulate interactions between these systems and Reasoning to assess health information to isolate cascading faults. FAVER currently uses four aircraft systems: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, to demonstrate its ability to provide high level reasoning, which can be used for troubleshooting in practice. FAVER is also demonstrated for its ability to expand, update, and scale for accommodating new aircraft systems into the framework along with its flexibility. FAVER’s reasoning ability is also evaluated by testing various use cases.Transport System

Contribution to reliable control of dynamic systems

Aplicat embargament des de la data de defensa fins al maig 2020This thesis presents sorne contributions to the field of Health-Aware Control (HAC) of dynamic systems. In the first part of this thesis, a review of the concepts and methodologies related to reliability versus degradation and fault tolerant control versus health-aware control is presented. Firstly, in an attempt to unify concepts, an overview of HAC, degradation, and reliability modeling including some of the most relevant theoretical and applied contributions is given. Moreover, reliability modeling is formalized and exemplified using the structure function, Bayesian networks (BNs) and Dynamic Bayesian networks (DBNs) as modeling tools in reliability analysis. In addition, some Reliability lmportance Measures (RIMs) are presented. In particular, this thesis develops BNs models for overall system reliability analysis through the use of Bayesian inference techniques. Bayesian networks are powerful tools in system reliability assessment due to their flexibility in modeling the reliability structure of complex systems. For the HAC scheme implementation, this thesis presents and discusses the integration of actuators health information by means of RIMs and degradation in Model Predictive Control (MPC) and Linear Quadratic Regulator algorithms. In the proposed strategies, the cost function parameters are tuned using RIMs. The methodology is able to avoid the occurrence of catastrophic and incipient faults by monitoring the overall system reliability. The proposed HAC strategies are applied to a Drinking Water Network (DWN) and a multirotor UAV system. Moreover, a third approach, which uses MPC and restricts the degradation of the system components is applied to a twin rotor system. Finally, this thesis presents and discusses two reliability interpretations. These interpretations, namely instantaneous and expected, differ in the manner how reliability is evaluated and how its evolution along time is considered. This comparison is made within a HAC framework and studies the system reliability under both approaches.Aquesta tesi presenta algunes contribucions al camp del control basat en la salut dels components "Health-Aware Control" (HAC) de sistemes dinàmics. A la primera part d'aquesta tesi, es presenta una revisió dels conceptes i metodologies relacionats amb la fiabilitat versus degradació, el control tolerant a fallades versus el HAC. En primer lloc, i per unificar els conceptes, s'introdueixen els conceptes de degradació i fiabilitat, models de fiabilitat i de HAC incloent algunes de les contribucions teòriques i aplicades més rellevants. La tesi, a més, el modelatge de la fiabilitat es formalitza i exemplifica utilitzant la funció d'estructura del sistema, xarxes bayesianes (BN) i xarxes bayesianes dinamiques (DBN) com a eines de modelat i anàlisi de la fiabilitat com també presenta algunes mesures d'importància de la fiabilitat (RIMs). En particular, aquesta tesi desenvolupa models de BNs per a l'anàlisi de la fiabilitat del sistema a través de l'ús de tècniques d'inferència bayesiana. Les xarxes bayesianes són eines poderoses en l'avaluació de la fiabilitat del sistema gràcies a la seva flexibilitat en el modelat de la fiabilitat de sistemes complexos. Per a la implementació de l?esquema de HAC, aquesta tesi presenta i discuteix la integració de la informació sobre la salut i degradació dels actuadors mitjançant les RIMs en algoritmes de control predictiu basat en models (MPC) i control lineal quadràtic (LQR). En les estratègies proposades, els paràmetres de la funció de cost s'ajusten utilitzant els RIMs. Aquestes tècniques de control fiable permetran millorar la disponibilitat i la seguretat dels sistemes evitant l'aparició de fallades a través de la incorporació d'aquesta informació de la salut dels components en l'algoritme de control. Les estratègies de HAC proposades s'apliquen a una xarxa d'aigua potable (DWN) i a un sistema UAV multirrotor. A més, un tercer enfocament fent servir la degradació dels actuadors com a restricció dins l'algoritme de control MPC s'aplica a un sistema aeri a dos graus de llibertat (TRMS). Finalment, aquesta tesi també presenta i discuteix dues interpretacions de la fiabilitat. Aquestes interpretacions, nomenades instantània i esperada, difereixen en la forma en què s'avalua la fiabilitat i com es considera la seva evolució al llarg del temps. Aquesta comparació es realitza en el marc del control HAC i estudia la fiabilitat del sistema en tots dos enfocaments.Esta tesis presenta algunas contribuciones en el campo del control basado en la salud de los componentes “Health-Aware Control” (HAC) de sistemas dinámicos. En la primera parte de esta tesis, se presenta una revisión de los conceptos y metodologíasrelacionados con la fiabilidad versus degradación, el control tolerante a fallos versus el HAC. En primer lugar, y para unificar los conceptos, se introducen los conceptos de degradación y fiabilidad, modelos de fiabilidad y de HAC incluyendo algunas de las contribuciones teóricas y aplicadas más relevantes. La tesis, demás formaliza y ejemplifica el modelado de fiabilidad utilizando la función de estructura del sistema, redes bayesianas (BN) y redes bayesianas diná-micas (DBN) como herramientas de modelado y análisis de fiabilidad como también presenta algunas medidas de importancia de la fiabilidad (RIMs). En particular, esta tesis desarrolla modelos de BNs para el análisis de la fiabilidad del sistema a través del uso de técnicas de inferencia bayesiana. Las redes bayesianas son herramientas poderosas en la evaluación de la fiabilidad del sistema gracias a su flexibilidad en el modelado de la fiabilidad de sistemas complejos. Para la implementación del esquema de HAC, esta tesis presenta y discute la integración de la información sobre la salud y degradación de los actuadores mediante las RIMs en algoritmos de control predictivo basado en modelos (MPC) y del control cuadrático lineal (LQR). En las estrategias propuestas, los parámetros de la función de coste se ajustan utilizando las RIMs. Estas técnicas de control fiable permitirán mejorar la disponibilidad y la seguridad de los sistemas evitando la aparición de fallos a través de la incorporación de la información de la salud de los componentes en el algoritmo de control. Las estrategias de HAC propuestas se aplican a una red de agua potable (DWN) y a un sistema UAV multirotor. Además, un tercer enfoque que usa la degradación de los actuadores como restricción en el algoritmo de control MPC se aplica a un sistema aéreo con dos grados de libertad (TRMS). Finalmente, esta tesis también presenta y discute dos interpretaciones de la fiabilidad. Estas interpretaciones, llamadas instantánea y esperada, difieren en la forma en que se evalúa la fiabilidad y cómo se considera su evolución a lo largo del tiempo. Esta comparación se realiza en el marco del control HAC y estudia la fiabilidad del sistema en ambos enfoques.Postprint (published version