System fault diagnostics using fault tree analysis

Over the last 50 years advances in technology have led to an increase in the complexity and sophistication of systems. More complex systems can be harder to maintain and the root cause of a fault more difficult to isolate. Down-time resulting from a system failure can be dangerous or expensive depending on the type of system. In aircraft systems the ability to quickly diagnose the causes of a fault can have a significant impact on the time taken to rectify the problem and return the aircraft to service. In chemical process plants the need to diagnose causes of a safety critical failure in a system can be vital and a diagnosis may be required within minutes. Speed of fault isolation can save time, reduce costs and increase company productivity and therefore profits. System fault diagnosis is the process of identifying the cause of a malfunction by observing its effect at various test points. Fault tree analysis (FTA) is a method that describes all possible causes of a specified system state in terms of the state of the components within the system. A system model is used to identify the states the system should be in at any point in time. This paper presents a method for diagnosing faults in systems using FTA to explain the deviations from normal operation observed in sensor outputs. The causes of a system's failure modes will be described in terms of the component states. This will be achieved with the use of coherent and non-coherent fault trees. A coherent fault tree is constructed from AND and OR logic, therefore only considers component failed states. The non-coherent method expands this allowing the use of NOT logic which implies that the existence of component failed states and working states are both taken into account. This paper illustrates the concepts of this method by applying the technique to a simplified water tank level control system

Model based fault diagnosis for hybrid systems : application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

System fault diagnostics using fault tree analysis

Over the last 50 years, advances in technology have led to an increase in the complexity and sophistication of systems. More complex systems can be harder to maintain and the root cause of a fault more difficult to isolate. Downtime resultin from a system failure can be dangerous or expensive, depending on the type of system. In aircraft systems the ability to diagnose quickly the causes of a fault can have a significant impact on the time taken to rectify the problem and to return the aircraft to service. In chemical prcess plants the need to diagnose causes of a safety-critical failure in a system can be vital and a diagnosis may be required within minutes. Speed of fault isolation can save time, reduce costs, and increase company productivity and therefore profits. System fault diagnosis is the process of identifying the cause of a malfunction by observing its effect at various test points. Fault tree analysis (FTA) is a method that describes all possible causes of a specified system state in terms of the state of the components within the system. A system model is used to identify the states that the system should be in at any point in time. This paper presents a method for diagnosing faults in systems using FTA to explain the deviations from normal operation observed in sensor outputs. The causes of a system’s failure modes will be described in terms of the component states. This will be achieved with the use of coherent and non-coherent fault trees. A coherent fault tree is constructed from AND and OR logic and therefore considers only component-failed states. The non-coherent method expands this, allowing the use of NOT logic, which implies that the existence of component-failed states and component-working states are both taken into account. This paper illustrates the concepts of this method by applying the technique to a simplified water tank level control system

Integrated application of compositional and behavioural safety analysis

To address challenges arising in the safety assessment of critical engineering systems, research has recently focused on automating the synthesis of predictive models of system failure from design representations. In one approach, known as compositional safety analysis, system failure models such as fault trees and Failure Modes and Effects Analyses (FMEAs) are constructed from component failure models using a process of composition. Another approach has looked into automating system safety analysis via application of formal verification techniques such as model checking on behavioural models of the system represented as state automata. So far, compositional safety analysis and formal verification have been developed separately and seen as two competing paradigms to the problem of model-based safety analysis. This thesis shows that it is possible to move forward the terms of this debate and use the two paradigms synergistically in the context of an advanced safety assessment process. The thesis develops a systematic approach in which compositional safety analysis provides the basis for the systematic construction and refinement of state-automata that record the transition of a system from normal to degraded and failed states. These state automata can be further enhanced and then be model-checked to verify the satisfaction of safety properties. Note that the development of such models in current practice is ad hoc and relies only on expert knowledge, but it being rationalised and systematised in the proposed approach – a key contribution of this thesis. Overall the approach combines the advantages of compositional safety analysis such as simplicity, efficiency and scalability, with the benefits of formal verification such as the ability for automated verification of safety requirements on dynamic models of the system, and leads to an improved model-based safety analysis process. In the context of this process, a novel generic mechanism is also proposed for modelling the detectability of errors which typically arise as a result of component faults and then propagate through the architecture. This mechanism is used to derive analyses that can aid decisions on appropriate detection and recovery mechanisms in the system model. The thesis starts with an investigation of the potential for useful integration of compositional and formal safety analysis techniques. The approach is then developed in detail and guidelines for analysis and refinement of system models are given. Finally, the process is evaluated in three cases studies that were iteratively performed on increasingly refined and improved models of aircraft and automotive braking and cruise control systems. In the light of the results of these studies, the thesis concludes that integration of compositional and formal safety analysis techniques is feasible and potentially useful in the design of safety critical systems

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Availability modeling and evaluation on high performance cluster computing systems

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power, cost effective, and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system\u27s availability requirement. In this study, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. Three key components in the investigation are availability modeling, model evaluation, and data analysis. The general availability concepts and modeling techniques are briefly reviewed. The system\u27s availability model is divided into submodels based upon their functionalities. Furthermore, an object oriented Markov model specification to facilitate availability modeling and runtime configuration has been developed. Numerical solutions for Markov models are examined, especially on the uniformization method. Alternative implementations of the method are discussed; particularly on analyzing the cost of an alternative solution for small state space model, and different ways for solving large sparse Markov models. The dissertation also presents a monitoring and data analysis framework, which is responsible for failure analysis and availability reconfiguration. In addition, the event logs provided from the Lawrence Livermore National Laboratory have been studied and applied to validate the proposed techniques

A review of model based and data driven methods targeting hardware systems diagnostics

System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls

A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants

Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of many studies. Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study. HAZOP analysis helps identify the fault modes and determine process variables monitored. The KPCA model is then constructed to reduce monitoring variable dimensionality. Meanwhile, the fault features of the monitoring variables are extracted, so then process monitoring can be performed with the squared prediction error (SPE) statistics of KPCA. Then, multiple WNN models are designed through the use of low-dimensional sample data preprocessed by KPCA as the training and test samples to detect the fault mode online. Finally, FTA approach is introduced to further locate the fault root causes of the fault mode. The proposed approach is applied to process monitoring and fault diagnosis in a depropanizer unit. Case study results indicate that this approach can be applicable to process monitoring and diagnosis in large-scale chemical plants. Accordingly, the approach can serve as an early and reliable basis for technicians’ and operators’ safety management decision-making

Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures

The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS

System fault diagnosis using fault tree analysis

Fault tree analysis is a method that describes all possible causes of a specified system state in terms of the state of the components within the system. Fault trees are commonly developed to analyse the adequacy of systems, from a reliability or safety point of view during the stages of design. The aim of the research presented in this thesis was to develop a method for diagnosing faults in systems using a model-based fault tree analysis approach, taking into consideration the potential for use on aircraft systems. Initial investigations have been conducted by developing four schemes that use coherent and non-coherent fault trees, the concepts of which are illustrated by applying the techniques to a simple system. These were used to consider aspects of system performance for each scheme at specified points in time. The results obtained were analysed and a critical appraisal of the findings carried out to determine the individual effectiveness of each scheme. A number of issues were highlighted from the first part of research, including the need to consider dynamics of the system to improve the method. The most effective scheme from the initial investigations was extended to take into account system dynamics through the development of a pattern recognition technique. Transient effects, including time history of flows and rate of change of fluid level were considered. The established method was then applied to a theoretical version of the BAE Systems fuel rig to investigate how the method could be utilised on a larger system. The fault detection was adapted to work with an increased number of fuel tanks and other components adding to the system complexity. The implications of expanding the method to larger systems such as a full aircraft fuel system were identified for the Nimrod MRA4