1,846 research outputs found
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
The xSAP Safety Analysis Platform
This paper describes the xSAP safety analysis platform. xSAP provides several
model-based safety analysis features for finite- and infinite-state synchronous
transition systems. In particular, it supports library-based definition of
fault modes, an automatic model extension facility, generation of safety
analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and
Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation
of Fault Trees, failure propagation analysis using Timed Failure Propagation
Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several
industrial projects as verification back-end, and is currently being evaluated
in a joint R&D Project involving FBK and The Boeing Company
A safety analysis approach to clinical workflows : application and evaluation
Clinical workflows are safety critical workflows as they have the potential to cause harm or death to patients. Their safety needs to be considered as early as possible in the development process. Effective safety analysis methods are required to ensure the safety of these high-risk workflows, because errors that may happen through routine workflow could propagate within the workflow to result in harmful failures of the system’s output. This paper shows how to apply an approach for safety analysis of clinic al workflows to analyse the safety of the workflow within a radiology department and evaluates the approach in terms of usability and benefits. The outcomes of using this approach include identification of the root causes of hazardous workflow failures that may put patients’ lives at risk. We show that the approach is applicable to this area of healthcare and is able to present added value through the detailed information on possible failures, of both their causes and effects; therefore, it has the potential to improve the safety of radiology and other clinical workflows
The integration of on-line monitoring and reconfiguration functions using IEEE1149.4 into a safety critical automotive electronic control unit.
This paper presents an innovative application of IEEE 1149.4 and the integrated diagnostic reconfiguration (IDR) as tools for the implementation of an embedded test solution for an automotive electronic control unit, implemented as a fully integrated mixed signal system. The paper describes how the test architecture can be used for fault avoidance with results from a hardware prototype presented. The paper concludes that fault avoidance can be integrated into mixed signal electronic systems to handle key failure modes
Causality and Temporal Dependencies in the Design of Fault Management Systems
Reasoning about causes and effects naturally arises in the engineering of
safety-critical systems. A classical example is Fault Tree Analysis, a
deductive technique used for system safety assessment, whereby an undesired
state is reduced to the set of its immediate causes. The design of fault
management systems also requires reasoning on causality relationships. In
particular, a fail-operational system needs to ensure timely detection and
identification of faults, i.e. recognize the occurrence of run-time faults
through their observable effects on the system. Even more complex scenarios
arise when multiple faults are involved and may interact in subtle ways.
In this work, we propose a formal approach to fault management for complex
systems. We first introduce the notions of fault tree and minimal cut sets. We
then present a formal framework for the specification and analysis of
diagnosability, and for the design of fault detection and identification (FDI)
components. Finally, we review recent advances in fault propagation analysis,
based on the Timed Failure Propagation Graphs (TFPG) formalism.Comment: In Proceedings CREST 2017, arXiv:1710.0277
TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system
Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system
Failure mode identification and end of life scenarios of offshore wind turbines: a review
In 2007, the EU established challenging goals for all Member States with the aim of obtaining 20% of their energy consumption from renewables, and offshore wind is expected to be among the renewable energy sources contributing highly towards achieving this target. Currently wind turbines are designed for a 25-year service life with the possibility of operational extension. Extending their efficient operation and increasing the overall electricity production will significantly increase the return on investment (ROI) and decrease the levelized cost of electricity (LCOE), considering that Capital Expenditure (CAPEX) will be distributed over a larger production output. The aim of this paper is to perform a detailed failure mode identification throughout the service life of offshore wind turbines and review the three most relevant end of life (EOL) scenarios: life extension, repowering and decommissioning. Life extension is considered the most desirable EOL scenario due to its profitability. It is believed that combining good inspection, operations and maintenance (O&M) strategies with the most up to date structural health monitoring and condition monitoring systems for detecting previously identified failure modes, will make life extension feasible. Nevertheless, for the cases where it is not feasible, other options such as repowering or decommissioning must be explored
Timed Fault Tree Models of the China Yongwen Railway Accident
Safety is an essential requirement for railway transportation. There are many methods that have been developed to predict, prevent and mitigate accidents in this context. All of these methods have their own purpose and limitations. This paper presents a new useful analysis technique: timed fault tree analysis. This method extends traditional fault tree analysis with temporal events and fault characteristics. Timed Fault Trees (TFTs) can determine which faults need to be eliminated urgently, and it can also provide a safe time window to repair them. They can also be used to determine the time taken for railway maintenance requirements, and thereby improve maintenance efficiency, and reduce risks. In this paper, we present the features and functionality of a railway transportation system based on timed fault tree models. We demonstrate the applicability of our framework via a case study of the China Yongwen line railway accident
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