13 research outputs found
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
A Hierarchical Approach for Dynamic Fault Trees Solution Through Semi-Markov Process
Dynamic fault tree (DFT) is a top-down deductive technique extended to model systems with complex failure behaviors and interactions. In two last decades, different methods have been applied to improve its capabilities, such as computational complexity reduction, modularization, intricate failure distribution, and reconfiguration. This paper uses semi-Markov process (SMP) theorem for DFT solution with the motivation of obviating the model state-explosion, considering nonexponential failure distribution through a hierarchical solution. In addition, in the proposed method, a universal SMP for static and dynamic gates is introduced, which can generalize dynamic behaviors like functional dependencies, sequences, priorities, and spares in a single model. The efficiency of the method regarding precision and competitiveness with commercial tools, repeated events consideration, computational complexity reduction, nonexponential failure distribution consideration, and repairable events in DFT is studied by a number of examples, and the results are then compared to those of the selected existing methods
Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring
The offshore wind energy is increasingly becoming an attractive source of
energy due to having lower environmental impact. Effective operation and
maintenance that ensures the maximum availability of the energy generation
process using offshore facilities and minimal production cost are two key
factors to improve the competitiveness of this energy source over other
traditional sources of energy. Condition monitoring systems are widely used for
health management of offshore wind farms to have improved operation and
maintenance. Reliability of the wind farms are increasingly being evaluated to
aid in the maintenance process and thereby to improve the availability of the
farms. However, much of the reliability analysis is performed offline based on
statistical data. In this article, we propose a drone-assisted monitoring based
method for online reliability evaluation of wind turbines. A blade system of a
wind turbine is used as an illustrative example to demonstrate the proposed
approach.Comment: A modified version of this work has been published in the 2022
International Conference on Computing, Electronics & Communications
Engineering (iCCECE). This work is a draft author versio
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability,
popularity, and sophistication. However, one of the major barriers to
widespread acceptance of machine learning (ML) is trustworthiness: most ML
models operate as black boxes, their inner workings opaque and mysterious, and
it can be difficult to trust their conclusions without understanding how those
conclusions are reached. Explainability is therefore a key aspect of improving
trustworthiness: the ability to better understand, interpret, and anticipate
the behaviour of ML models. To this end, we propose SMILE, a new method that
builds on previous approaches by making use of statistical distance measures to
improve explainability while remaining applicable to a wide range of input data
domains
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Machine Learning~(ML) has provided promising results in recent years across
different applications and domains. However, in many cases, qualities such as
reliability or even safety need to be ensured. To this end, one important
aspect is to determine whether or not ML components are deployed in situations
that are appropriate for their application scope. For components whose
environments are open and variable, for instance those found in autonomous
vehicles, it is therefore important to monitor their operational situation to
determine its distance from the ML components' trained scope. If that distance
is deemed too great, the application may choose to consider the ML component
outcome unreliable and switch to alternatives, e.g. using human operator input
instead. SafeML is a model-agnostic approach for performing such monitoring,
using distance measures based on statistical testing of the training and
operational datasets. Limitations in setting SafeML up properly include the
lack of a systematic approach for determining, for a given application, how
many operational samples are needed to yield reliable distance information as
well as to determine an appropriate distance threshold. In this work, we
address these limitations by providing a practical approach and demonstrate its
use in a well known traffic sign recognition problem, and on an example using
the CARLA open-source automotive simulator
SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities
The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a
variety of applications. However, safety assurance is a key barrier to
widespread usage, especially given the unpredictable operational and
environmental factors experienced by UAVs, which are hard to capture solely at
design-time. This paper proposes a new reliability modeling approach called
SafeDrones to help address this issue by enabling runtime reliability and risk
assessment of UAVs. It is a prototype instantiation of the Executable Digital
Dependable Identity (EDDI) concept, which aims to create a model-based solution
for real-time, data-driven dependability assurance for multi-robot systems. By
providing real-time reliability estimates, SafeDrones allows UAVs to update
their missions accordingly in an adaptive manner
A Markov Process-Based Approach for Reliability Evaluation of the Propulsion System in Multi-rotor Drones
Part 3: Decision SystemsInternational audienceAutonomous multirotor drones as a popular type of Unmanned Aerial Vehicles (UAVs) have a tremendous potential to facilitate activities such as logistics, emergency response, recording video, capturing special events, and traffic management. Despite the potential benefits the possibility of harming people during operation should be considered. This paper focuses on modeling the multirotor drones’ propulsion system with Markov chains. Using the proposed model, both reliability and Mean Time To Failure (MTTF) of the propulsion system are evaluated. This study proposes a fault detection and recovery system based on a Markov Model for mission control of multirotor drones. Concretely, the proposed system aims to reduce potential injuries by increasing safety