Download
1 / 10
100 likes | 124 Views
This presentation explores the challenges in certifying Bayesian networks for fault diagnosis in aerospace systems according to DO-178B/C. It discusses the capabilities and benefits of Bayesian networks, and the assurance approach in DO-178B/C. The identified challenges include model parameterization, structure, evaluation, implementation, and assurance. The presentation also highlights the need for high levels of confidence in data and abstract models.
E N D
Certification Challenges in the V&V of Bayesian Networks for Safety Critical Fault Diagnosis in Aerospace Systems Speaker: Mark Douthwaite Supervisor: Tim Kelly
Overview • Introduction to Bayesian Networks – Capabilities, Use Cases and Benefits • Assurance According to DO-178B/C – How Assurance is Approached in DO-178 • Challenges in Bayesian Network Assurance – Overview of Identified Challenges • Bayesian Networks and DO-178B/C – How the Assurance Challenges map to DO-178
Introduction to Bayesian Networks Figure 1: Visual representation of medical Bayesian Network used to aid diagnoses of patients in an ICU.
Introduction to Bayesian Networks • Use Cases: • Diagnostic/Prognostics (Medicine, Aerospace, Environment) • Navigation/Time Series Analysis (Aerospace, Robotics) • Text Processing/Filtering (Email, Consumer Products) • Benefits: • Robust to error & missing data • Intelligible to human experts • Designed to cope with uncertainty But – no formal safety critical guidance.
Assurance According to DO-178B/C System Requirements High-Level Requirements Low-Level Requirements Software Architecture Source Code Executable Object Code Figure 2: Visual interpretation of implicit safety lifecycle in DO-178
Assurance According to DO-178B/C • Non-prescriptive: • Safety lifecycle is implicit • Designed to be flexible • Testing & Traceability: • Strong focus on testing and V&V activities • Stresses importance of traceability to requirements
Challenges in BN Assurance • High level failure modes – Variations of Type I and Type II errors. • Model Parameterisation – Defining the probability distribution of network. • Model Structure – Defining the independence relations within the network. • Model Evaluation – Safety-focused evaluation of model performance. • Implementation – Implementing a software-based network system. • Assurance of abstract Bayesian Network model is key.
Bayesian Networks and DO-178B/C System Requirements High-Level Requirements Data Artefacts Low-Level Requirements Software Architecture Model Structure Source Code Executable Object Code Figure 3: Visual interpretation of safety lifecycle in DO-178 with envisioned mapping of BN-specific consideration.
Bayesian Networks and DO-178B/C • Existing Provisions • Data parameterisation items guidance in DO-178B/C (2.5.1) • 2.5.1: ‘A data set that influences the behaviour of the software without modifying the Executable Object Code… Examples include configuration tables and databases.’ • Data Assurance • Need to provide high levels of confidence in underlying data artefacts • Model Assurance • Need to provide high levels of confidence in abstract model
