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Activity title

Physics of Failure for Military Platforms

Activity Reference

AVT-ET-184

Panel

Applied Vehicle Technology

Security Classification

NATO UNCLASSIFIED

Status

Active

Activity type

ET

Start date

2018

End date

2018

Keywords

FMECA, Lifing, PoF, Predictive Analysis

Background

Data and Models need to be treated as corporate assets, i.e. assets have attributes of quality, value and benefits, Data Analytics as a collective term for “Big Data”, “Data Mining”, “AI”, “Deep Learning” can provide confidence and reduced uncertainty value, plus report data that assists in decision making at strategic, operational and tactical levels of leadership, including S&T. As we break the problem into executable chunks, there is a need to collect “eco-system” domains of data (sources of truth) to feed into the integrated models. Component failure data is one of those eco-systems domains, where we need the user/scientific stories that con be converted into computationally useful data. Component failure involves multiple-physics at multiple-length scales including time. Much of the classical failure analysis has focused on predicting failure in a linear fashion rather than considering the holistic changes in the component functioning as a system over time: 1) Atomic scale (fatigue crack nucleation, dislocation-based model); 2) Nano and meso-scale (micro-crack growth); 3) Microstructure-scale (crack growth and propagation); 4) Macrostructure-scale (large crack linking and collapse) and 5) Final failure of components The eco-system domain of “Physics of Failure” includes physics-based failure mechanisms, failure progression and associated accuracies and risks for single and combined failure modes. New classes of analysis tools are being worked on at atomic, component and system levels ? Developed from multi-disciplinary knowledge, and data from diverse new sensors ? Applied/adapted across different harsh environments and ? Use for diagnostics, prognostics and NATO fleet decision support However, success of diagnostic and prognostic applications affecting availability and cost effectiveness has varied due to the: ? Difficult in validating with highly reliable but critical elements in variable environments ? Multi-site, multi-failure types are not well understood ? Failure mechanisms of newly developed materials are not well understood. ? The complexity of interaction between materials, structures, and varying rates of atomic changes has challenged analytical and human capacities and ? Integration into human decision support processes at component and platform level is difficult Past AVT experience has examined capabilities and needs at particular scales: AVT-211 Understanding Failure Mechanisms of Composites for Sustaining and Enhancing Military Systems Structures, AVT-172 Condition Based Maintenance, AVT-212 Application of Integrated Munition Health Management, AVT-220 Structural Health Monitoring of Military Vehicles, AVT-222 Continuing Airworthiness of Ageing Aircraft Systems, AVT-223 Cross-Domain Integrated System Health Management Capability and AVT-242 Coated Component Condition Assessment and Remaining Life Prediction for Advanced Military Air Vehicles. In many ways this AVT technical activity proposal is the natural extension of this foundational work.

Objectives

NATO’s integrating function gives it a unique opportunity to influence and shape the exploitation of an emerging technology. Collaboration on assessments of Physics of Failure (PoF) current capabilities and needs, gaps and development priorities within an asset and information management strategy should include consideration of: • Coordination of through-life PoF capabilities and assets to aid interoperability and theatre level analysis • Significant improvement in Safety and cost of ownership related to prolonging weapons system useful life expectancy. • Enabling theatre level PoF architecture through policy, resourcing and facilitation • Standardization and the achievement of interoperability, including the ownership, leasing or generation of technical failure data files and electronic record management • Failure and risk data and information infrastructure ownership and applicable decision making processes within the context of NATO defence planning • Workforce development (training, education and certification) for configuration control, use and upgrade of failure and risk model/data resources within the information management hierarchy • Consideration of vulnerabilities exposed through adoption of the technology by adversaries • Development of a cyber threat assessment relating to technical data integrity and the security of PoF software and firmware • Provision and management of Model/Data Repositories especially access to proprietary and multi-user datasets. • Means to provide advice and subject matter expertise on PoF and associated risk assessment, its employment and legal and warranty issues. Specific outputs would include: 1. Technology Assessment Report: Integrated needs, experiences and issues from AVT, MSG, SET, IST and other panels as appropriate 2. Application of PoF for Platform Asset Management Report: Capabilities, Issues and Priorities for development with Program Office and Warfighter customers for feasible/desirable application areas including fleet operations, maintenance and refit/upgrade, vehicle manufacture 3. Technology maturation and development hierarchy and process framework including Integration Process for Member Nation facilities, Component Improvement Programs

Topics

• Identification of Component failure data needs and capabilities within AVT and other STO groups • Assess effectiveness of prior Data Analytics as a collective term for “Big Data”, “Data Mining”, “AI”, “Deep Learning” can provide confidence and reduced uncertainty value, plus report data that assists in decision making at strategic, operational and tactical levels of leadership, including S&T. • Collect Component failure data “eco-system” domains of data (sources of truth) to feed into the integrated models. • Translate high asset data into user/scientific stories that con be converted into computationally useful data. Methods failure can be discovered/described/reported/collected and the processes that produce the physics of failure and follow on actions. • Showcase novel approaches for predicting failure that considers the holistic changes in the component functioning as a system over time • Focus will be on High value assets, high failure rates, UAVs, and information sources collected in a disciplined data centric approach. 1. Process (described as an act of…) occupies a temporal region ? Current holistic and computational/hierarchical/hybrid multi-disciplinary methods applicable to the component level ? Flight Debrief ? Inspection ? Planning ? Investigation ? Grounding ? Development ? Validation, verification and Accreditation/Acceptance process ? Assessments (eg. Risk and confidence assessments) ? Physics of Failure ? Probability of failure ? Design Process ? Production Process ? Production Plan Generation Process ? Sortie ? Taxi ? Landing ? Logistics Process ? Flight Debrief Inspection ? Grounding ? Assessment ? Mx (machine, tool, IT, infrastructure) • Inspection • Buy • Repair • Replace • Retire 2. Materiel Entity/ Continuant (retains its identity even though its states and relations may change) occupies a spatial region with its Attributes (information that provides properties such as quality or feature, regarded as a characteristic or inherent part; something as being caused by) ? Fleet ? Vehicle Tail ? Aircraft ? Sub-system • Engine ? Status ? Function ? Models ? Risk and confidence assessment tools ? Platform -focused Gaps, Opportunities, Priorities ? Pre-Condition ? Post-Condition ? Fault • Crack 3. Information entity ? Platform and Enterprise model, data ? Joint Doctrine Publication ? Technical Manual ? Requirement Specification ? Aircraft Design ? Production Plan ? Part List ? Sensor Data ? Maintenance Plan ? Maintenance Report ? Maintenance History plans ? Flight Debrief Report ? Inspection Report ? Maintenance Report ? Work Order ? Technical Order ? technology demonstration report ? Serial Number ? Tail Number ? Part Number ? Stock Number

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