Modelling the Risk In Defence Engineering

Transcription

1 Modelling the Risk In Defence Engineering Model-Based Systems Engineering Symposium 27 October 2014 Canberra, Australia Presented by Chris Stecki, PHM Technology

2 Executive Summary Defence is transitioning from a document-centric approach to an architectural model to generate and capture the Preliminary Operational Concept Document (POCD) and Preliminary Functional and Performance Specification (PFPS). The MADe solution is an MBSE toolset that generates a functional engineering model of the system that simulates system behaviour, and has integrated technical analysis to optimise decisions on capability acquisition and support. This presentation demonstrates how the MADe software can be used by system engineers to address engineering risk and analysis requirements / deliverables from concept definition to in-service support. The potential benefits of this approach are improved availability of the system with a lower cost of ownership across the Capability Lifecycle.

3 Presentation Outline System Engineering in Defence Sustainment in Defence Engineering Analysis benefits Solution Context (Capability Lifecycle) Solution Requirements Engineering Risk - data How MADe works Capability Analysis Requirements Engineering Analysis Requirements MADe Technical Features Taxonomy: Functions & Flows MADe Fuzzy Cognitive Mapping (FCM) Modelling Model Simulation: Failure Propagation Taxonomy: Failure Concepts & Failure Diagrams Configuration Management (Data Quality Analysis) Summary

4 Systems Engineering in Defence The Systems Engineering function in Defence needs to consider and integrate the appropriate data and information from multiple engineering domains to identify the optimal Capability System and Support Solution. User Requirements & Needs Operational Concepts V&V Procedures Specifications Software & Hardware MBSE Model Engineering Analysis Logistics & Support Operational Concepts Compliance & Documentation

5 Defence sustainment Defence capability typically has platforms and their support systems with these characteristics: complex, integrated engineering systems diverse and evolving usage profiles long life (up to 40 years) demanding and varied operating environments regularly modified design (technology upgrade, life of type extension) sustainment represents between 65-85% of total ownership costs total ownership cost is >$20M Sustainment is an important consideration for Defence capability during the acquisition phase. Source: Defence Acquisition University

6 Implications of sustainment risk Significant divergence in military sustainment programs can impact: military capability Operational Tempo Mission Profiles reduced fleet size delayed modernisation / upgrades delayed replacement programs LOTE / asset withdrawal Source: naval-technology.com defence budgets Total Cost of Ownership (TCO) contract models for sustainment (e.g. PBC) future Capability acquisition (budget allocation) accountability (decision audits) Source: airforce-technology.com

7 Engineering Analysis benefits By improving the capability and quality of its engineering analysis solution, Defence can: more accurately define the system and support requirements of a Capability more accurately define the technical risk associated with each design and support option (maintenance strategy) improve the quality of engagement with industry during design Capability definition more accurately define the sustainment costs associated with each design / support option improve its confidence level in the technical analysis to support Capability decisions optimise the Total Cost of Ownership for a Capability across its lifecycle

8 Solution Context (Capability Lifecycle)

9 Solution Requirements A significant number of engineering risks can be effectively mitigated if inter-dependent engineering analyses are performed using a common data source removing system integration risk. Productivity and knowledge management are significantly enhanced. One model for engineering analysis across the capability lifecycle

10 Engineering Risk data Significant risks to the engineering analysis are based on data attributes, including: Accuracy (have all relevant functional dependencies of a defect been identified or understood?) Consistency (e.g. standardised mission parameters, failure concepts, criticality assessments, etc.) Currency (when was the data obtained?) Integrity (has the data been validated or verified? Source (was it generated [design data], sourced [OEM, supply chain, consultant] or captured [operational data] etc.) Usability (can data be sourced from existing applications?, customisation required for systems integration, etc.). Quality Assessment (metric to assess the quality of the data used in the various required analyses across the product lifecycle and supply chain if you can t measure it you cant manage it

11 How MADe works A platform / system is represented in a simulation model (Functional Block Diagram) based on 354 defined functions and synonyms that: define how it functions (behaves); show how, when and why it can fail; and captures the integration and dependencies between electronic, hydraulic, mechanical and pneumatic systems. Engineering analyses are conducted on the model to determine the appropriate mitigation for the impacts of potentially critical failures (risks). The mitigation may be: a design change; a change in usage characteristics, a change in maintenance approach (e.g. diagnostics); etc.

12 Capability Analysis Requirements Maintenance Cost Estimates (MCE) - [System and Support System cost] Mission Profiles Mission Effective Function List (MEFL)

13 Engineering Analysis Requirements Engineering ILS DIDs: Functional Block Diagram FMEA / FMECA RBD Fault Tree Analysis RCM LSAR

14 MADe Technical Features A Model Based solution, MADe has: concurrent analysis (not retrospective) integrated analysis capabilities compounding analysis potential for closing the loop with operational data configuration management of analysis The software has capabilities (unique features) to: 1. accelerate modeling /analysis for new and legacy systems (libraries) 2. improve the quality of modeling and analysis (automated dependency mapping, functional taxonomy, etc.) 3. document engineering decisions for the purpose of capability definition, risk management and quality assurance 4. provide rapid / comprehensive decision support for ILS (MCE/MRD/MTA/BRCM) 5. provide analysis & decision support capability for continuous maintenance optimisation 6. capture system knowledge in the model graphically (develop / retain organisation IP)

15 Taxonomy: Functions & Flows Functions are terms used to define the functional purpose of an item in a system. Flows define the energy, material or signals that are transferred between items. The 54 Function and 23 Flow types are used to standardise terminology used to functionally describe the system and its components.

16 Taxonomy: Functions & Flows

17 MADe Fuzzy Cognitive Mapping (FCM) The FCM modelling approach is a computing method used for the simulation & analysis of the complex, integrated systems typical in Defence. It is based on the concept of unidirectional material, signal and energy flow properties across a system. [Note: MADe also has Bond Graph methods to support multi-directional flows in hydraulic systems] Flow properties are associated with fuzzy values (linguistic terms low, moderate, high) to describe the causal relationship between properties.

18 MADe Fuzzy Cognitive Mapping (FCM) FCM is used in a Functional Model to represent a system and its elements in terms of their functions using a standardized taxonomy. FCM enables the automated causal propagation that maps functional dependencies.

19 Simulation: Failure Propagation

20 Taxonomy: Failure Diagrams PHMT has defined 470 failure concepts (causes, mechanisms, faults) in a standardised taxonomy used in Maintenance Aware Design environment software (MADe) Failure concepts are used to understand the potential causes of system failure in order to identify the optimal maintenance approach (e.g. maintenance action periodicity, or application of sensors for condition based maintenance) Using consistent failure concept taxonomy will ensure that not only are failures identified in the design process, later they can be reported accurately during operational usage. Relevant engineering concepts are associated with the failure which will support further analysis (e.g. root cause) and enable a FRACAS to be implemented Standardisation across the fleet will also enable comparison of operational data for common systems (between platforms) for benchmarking Failure Causes Failure Mechanisms Failure Faults

21 MADe Failure Diagrams The Failure Diagram illustrates how failure concepts are associated and can be used to provide guided defect analysis that is specific to the required level of repair. MADe Failure Diagram for a generic gearbox MADe Failure Diagram for a generic piston pump

22 Data Quality Analysis The source of the information, time/date of data entry and annotation of a particular entry are recorded Confidence levels are assigned to each type data source which are aggregated to provide an overall level of confidence. The dashboard is automatically updated as data is generated / annotated in the model to provide a confidence level in the model data.

23 Summary How Defence buy, use and support military equipment is significantly impacted by the design and operational performance of highly complex engineering systems. Defence requires dynamic and adaptable engineering analysis to make appropriate decisions based on technical considerations throughout the capability lifecycle - systemic concerns can arise if engineering risk is not consistently and efficiently managed. The quality of the engineering analysis, data quality and configuration management of these analysis processes are structural risks that should be minimised and mitigated by Defence. MADe provides a model based engineering solution framework provides a methodology for Defence organisations to address these risks and provides potential cost, schedule and technical benefits to Defence decision making.

24 Questions?