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Sustainment Systems Division Model-based Diagnostics, Prognostics & Health Management.

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Presentation on theme: "Sustainment Systems Division Model-based Diagnostics, Prognostics & Health Management."— Presentation transcript:

1 Sustainment Systems Division Model-based Diagnostics, Prognostics & Health Management

2 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data VSE: Who we are and what we do… Public Engineering Services Company – Established in 1959 – Over $860M in annual revenue – Americas #1 Defense Contractor (small) International Presence; HQ in Alexandria, Virginia Unique Combination of Experience and Entrepreneurial Spirit – ~2,700 employees – 40% Veterans A Culture of Creative, Cost Effective Problem Solving A History of Exceptional Performance Legacy System Sustainment Reset / Remanufacture / Modernization Full Spectrum Integrated Logistics Support Prepositioned Stock Management Field Support Integrated Logistics Support Services Warehousing / Inventory Control Configuration Data Management Obsolescence Management Supply Chain / Logistics Analysis Sustainment Systems Health Management Systems System Diagnostics & Prognostics Condition & Reliability Based Maintenance Ship Transfer / Repair / Modernization 67 Ships to-date, Navy and Coast Guard From complex Combat Systems upgrades –to– basic hull repair Foreign Military Sales (FMS) support 13 years experience, 42 Countries Full spectrum training Modernization / Tech Insertion Protection / Armor / Survivability Alternative Energy Technologies Big Business Capability…Small Business Agility

3 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Core Technology Expert System using Model-Based Reasoning – Uses design-based model for diagnostics/prognostics – Deterministic model using first principles of design – Reasons by dynamically interpreting the inference of data Reads test data from variety of sources Interprets test data to assess system health, predict, detect and isolate faults Results in health monitoring and/or diagnostics fault isolation Can be embedded (on-line, real-time) or off-line Can be used on new or legacy systems Run-time reasoning engine is structured as a library of functions that are called by a client program Use functions to create unique solutions

4 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Technology Applications Test Program Sets Health Monitoring Systems Automated Maintenance & IETM Embedded Prognostics Model Reasoner Diagnostic Reasoning Services Reasoner Diagnostic Reasoning Services

5 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data 5 Binary File Can be readily hosted on any processor Library of Functions Written in C Can be re-compiled to any processor environment. GUI Client Programs Existing client programs New client programs written by customer Client programs written by VSE Well-Documented API Model Reasoner Diagnostic Reasoning Services Reasoner Diagnostic Reasoning Services Architecture Client Applications Health MonitoringDebriefTech ManualsTest Programs

6 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Applications HP Indigo Digital Press Monitoring & Diagnostics (Embedded) Joint Land Attack Cruise Missile Defense Elevated Netted Sensor System (JLENS) Navy SPS-48E (ITT Gilfillan) US Army IBCS System (Northrop-Grumman/Boeing) Kiowa Warrior Mast-Mounted Sight (TPS) A-10/KC-135 Turbine Engine Monitoring System (TPS) C-130 Gunship Ballistic Computer (TPS) Joint Tactical Information Distribution System (JTIDS) (TPS) Seawolf Submarine Ship Control System (Embedded) Avitronics Radar Warning Receiver (IETM &ATE) FAA Wide Area Augmentation System (Embedded and IETM) Future Combat System Gun Mount Diagnostics and Prognostics (ADAPT) (Changes operating parameters to AVOID failure situations) NASA Remote Power Controller (Diagnostician On A Chip) Dynamic Reconfiguration Manager Navy Total Ship Monitoring (TSM) Program SPY Radar Final Power Assembly SPY-1 Electronic Cooling Water System Lube Oil System & Pump Navy Battle Group Automated Maintenance Environment Program (BG-AME) Electronic Dry Air Low Pressure Air Compressor Fuel Service Pump Firemain System F/A-18 Operator Debrief and IETM Adaptive Training and Skills Assessment for F/A-18 Automated Maintenance Environment Universal Data Acquisition System (UDAS) – candidate replacement for F-16 Crash Survivable Flight Data Recorder APG-63(V1) Radar (Raytheon) Flight Data Capture for Depot Use Mikros Systems ADSSS monitoring LCS Combat Systems

7 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Model-Based Diagnostic Technology Instead of depending on hard-coded troubleshooting logic trees, the Diagnostician uses a knowledge base that is derived from the design of the system! Diagnostician is a set of reasoning algorithms that correlate all possible faults to all possible symptoms, or test results to provide fast, effective fault isolation. Dynamically bases its determinations based on a snapshot of current fault possibilities. Dynamically bases its determinations based on a snapshot of current fault possibilities. Diagnostic Profiler provides an automated development and maintenance process. Diagnostic Profiler provides an automated development and maintenance process. Diagnostician Diagnostic Knowledge Base

8 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Fault/Symptom Matrix Design Import / Capture Part 1 Output 1 Output 2 Part 2 Output 1 Part 3 Output 1 Part 4 Output 1 Part 5 Output 1 Part 6 Output 1 Part 7 Output 1 Part 8 Output 1 FAULTS TESTS T1 T2 T3 T4 P1 P2 XX X XX X X X XXXX XX X T1 T2 T3 T4 P2 Part P1 Fault Propagation

9 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Dynamic Reasoning Techniques Uses all test data to collapse the field of possible faults Cones of Evidence Produced by Pass and Fail Data Minimum Set Covering Algorithms Any data input: discrete, parametric, analysis, s/w or h/w, operational, observable conditions, etc.

10 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data "Dynamic" Diagnostic Capability Test Results can be input … in any order no pre-set sequence … from any source operator observations, test instruments, data bus, data file, built-in test, automatic test equipment, system panels & displays, etc. … as many as test source(s) can provide not restricted to one-at-a-time to traverse fault tree zeroes-in on cause of fault(s) Can identify multiple faults … Diagnostic trees follow single-fault assumption Will always zero in on fault … Never leaves the technician hanging Only requests tests of diagnostic significance … Based upon snapshot of current fault possibilities Performance Monitor System Sensors Built-in Test Start-up BIT Periodic BIT Operator Initiated Test Data SNAPSHOTS System Status Test Request Fault Call-Out Repair Procedure Fault Recovery Data Log Inference Engine Faults Test Results Embedded System Interrogation System Status Fault Description Fault Evidence Maintenance Procedures Troubleshooting Guidance Repair Options Data Log Parts Ordering

11 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data A software tool for developing fault isolation diagnostics for test program sets (TPS) or interactive electronic technical manuals (IETM). What is the Diagnostic Profiler? An engineer uses it to create a diagnostic database (dkb file), which communicates with the test program through a dynamic link library (dll file). 11

12 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data The Inference Engine (Runtime tool) that uses the Diagnostic Knowledge Base (DKB) for diagnostic reasoning What is the Diagnostician? 12

13 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data A developer uses the Diagnostic Profiler to develop and maintain a diagnostic knowledge base The TPS/runtime environment uses the Diagnostician to access the Diagnostic Knowledge Base to: Provide runtime information (Pass/Fail statuses) Identify next best test, callout information, etc. (see Diagnostician Users Manual for complete list of queries). How they work together 13

14 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Test Engineering Determining tests/measurements and requirements for pass/fail status Diagnostic Engineering Determining what fault isolation can be inferred from pass/fail data. UUT Testing 14

15 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Program a fixed logic tree for each test failure. When a test fails, the program runs through the logic tree. It measures one node signal after another, until it finds the one that is wrong. It then calls out the components associated with that node. Traditional Diagnostic Method Repair/Replace: U2 15

16 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Issues Engineer must code EVERY test path Traditional Diagnostic Method Code Tests Code Logic 16

17 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Issues Each test path inherits fault coverage from the previous test. Each tests callouts are dependent on the previous tests that were run A change at any point in the logic tree affects, possibly breaks tree Updating Tree/Recoding = Rework Traditional Diagnostic Method Repair/Replace: ?? 17

18 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Issues Quality of logic tree is only as good as engineer that created it. Developers have to manually track Tree flow Inherited coverage at each point Manageable for smaller circuits, gets complicated for multi-page schematics Traditional Diagnostic Method 18

19 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Diagnostic Model is created for each test Profiler determines functional circuitry by using a net list. The Diagnostician clears circuits when a test passes, and suspects them when it fails. When a test fails, the Diagnostician takes over the test sequence. It runs tests until it cannot clear any more circuits. It then calls out the suspected circuits (the components in them) that have not been cleared. Diagnostic Profiler Test1 Test 2 Test 3 Test 4 Probe 2 Ckt Probe 1 19

20 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Advantages Engineer codes ONLY the Test Modules Diagnostic Profiler Test1 Test 2 Test 3 Test 4 Probe 2 Ckt Probe 1 Code Tests 20

21 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Advantages Diagnostic Reasoning: Diagnostician always finds the best diagnostic path to take Logic Tree is dynamic-Diagnostician finds Available/Useful tests based on test failure Example: Test 1 Fails Test 2/Probe 1 are now Available/Useful Tests Probe 1 runs, and Passes Diagnostic Profiler Test1 Test 2 Test 3 Test 4 Probe 2 Ckt Probe 1 Repair/Replace: Circuit 3 21

22 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Advantages Each test has its own diagnostic model in the database Removing/Changing/Adding a test does NOT break logic tree. Model is re-compiled; Profiler finds best diagnostic path with new list of Available/Useful tests. Example: Test 1 Fails Test 2 is now the only Available/Useful Test Test 2 runs, and Passes Diagnostic Profiler Test1 Test 2 Test 3 Test 4 Probe 2 Ckt Probe 1 Repair/Replace: Circuit 2, 3 22

23 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Advantages Test Engineering/Diagnostic Engineering are 2 separate, concurrently running processes TPS Quality is maximized Test Engineer focuses on Test Requirements/quality of test Diagnostic Engineer focuses on diagnostic significance of tests Diagnostic Profiler 23

24 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Advantages Enhanced Diagnostic Capability Reduced Runtimes Go-Chain runs only tests that are truly required Many Legacy TPS have diagnostics embedded in Go-Chain Diagnostic Database always finds best diagnostic path Benefits 24

25 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Dynamic Reasoning Capability Will be better than traditional diagnostics Algorithms use pass & fail data, minimum set covering, etc., which gives better diagnostic resolution for test data Will cost less to implement No Hard-Coded Diagnostic Logic Will be easier to Update & Maintain Design Changes / Test Changes easily introduced to Knowledge Base 25

26 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Simplified & Efficient Test Code Get R/R Input Initial Symptom Data Get Ambiguity Group Get Best Next Test Run Test & Pass/Fail to Diagnostician Ambiguity Group > 1? No Matter what the fault(s), the Logic is the same! No Matter how big the system, the Logic is the same! Isolates Multiple Fault events Enables the use of Test Program templates Enables the use of Test Program templates Test Program can focus on running the tests Test Program can focus on running the tests Test Program is highly modularized Test Program is highly modularized Can help alleviate machine timing dependencies Can help alleviate machine timing dependencies Diagnostic Model is configuration & hardware independent Diagnostic Model is configuration & hardware independent Model Binary File (.dkb) Diagnostician Diagnostic Reasoning Services Diagnostician Diagnostic Reasoning Services Test Program Symptom Data Faults 26

27 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Prognostics Framework

28 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Prognostic Framework Developer 28 Prognostics Framework is a development system for developing models for use with the PF reasoner PF Run-time: Condition Monitoring and Prognostic Reasoning Reads streams of parametric data values Correlates current values to determine system statuses Computes prognostics algorithms based on calculation specification in model

29 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Diagnostics & Prognostics Reasoning Input Data Real-time Continuous Monitoring Operations Support Maintenance Support Alerts / Notifications Health Assessment Maintenance Tasks Operations Impact Prognostics Framework 29

30 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Prognostics Framework Reasoning 30 Symptom Data Symptoms Faults X T=0 T=1 T=N X T=2 Faults Parts Sub-systems 1.Analyze operational data, sensor, BIT and parametric data as symptoms – Diagnostics 2.Apply algorithms to predict & diagnose the implication of out of tolerance symptoms on each future time point defined in the model - Prognostics 3.Identify the components and sub-systems affected by failures and predicted failures – Health Assessment 4.Identify the functions and missions affected by failures - Mission Readiness 5.Identify the repair actions needed - Anticipatory Maintenance Prediction Time Horizon (4) (5) (1) (2) (3) Maintenance Needs Spare Parts Repair Actions

31 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Prognostics Framework Uses design-based engineering model coupled with Inference Engine to provide a deterministic method of real-time condition assessment (first principles of design) Condition Monitoring (Condition-Based Prognostics) – Condition Based prognostics monitors outliers to failure onset – Includes a variety of algorithms to identify the onset of failure conditions or anomalous operations Life Usage Monitoring (Reliability-Based Prognostics) – Reliability Based Prognostics uses de-rated failure rates and accumulates operating time against the units. Contextual stress factors are used as a multiplier of operating time accumulated against the unit – Maturation process used to verify and adjust de-rated failure rates and stress factor weighting – Can also be applied to track preventive maintenance intervals based on operating hours, stress factors, elapsed time intervals or calendar intervals

32 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Prognostics Capabilities Symptom Data Faults Raw Data Inputs Perform Mathematical Calculations (Algorithms) getTrend() applyLeastSquaresBestFit() verifySensorData() Least Squares Best Fit (LSBF) Trend Extrapolation Detect out of limit values Out of Range function/Percent Out of Range Counts per Interval Detect sensor failure Reduce sensor noise Analyze false alarms Apply filters (e.g., M of N) Auto-Baseline Cross-correlate values to make inferences on symptom Accumulate operating time and stresses Standard Functions

33 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Client-Server Software Architecture Client Program (Graphical User Interface) Design user interface as desired or use existing Existing client programs New client programs written by customer Client programs written by VSE Server (Prognostic Reasoner) Library of functions written in C that can be re- compiled to any processor environment Software functions serve as building blocks Integrate building blocks to build desired functionality Well-documented API Prognostic Model Binary file Can be readily hosted on any processor Prognostic Reasoner Generic API Health Management System User Interface Health Management System User Interface Model

34 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Input Data Sample Prognostics Analysis Diagram Compute Life Usage Increment (LUI) Trend and Extrapolate LUI Trend and Extrapolate Margin Compare Signals Vs Limits Prognostic Alerts Prognostic Reports Life Usage Limit (End of Useful Life – 20 Hours) OR State & Context (Stressor) Life Usage Data Prior Life Usage Updated Life Usage Life Usage > Limit Margin < Limit Margin Limits - + Margin Condition Based Prognostic Data Predicted Exceedance Prognostic Alert Predicted RUL Computed RUL + Reliability Based Prognostics Condition Based PrognosticsPrognostic Alerts Prognostics Framework Reasoner Models and Usage Database Prognostics Framework Reasoner Models and Usage Database Usage Report Centralized Usage Database Health Management System

35 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data Symptom Data Faults Liquid/air heat exchanger Symptom Data Faults Glycol heater Symptom Data Faults Controller Assy Symptom Data Faults Flow Switches Symptom Data Faults Pump Symptom Data Faults Control Valve Symptom Data Faults Fan/Motor Assy Symptom Data Faults Symptom Data Faults PowerGPS Symptom Data Faults Antenna Symptom Data Faults Transmitter Symptom Data Faults Data Distribution Symptom Data Faults Receiver Symptom Data Faults Heat Exchange Unit Symptom Data Faults Symptom Data Faults Symptom Data Faults RadarCommsPower Symptom Data Faults Processors Model Based Reasoning Reasoner is software that correlates BIT data to system hierarchy to determine status System model constructed as hierarchical family of fault/symptom matrices Fault/symptom matrix contains mapping of fault propagation and test coverage Reasoner correlates actual test data with faults – across hierarchy of fault/symptom matrices Operational Data/State Data Status Monitoring BIT/BITE Results Prognostic Indications Operator/Maintainer Inputs Faults Symptom Data System

36 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data 36 Scope of Prognostics Model Prognostics Framework Model SYSTEM DATA MANAGEMENT Input Data Definition & Characterization Prediction Horizons TEST/SENSOR DATA BIT Inputs & Mapping Sensor Data & Mapping Additional Data Inputs & Mapping HEALTH MANAGEMENT Detection Algorithms Diagnostic Coverage Prediction Algorithms Fault Criticality Input Data Processing & Filtering Confidence Factors MISSION SUPPORT Mission Profile Function Correlation to Mission Phases Function Criticality to Mission Immediate Operator Actions DESIGN DATA Definition of Parts, Faults, Failure Modes, Failure Rates, Tests, Interconnectivity and Test Coverage MAINTENANCE SUPPORT Repair Item Definition Combinations of Repair Items Repair Actions (IETM Interface) Parts Ordering Data PMCS Triggering and Tracking

37 Use or disclosure of the data contained on this slide is subject to the restrictions in accordance with FAR (a) and FAR , restriction on disclosure of use and data For More Information Questions? Contact us: Mary NolanRebecca Norman (706) (973)


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