The presentation summarizes Glenn Research center work in IVHM related areas. The focus is on the model-based engine control and diagnostics work being done at the Controls and Dynamics Branch

Garg, Sanjay

NASA Technical Reports Server

at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
IVHM Related Research at NASA Glenn 
Dr. Sanjay Garg 
Branch Chief 
Ph: (216) 433-2685 
FAX: (216) 433-8990 
email: sanjay.garg@nasa.gov 
http://www.grc.nasa.gov/WWW/cdtb 
 
https://ntrs.nasa.gov/search.jsp?R=20140011016 2019-08-31T19:49:39+00:00Z
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
Advanced Health 
Management technologies 
for self diagnostic and 
prognostic propulsion 
system 
- Life usage monitoring and 
prediction 
- Data fusion from multiple 
sensors and model based 
information 
Active Control Technologies 
for enhanced performance  
and reliability, and reduced 
emissions 
- active control of 
combustor,  compressor, 
vibration etc. 
- MEMS based control 
applications 
Intelligent Propulsion Systems 
Control System perspective 
Distributed, Fault-Tolerant Engine Control for  
enhanced reliability, reduced weight and optimal 
performance with system deterioration 
- Smart sensors and actuators 
- Robust, adaptive control 
Multifold increase in propulsion system Affordability, Capability 
Environmental Compatibility, Performance, Reliability and Safety 
at Lewis Field 
Glenn Research Center 
Aeronautics Research Mission Directorate 
Aeronautical  
Sciences 
(GRC) 
Fundamental Aeronautics  
Program 
Aviation Safety  
Program 
Airspace Systems  
Program 
Fixed Wing 
(GRC) 
High Speed 
(LaRC) 
Rotary Wing 
(ARC) 
Vehicle System 
Safety Tech 
(VSST - LaRC) 
System-wide 
Safety & 
Assurance Tech 
(SSAT – ARC) 
Atmospheric  
Env. Safety Tech 
(AEST – GRC) 
NASA Aeronautics Program Structure 
Effective FY13 
Aeronautics Test 
 Program 
Integrated Systems 
Research  
Controls and Dynamics Branch 
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
Fundamental Aeronautics Program (FAP) 
• Distributed Engine Control – AS 
• Model-Based Engine Control – AS 
• Pressure Gain Combustion – AS 
• Dynamic Systems Analysis – FW 
• Active Combustion Control – AS 
• Aero-Propulso-Servo-Elasticity – HS 
Aviation Safety Program 
• Gas Path Health Management – VSST 
• Integrated Resilient Propulsion Control – VSST – complete 
• Robust Propulsion Control – VSST 
• Run-time Validation of Complex Systems - SSAT 
CDB Tasks Under NASA Aeronautics Research 
at Lewis Field 
Glenn Research Center 
Human Exploration and Operations Programs 
CDB Activities 
Controls and Dynamics Branch 
Space 
Launch 
System 
System 
Engineering 
& Integration 
Vehicle 
Mgmt 
Mission & 
Fault Mgmt 
Ground 
Systems 
D&O 
Command 
Control 
Comm. & 
Range 
Advanced 
Ground  
Sys. Maint 
Engines 
Low Earth 
Orbit 
Upper Stage 
Engine 
at Lewis Field 
Glenn Research Center 
SELF-OPTIMIZING 
ENGINE CONTROL 
SELF DIAGNOSTIC 
SELF PROGNOSTIC 
TECHNOLOGY 
SELF- 
RECONFIGURING 
CONTROL 
Autonomous Propulsion System Technology 
Model-Based Control 
And Diagnostics 
Reduce/Eliminate human dependency in the control and 
operation of the propulsion system 
Structural 
Health 
Gas Path 
Health 
Data Fusion 
SYSTEM 
IDENTIFICATION 
ADAPTIVE 
CONTROL 
ENGINE MODEL 
+ 
- 
Leverage 
Technology 
from other 
programs 
Demonstrate 
Technology 
in a relevant 
environment 
Vehicle Management System 
Self-Diagnostic Adaptive Engine Control System 
• Performs autonomous propulsion system monitoring, 
diagnosing, and adapting functions 
• Combines information from multiple disparate sources using 
state-of-the-art data fusion technology 
• Communicates with vehicle management system and flight 
control to optimize overall system performance 
Engine 
Condition/Capability 
Performance 
Requirement 
Controls and Dynamics Branch 
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
Model-Based Control and Diagnostics Concept 
Ground 
Level
Engine  
Instrumentation 
• Pressures 
• Fuel flow 
• Temperatures 
• Rotor Speeds 
Actuator  
Commands 
• Fuel Flow 
• Variable Geometry 
• Bleeds 
Ground-Based Diagnostics 
• Fault Codes 
• Maintenance/Inspection 
Advisories 
On-Board Model  
& Tracking Filter 
 
• Efficiencies  
• Flow capacities 
• Stability margin 
• Thrust 
Selected Sensors 
On Board 
 
Sensor 
Validation & 
Fault Detection 
 
Component 
Performance 
Estimates 
Sensor Estimates 
Sensor Measurements 
Actuator  
Positions 
“Personalized” Engine 
 Control 
Control 
Logic 
Sensor 
Measurements 
Actuator 
Commands 
FADEC 
Onboard Model & 
Tracking Filter 
Fault Detection & 
Isolation Logic 
Optimal Tuner Selection for Kalman Filter-
Based Performance Estimation 
Model-based Performance Estimation Architecture 
Thrust estimation accuracy comparison 
(conventional vs. optimal model tuning parameters) 
Background: 
• Adaptive on-board engine model 
• Applies Kalman filter-based 
tracking filter  
Challenge: 
• Underdetermined estimation 
problem – more unknowns 
(health parameters) than 
available sensor measurements 
Approach: 
• Define tuner vector that is a 
linear combination of all health 
parameters and systematically 
selected to minimize KF mean 
squared estimation error in the 
parameters of interest 
Results:  
• Linear Monte Carlo simulation 
studies have shown a mean error 
reduction of approximately 33% 
at Lewis Field 
Glenn Research Center 
An Integrated Architecture for Aircraft 
Engine Performance Trend Monitoring and 
Gas Path Fault Diagnostics 
Background: 
• Conventionally, performance trend monitoring and 
gas path fault diagnostics are performed off-board, 
post-flight, based on a limited quantity of 
“snapshot” measurements collected in-flight 
• Advancements in data acquisition/processing 
capabilities are providing access to streaming full-
flight engine measurement data 
• Enables continuous real-time monitoring of engine 
health, and integration with control logic 
Challenge: 
• Streaming data analysis capabilities 
• Efficient data compression and data management 
Approach: 
• Developed an integrated performance trend 
monitoring and fault diagnostic architecture 
containing two engine models operating in parallel 
 Real-time adaptive performance model 
 Performance baseline model 
• Fault diagnostics based on sensed residuals (y) 
between engine and performance baseline model 
Benefits:  
• Significant reduction in diagnostic latency 
• Ability to diagnoses intermittent faults 
• Enabling for model-based controls strategies 
Control 
logic 
Sensor 
Measurements 
Fault 
diagnostics 
Transmission to 
ground station 
Engine performance and diagnostic information 
+ 
- 
y 
Actuator 
Commands 
Model tuning 
parameters 
Real-time adaptive 
performance  model  
(Performance trend monitoring) 
Ground-Based System 
Ground 
Station 
Fleet-wide Trend 
Monitoring & Diagnostics 
On-Board System 
Denotes 
enhanced  
functionality Performance  
baseline model 
Controls and Dynamics Technology Branch 
at Lewis Field 
Glenn Research Center 
Model Based Engine Control 
    Goals 
• Use an on-board “self-tuning” model of the engine to provide accurate estimates of 
unmeasured parameters for control design as the engine ages 
• Allow for the engine to operate more efficiently and extend operating life 
    Approach 
• CMAPSS40k simulation as baseline engine 
• Integrate engine with Optimal Tuner Kalman 
Filter to get estimates of unmeasured 
parameters 
• Replace current control architecture with a 
Thrust controller and Stall Margin limit 
protection 
Results 
Thrust Control 
response over 
engine life with 
 traditional 
controller error 
bound 
Controls and Dynamics Branch 
Stall Margin 
estimation over 
engine life cycle  
  
at Lewis Field 
Glenn Research Center 
Vehicle Integrated Propulsion Research (VIPR) 
Ground-test Maturation of Engine Health Management (EHM) Technology 
Background: 
• NASA, in partnership with the U.S. Air Force and 
Pratt & Whitney, is conducting a series of on-
ground aircraft engine health management (EHM) 
technology tests 
• This series of tests is collectively referred to as 
Vehicle Integrated Propulsion Research (VIPR) 
 
Approach: 
• A series of on-wing engine ground tests 
conducted on a C-17 aircraft equipped with Pratt 
& Whitney F117 high-bypass turbofan engines 
• Tests include “nominal” and “faulted” engine 
operating scenarios 
 
Model-Based Gas Path Health Management 
Architecture: 
• Architecture provides dual-functionality of 
performance estimation and fault diagnostics  
• VIPR testing enables evaluation of self-tuning 
engine model’s ability to track engine performance 
• Insertion of gas path system faults (mis-scheduled 
bleed valves) enables evaluation of gas path fault 
detection and isolation capabilities 
Boeing C-17 Globemaster III Aircraft
Power sweep 
(steps up & down) 
Transients 
(slow & rapid)
VIPR Event Test Sequence (notional) 
Data acquisition over a 
range of power 
settings, including 
quasi-steady-state and 
transient engine 
operating scenarios. 
Controls and Dynamics Technology Branch 
Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) 
• A standard benchmarking problem and evaluation metrics to enable the comparison of candidate 
aircraft engine gas path diagnostic methods 
• Simulated problem implemented in the Matlab environment 
• Publicly available through the NASA GRC Software Repository 
https://technology.grc.nasa.gov/software/  
Engine Fleet Simulator User’s 
Diagnostic 
Solutions 
Evaluation 
Metrics Diagnostic 
assessments 
Sensed parameter 
histories 
1a. Engine fleet simulator: Enables user to 
specify the type and number of gas path fault 
cases. 
2. Solution providers apply 
their individual diagnostic 
solutions 
3. Evaluation Metrics: 
Defined and applied to 
provide a uniform 
assessment of 
performance 
 
 
Results Outputs 
4. Results: 
archived in 
common format 
Fault occurs 
Blind Test Case Data User’s 
Diagnostic 
Solutions 
Evaluation 
Metrics & 
“Ground 
Truth” 
Information 
 
 
Results Outputs 
Independent 
Development 
and 
Evaluation 
Fault occurs 
1b. Blind test cases: User has no a priori 
knowledge of fault existence or fault 
type 
Blind Test Case 
Side-by-Side 
Comparison 
“Ground truth” information 
ProDiMES Public Benchmarking Process 
Diagnostic 
assessments 
Sensed parameter 
histories 
at Lewis Field 
Glenn Research Center 
Information Fusion 
Controls and Dynamics Branch 
Lubrication 
Monitoring 
Vibration 
Monitoring
Gas Path 
Monitoring 
Other 
Information
Information Fusion 
Leverage all available information 
Health inferences do not have to be 
based solely on gas path 
measurements! 
 
• Other subsystem health information 
(e.g., vibration, lubrication, etc.) 
• Recent maintenance actions 
• Opposite engine health information 
• Control information—fault codes, limit 
activation  
• Fleet-wide engine statistics 
• Domain expert knowledge / heuristics 
• Negative information (the absence of 
information can be significant) 
Propulsion Level 
Reasoning 
Information Fusion 
Architecture 
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
Systematic Sensor Selection Strategy 
•  The S4 method provides for a 
systematic way to perform sensor 
selection. 
- Leverages design and heritage 
experience base 
- Predicated on the system fault 
detection/isolation philosophy. 
- Ability to perform sensor selection 
based on enabling diagnostic 
approach to discriminate between 
sensor and component failures. 
-  Accommodates various types of 
models/physical inputs  
Propulsion Applications: 
• Studies conducted for RS-83 and RS-84  
• Selected suites used in RS-84 HM test 
bed evaluations 
• Currently being applied to aircraft 
engine gas path health monitoring and 
the Ares Upper Stage J2-X engine 
Iterative Down-Select Process 
Knowledge Base 
Final Selection 
Health 
Related 
Information 
System 
Simulation 
Down-Select 
Algorithm 
(Genetic Algorithm) 
System 
Diagnostic 
Model 
Sensor Suite 
Merit 
Algorithm 
Optimal 
Sensor 
Suite 
Candidate Sensor Suites 
  i=0         i>0 
Candidate Selection 
Complete
Yes No 
Collection of Nearly 
Optimal 
Sensor 
Suites 
Statistical 
Evaluation
Algorithm 
Combustor 
Fan 
LPC HPC LPT HPT 
The overall fitness of different sensor 
suites selected by the S4 algorithm 
Target 
Num
Target 
Wt. N1 N2 P2 T2 P1 T1 P3 T3 P4 T4 T5 P6 T6 WF Fitness
Num 
Sensors
9 0.000 X X X X X X X X X X X X 27.708 12
9 0.010 X X X X X X X X X X X X 26.901 12
9 0.100 X X X X X X X X X X 23.161 10
9 0.500 X X X X X X X X X 20.857 9
10 0.001 X X X X X X X X X X X X 27.653 12
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
Ares I Real-time Data Qualification Studies 
using CDB-developed PHALT & FASTR Platforms 
CBP
P2
P0bP0a
P1bP1a
R-4R-3
R-1
R-2
R-14 R-15
R-6R-5
R-7
R-8
R-9
R-10
R-11
R-12
R-13
OR1
P2
P0bP0a
P1bP1a
R-17 R-19
R-16 R-20
R-18 R-21
CBP
P2
P0bP0a
P1bP1a
R-4R-3
R-1
R-2
R-14 R-15
R-6R-5
R-7
R-8
R-9
R-10
R-11
R-12
R-13
OR1
P2
P0bP0a
P1bP1a
R-16 R-20
R-18 R-21
CBP
OR1
P2
P0bP0a
P1bP1a
R-12R-11
R-1
R-6 R-8
R-5 R-9
R-7 R-10
R-2
R-3 R-4
Green Yellow Red Green Yellow Red Green Yellow Red
1 13 100% 0% 0% 62% 38% 0% 92% 0% 8%
2 4 0% 25% 75% 0% 25% 75% 100% 0% 0%
3 2 100% 0% 0% 100% 0% 0% 100% 0% 0%
4 2 0% 0% 100% 0% 0% 100% 50% 0% 50%
5 8 50% 50% 0% 50% 13% 38% 75% 25% 0%
Combined 29 66% 17% 17% 48% 24% 28% 86% 7% 7%
Test
Series 
No.
Tests
FASTR-1 FASTR-2 FASTR-3
The  3 Analytical Redundancy  Relationship 
Networks evaluated and summary of results 
 FASTR-1 FASTR-2 FASTR-3 
Portable Health 
ALgorithms 
Test (PHALT) 
System  
Fuel Actuator 
System Test 
Rig (FASTR) 
 
Green – Correct  Detection and Isolation 
Yellow – Fault Detected, Incorrect Isolation 
Red – False Alarm or Missed Detection 
 
at Lewis Field 
Glenn Research Center 
Controls and Dynamics Branch 
• Oil Debris Monitoring 
• HUMS – focused on helicopter engine gear box 
• Structural Health Monitoring 
Other IVHM Related Work 


IVHM Related Research at NASA Glenn

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server