# Presentation: 491643

## Advanced Controls andGas Path Health Management:  Relevance to Propulsion Safety and Affordable Readiness

- Don Simon & Jonathan Litt
- Army Research Laboratory – Vehicle Technology Directorate (Z)
- Controls and Dynamics Technology Branch (RIC)
- DLT Forum
- May 5, 2006

## NASA & DoD Safety-Related Programs

|  |  | NASA
Aviation Safety Program | DoD
Propulsion Safety & Affordable Readiness (P-SAR) Program |
| --- | --- | --- | --- |
| Goals | Safety | Improve inherent safety of legacy and new vehicles
Overcome safety technology barriers which would otherwise constrain full realization of the Next Generation Air Transportation System | Reduce propulsion related Class A mishaps |
|  | Affordability |  | Reduce propulsion maintenance costs |
|  | Readiness |  | Increase average time on wing |
| Projects / Elements |  | Integrated Vehicle Health Management
Integrated Intelligent Flight Deck
Integrated Resilient Aircraft Controls
Aircraft Aging and Durability | Hot Section
Cold Section
Mechanical Systems & Drives
Electrical Power Generation
Prognostics & Health Management
Life Management |

## Outline

- Aircraft Engine Life Extending Control
- Gas Path Health Management
- Overview
- Enhanced Bank of Kalman Filters for Sensor Fault Diagnostics
- Automated Power Assessment for Turboshaft Engines
- Data Fusion System For Aircraft Engine Foreign Object Damage Detection

## Life Extending Controls

**Goal: Reduce maintenance costs by increasing the average engine on-wing life**
**Approach**
_**Life Modeling**_** **
- Developed a sensor-based Thermomechanical Fatigue (TMF) life model for the first stage cooled stator of the High Pressure Turbine (HPT)
- Developed a probability of failure model, based on a Weibull distribution, to calculate a components risk of failure based upon its past operating history
- Included uncertainty in sensor measurements, actuators, and material qualities
_**Controls**_
- Developed a new control strategy which optimizes the engine core speed (N2) acceleration schedule to minimize TMF damage while maintaining adequate engine rise time

- Retired HPT Blades

## Engine Simulation Demonstration of Life Extending Control Using Stochastic Based Life Models

| Optimized
Control
Schedule |
| --- |
| 0.628
(-37.2%) |
| 0.718
(-36.6%) |
| 0.931
(-44.2%) |
| 0.481
(-21.0%) |

**Comparison of Average TMF Damage Accumulation**
**(With Varying Ambient Condition and Control Mode )**

**TMF Damage Accumulation w/ Original Control**
**(At Varying Ambient Conditions)**

- Control

- Operating
- Condition

**1. Component damage accumulation is a function of ambient operating condition**

|  | Original
Control
Schedule |
| --- | --- |
| Design
Condition | 1.0 |
| With Typical 
Operating
Conditions | 1.132 |
| Hot Day
Bias | 1.668 |
| Cold Day 
Bias | 0.609 |

## Aircraft Engine Gas Path (Flow Path)Health Management

## Gas Path Health Management is a Key Element of an Overall Aircraft Engine Health Management System

- Vibration
- Diagnostics

- Lubrication
- System
- Monitoring

- Component
- Life Usage
- Monitoring

- FADEC
- Control &
- FDIA

- Performance
- Diagnostics
- (Ground-based)

- Gas Path
- Diagnostics
- (On-board)

- Engine Health Management System

**Closely Coupled**

**Closely**
**Coupled**

## Gas Path Performance Diagnostics Engine Fault Isolation Approach *

- A general influence coefficient matrix may be derived for any particular gas turbine cycle, defining the set of differential equations which interrelate the various dependent and independent engine performance parameters.

- _Physical Problems_
- Erosion
- Corrosion
- Fouling
- FOD
- Worn seals or excessive clearance
- Burned, bowed or missing blades
- Plugged nozzles
- Sensor/Actuator Bias

- Degraded Module_    Performance_
- Flow capacities
- Efficiencies
- Effective nozzle areas
- Expansion coefficients

- Changes in Measurable_ Parameters_
- Spool speeds
- Fuel flow
- Temperatures
- Pressures
- Power output

- Result in

- Producing

- Permitting correction of

- Allowing isolation of

- * From “Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines” by Louis A. Urban, 1974

## Gas Path Health Management Process

- 2. Condition/Trend
-     Monitoring

- 3. Detection

- 4. Isolation

- 5. Recommendation

- 1. Sensing & Data
-     Acquisition

- # of Flights

- Health Parameter

- Engine Performance Deterioration

- and Abrupt Event Illustration

## Gas-Path Health Management Architecture

- Sensor
- Measurements

- Actuator
- Commands

*On-Board*

*Ground-Based*

- Engine

- FADEC

- Ground
- Station

- Onboard Model &
- Tracking Filter

- Fleet-wide Trend & Condition Monitoring

- Fault Detection &
- Isolation Logic

- Control
- Logic

## Enhanced Bank of Kalman Filters forSensor Fault Diagnostics                 .

- Sensor Fault
- Hypothesis

- Enhanced Bank of Kalman Filters forSensor Fault Diagnostics                 .

**Filter 1**

**Filter *****m***

*y*1

*y**m*

*y*

*u**cmd*

*y**i*

**Filter *****i***

- Sensor Sorting: ith sensor removed from y to create yi vector

*WSSR*1

*WSSR**i*

*WSSR**m*

-  No Fault
-         or
-  Fault Detected
-         or
-  Fault Isolated

- Fault Isolation Process

- Computation of weighted sum of squared residuals, WSSR

**Filter 2**

*WSSR*2

*y*1

- ^

*y*2

- ^

*y*i

- ^

*y*m

- ^

- Fault Indicator Signal Generation Process

- Fault Isolation Process

- & Accommodation

## Bank of Kalman Filters Example:Detection & Isolation of a T3 Sensor Bias

- Generate Fault
- Indicator Signals

- Bank of
- Kalman
- Filters

## Automated Power Assessment for Helicopter Turboshaft Engines 

- Army Research Laboratory Vehicle Technology Directorate task with Aviation & Missile Research Development and Engineering Center (AMRDEC) Aviation Engineering Directorate (AED)
- Objective: Develop a technique for the automated continuous assessment of available power for helicopter turbo-shaft engines

## Automated Power Assessment for Helicopter Turboshaft Engines (Background)

**Engine Maximum Power Check (MPC)**
- Establishes Engine Torque Factor (ETF), an indication of available engine power
- Requires engine performance data to be collected at altitude while maintaining constant airspeed and operating the engine at a control limit
- ETF determined as a function of recorded parameters and target torque values
- Required when an engine is first installed, or when an engine fails the Health Indicator Test (HIT) Check
**Operation in theater**
- Desert (sandy) environments significantly accelerates engine deterioration – necessitates more frequent MPCs
- Performing MPCs in theater presents risk of vehicle incurring hostile fire

- ETF Table Lookup

- T700 Stage 1 Blisk Erosion
- Source: Westar Aerospace & Defense Group, Inc. Army RIMFIRE Program

## Automated Power Assessment for Helicopter Turboshaft Engines

- Progress to date:
- Real-time table lookup model developed to process Health and Usage Monitoring System (HUMS) data
- Inputs: ambient temperature, ambient pressure, airspeed, and shaft horsepower
- Outputs: nominal gas generator speed (Ng) and turbine gas temperature (T45)
- Influence coefficient matrices generated to estimate engine performance deterioration from measurement residuals
- Remaining Tasks:
- Non-uniform update of performance estimates (collected at varying operating conditions over time)
- Extrapolation of performance estimates to high power conditions
- Automation of the Engine Torque Factor (ETF) calculation

- Evolution of Ng & T45 residuals for a T700-701C engine over a six month period

## Gas-Path Health Management Architecture

- Sensor
- Measurements

- Actuator
- Commands

*On-Board*

*Ground-Based*

- Engine

- FADEC

- Ground
- Station

- Onboard Model &
- Tracking Filter

- Fleet-wide Trend & Condition Monitoring

- Fault Detection &
- Isolation Logic

- Control
- Logic

## Data Fusion System For Foreign Object Damage Detection

- Jonathan Litt
- Z/RIC

## Data/Sensor Fusion

- Use multiple disparate sensor suites to observe an event though different eyes.
- Various “domain experts” form an opinion about the event based on the evidence and their knowledge.
- The expert opinions are brought together to support or refute each others’ inferences.
- When experts agree, their final fused opinion carries more weight than individual opinions

## Cues and Symptoms of Foreign Object Damage (FOD)

- How does the flight crew recognize a FOD event?
- Engine surge, potentially resulting in the loss of power
- Thud or bang
- Fire warning
- Flame coming out of the engine
- Vibration
- Yaw of the airplane caused by thrust imbalance
- High Exhaust Gas Temperature (EGT)
- Change in the spool speeds
- Smoke/odor in cabin bleed air
- Engine Pressure Ratio (EPR) change
- Flock of birds in the immediate vicinity
- 	Several of these should occur TOGETHER, would a pilot diagnose a FOD event if only one symptom is evident?

## Why Develop This System?

- System developed to raise the level of autonomy of the engine and thus reduce pilot workload
- Most FOD events occur close to the ground where pilot workload is greatest
- Pilot procedures in place to evaluate engines with suspected FOD once safe altitude is reached, easier for pilots if potentially damaged engines are identified
- Rejected take-off due to suspected FOD is a cause of accidents
- Even if no obvious damage, impact can produce cracks that can be propagated through high-cycle fatigue

## Problem Setup

**Engine Simulation**
**Structural and**
**Aerothermal**

- FOD
- DETECTION
- DATA FUSION
- SYSTEM

- Expert Consensus
- (or not)

- Gas Path
- Measurements

- Accelerometer
- Measurements

## Use Tools to Analyze Data and Extract Features and Fuse

- A Kalman Filter can analyze gas path data to estimate shift in fan efficiency
- Wavelet analysis can be used to identify the signature of an impulse due to impact from accelerometer data.
- Fuzzy logic can be used to interpret features extracted from the data
- Dempster-Shafer-Yager Theory to combine expert opinions while accounting for conflict

## FOD Detection Data Fusion System Overview

## Foreign Object Ingestion Detection Data Fusion System

- PROCESS
- SENSOR
- OUTPUT

- PROCESS
- SENSOR
- OUTPUT

- FUZZY
- LOGIC
- PROCESSING

- FUZZY
- LOGIC
- PROCESSING

- FUZZY
- LOGIC
- OUTPUT

- FUZZY
- LOGIC
- OUTPUT

- FUSE OPINIONS USING
- DEMPSTER-SCHAFER-YAGER
- THEORY OF EVIDENCE

**Plausibility: 1 -  ( all evidence supporting NOFOD) **
**Belief: all evidence in direct support  of FOD**

- FINISH

## Final Comments

- This work was performed using simulated data and best guesses based on the very limited literature on FOD events. The system provides a framework for more expert knowledge
- The framework is flexible and modular
- The structure facilitates the incorporation of additional experts in parallel
- The existing fuzzy rule bases can be changed easily based on the acquisition of expert knowledge
- Additional features can be easily added to the rule bases