Introduction to Prognostics

81 Pages·2015·5.78 MB·English

Introduction to
Prognostics
George Vachtsevanos, Abhinav Saxena, Marcos Orchard, Scott Clements, and many others
The G eorgia Institute of Technology
[email protected]

Kai Goebel, George Gorospe, Chetan Kulkarni, Matt Daigle, Scott Poll, Indranil Roychoudhury,
Shankar Sankararaman, Chris Teubert
N AS A Ames R esearch C enter, P rognosti cs C enter of E xcel l ence
[email protected]
I. Diagnosis:

from the Greek (of course!): διάγνωσις

meaning:

knowledge of an event during

the occurrence
II. Prognosis:

from the Greek (again!): πρόγνωσις

meaning:

knowledge of an event before it occurs

prediction
In engineering terms: “non-causal system”
If you go to war, you shall return not die at
war.
Outline
 Prognostics Overview
– What is Prognostics?
– Types of Prognostic algorithms
 Trends, Remaining Useful Life, & Uncertainty
– What does a prognostic algorithm tell you?
– How does uncertainty screw everything up?
 Prognostics Methods
– Data-Driven Methods
– Physics-Based Methods
– Models
– Algorithms
– Hybrid Approaches
 Application Examples
 Other Considerations
– Metrics and Requirements
– Current Challenges in Prognostics
 Q&A
Prognostics Overview
“It’s tough to make predictions,
especially about the future.”
Yogi Berra
Prognostics Definition
4.2
4 E (measured)
Prediction points
3.8
)
V 3.6 E (from PF)
(

e
g 3.4
a
t
l
o 3.2
v
3
2.8 E EOD pdfs
EOD
t
2.6
EOD
0 500 1000 1500 2000 2500 3000 3500
time (secs)
Definition: Predict progression of an event based on current and
future operational and environmental conditions to estimate the time at
which a system no longer fulfils its function within desired specs
(“Remaining Useful Life”)
Ingredients for Prognostics
• RUL: Remaining Useful Life
– Model underlying physics of a component/subsystem
Top
Pneumatic Port
Return Spring
Piston
Bottom
Pneumatic Port
Plug
Fluid Flow
– Model physics of damage propagation mechanisms
4x 106Damx a1g0e6 Progression of Friction Coefficient
3.5 1.1
)m/sN( tneiciffeoC noitcirF21..55321 1.0540 41
0.5
– D00 e20t4e0 r60 m80 10i0 ne criteria for End-of-Life threshold
Time (cycles)

– Develop algorithms to
propagate damage into future

– Deal with uncertainty
What we can do with Prognostics
Aircraft Prognostic Candidates (examples)
Heat Exchangers
Air Conditioning System
Hydraulic Pump
Battery
Hydraulic Filters
Composite Structures
Landing Gear Strut
Rotary Actuators
Brakes
Engines
Tires
The Need/Opportunity/Challenge
• Technology development – A multi-disciplinary approach to system
studies, sensing and data analysis, fault diagnosis and failure prognosis,
reconfigurable control, ROI, etc.

• The opportunity: Advances in sensing, computing, communications;
recognition of potential benefits in terms of improved availability,
reliability, safety, maintainability and survivability.

• The challenge: A paradigm shift; cultural and technical issues; show me!
Transitioning on-platform.
Prognostics - Preliminaries
Remaining Useful Life (RUL) – The amount of time a component can be
expected to continue operating within its stated specifications. Dependent on
future operating conditions; input commands; environment; loads
Health - Based Prognosis: A fault or incipient failure condition has been
detected, isolated and its severity assessed; prognosis predicts the remaining
useful life of the failing component/system.
Usage – Based Prognosis: Long-term prediction/forecasting of the health state
of a component/system subjected to internal and/or external stresses.
Prognosis vs Trending vs Predictive Diagnostics

• Prognosis: Predict the remaining useful life or time to failure of a
failing component/system
• Trending: Trend or linearly project/regress a current measurement
until it reaches a predefined threshold
• Predictive Diagnostics: Find precursors to failure