LAB TO LOGIS TICS
Phys ics Informed Machine Learning (PIML) for
Advanced Diagnos tics & Prognos tics of Ground
Combat Vehicles
J uan F. Betts & Aras h Alizadeh
PrediciveIQ
MOTIVATION: WHY USE PIML FOR ENGINE
HEALTH?
LAB TO LOGIS TICS
AOAP OR BIG DATA ML WILL NOT LEAD TO A PREDICTIVE OIL HEALTH SOLUTION,
THEREFORE A PHYSICS INFORMED ML APPROACH IS REQUIRED
• The number of AOAP sample is too
small over the life of a program to
properly train a data driven machine
learning solution.
• PIML PPMX becomes predictive prior
to production and can handle nonstationary Army mission specific plans
PROGNOSTICS & PREDICTIVE
MAINTENANCE (PPMX) WORKFLOW
1. Problem
Assessment
2. Experiments
on Engine
3. Physics
Model
Development
4. PIML Model
Development
5. PIML V&V
UQ
LAB TO LOGIS TICS
6. PIML
Integration into
PPMX
Dashboard
7. PIML PPMX
Demonstration
ENGINE HEALTH DRIVERS
• Engine health is driven by the oil degradation &
contam ination, and the depos its created through
engine wear.
• Category 1 s ens ors are optional phys ical s ens ors
that can m eas ure s om e of the des ired drivers of
engine health (e.g. oil vis cos ity) and alert operators
of potential is s ues
• Category 2 s ens ors are s ens ors that are difficult to
be im plem ented in realworld conditions outs ide of a
tes t s etting. Therefore, thes e s ens ors are derived
from Category 1 s ens ors . Category 2 s ens ors are
s om etim es called “virtual s ens ors .”
• A m odular PIML PPMx PDT fram ework was
propos ed to us es phys ics -bas ed m odels and ML
fram eworks to predict Category 1 and Category 2
s ens ors . Thes e m odels would be Scalable to other
GVS engines with s om e m odel trainings .​
LAB TO LOGIS TICS
ENGINE WEAR
LAB TO LOGIS TICS
EXPERIMENTS ON ENGINE
• Cum m ins perform ed NATO50 and durability tes ts on
ACE in accordance with the m ethods and s tandard
conditions of AEP-5 Part-II​
• A data driven m odel was built to identify the m os t
im portant features influencing TAW in the engine
and reduce the num ber of features us ed in the final
PIML m odel.
• Features m ay depend on each other, s o we
perform ed a correlation analys is to rem ove the
correlated features from the m odel.
• The m odel was built us ing the durability tes t data-s et
for the ACE provided by Cum m ins .
• Wear volum e was defined as ∑(𝑁𝑁_{𝑝𝑝𝑎𝑎𝑟𝑟𝑡𝑡𝑖𝑖𝑐𝑐𝑙𝑙𝑒𝑒𝑠𝑠}
×𝑆𝑆𝑖𝑖𝑧𝑧𝑒𝑒_{𝑝𝑝𝑎𝑎𝑟𝑟𝑡𝑡𝑖𝑖𝑐𝑐𝑙𝑙𝑒𝑒𝑠𝑠} )
• We reduced the num ber of features to three by
em ploying m ethods like Factor Analys is (FA),
Variance Inflation Factor (VIF) and Engineering
J udgem ent
LAB TO LOGIS TICS
Coolant Outlet Temp
PHYSICS INFORMED TAW MODEL
• Engine TAW Model
–Sensor Data: Oil Temperature, Oil Pressure,
Engine Speed, and Oil Viscosity
–Data Driven Models: Fuel Dilution, Oil Acidity
–Physics Based Models: Oil Film Thickness,
Friction
–Hybrid Models: Wear, Oil Status
• A PINNs based virtual sensor (VISCO module)
was developed that predicted the engine oil
viscosity for every given temperature
• The physics model was validated with literature
examples.
LAB TO LOGIS TICS
PIML MODEL DEVELOPMENT
• Deep Operator Network (DeepONet) is based on
classical universal approximation theorem of
continuous functions. The operator G is a nonlinear
continuous operator.
• Transfer learning is the idea of overcoming the
isolated learning paradigm and use the knowledge
acquired for one task to solve related ones.
• In transfer learning, we can leverage
knowledge (features, weights etc) from previously
trained models for training newer models
with significantly less data for the newer task.​
• Transfer learning enables building a predictive model
for a specific mission prior to having any field data.​
Physics Model
Train
• The architecture is based on a design of two subnetworks, the branch net for the input function and
the trunk net for the location to evaluate the output
function.
LAB TO LOGIS TICS
Transfer Learning
DeepONet
BACKEND PREDICTIVE METHODOLOGY
Pre-trained Model
LAB TO LOGIS TICS
Predictive Model
New NATO 50 Data
DeepONet
Physics Model
PIML VALIDATION WITH NATO50 DATA
LAB TO LOGIS TICS
PIML TAW V&V UQ
LAB TO LOGIS TICS
ENGINE HEALTH PPMX ARCHITECTURE
PredictiveIQ
Models
LAB TO LOGIS TICS
OIL HEALTH AND MODULAR EXTENSION
LAB TO LOGIS TICS
PPMX DEMONSTRATION
LAB TO LOGIS TICS
LAB TO LOGIS TICS
THANK YOU
J uan F. Betts & Aras h Alizadeh
PrediciveIQ