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On the Road to Condition-Based
Maintenance for Army Vehicles
by MarK S. bo u n d S, Mary caLoMeriS, Mi ch a e L po h L a n d, a n d Ma r G u e r i t e ShepLer
The Army Materiel Systems Analysis Activity is developing onboard systems
for tactical wheeled vehicles that will allow maintainers and operators
to base preventive maintenance actions on actual vehicle conditions.
Condition-based maintenance (CBM) is a strategy
that bases the performance of maintenance
on the actual condition of the system and not on
fixed time intervals. This strategy is made possible by
the application of usage characterization, diagnostic,
and prognostic processes executed on a health and
usage monitoring system (HUMS).
“Usage characterization” refers to the evaluation of
the manner in which a system is being employed and
indicates how and why things may be broken or in the
process of breaking. Usage characteristics include
hours run, miles driven, time at idle, fuel consumed,
number of hard brakes and hard turns, vehicle speeds
over specific terrains, and so forth. “Diagnostics” are
based on the symptoms or indicators of problems and
use methods to find what is broken or breaking in a
system. “Prognostics” are based on a combination of
indicators and physics of failure methods; they result
in methods for predicting when components are going
to break. [“Physics of failure” refers to the analysis
of the physical processes that result in system failures.
Such an analysis can be used to improve system reliability
and maintainability.]
The Army Materiel Systems Analysis Activity
(AMSAA) at Aberdeen Proving Ground, Maryland, in
conforming to the common logistics operating environment
(CLOE) concepts developed by the Logistics
Innovation Agency, is focused on developing ways for
vehicles to self-diagnose and self-report their conditions.
Specifically, AMSAA is working on predictive
maintenance algorithms using both the maintenance
and operating histories of vehicles. The onboard system
that AMSAA has designed in conjunction with
the Aberdeen Test Center collects data from a vehicle’s
onboard vehicle sensors, data bus, terrain sensors, and
global positioning system (GPS) and analyzes the data
to determine the vehicle’s condition.
AMSAA’s Strategy for CBM
CBM is being implemented in four phases. In
phase 1, AMSAA identified appropriate hardware
and software to be integrated into a HUMS that could
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be used for engineering development purposes—an
engineering development HUMS (EDHUMS)—and
completed initial testing and evaluation of these
components in continental United States (CONUS)
test and training environments. Phase 2 consisted of
integrating these components into a robust, militarygrade
EDHUMS, designing a data analysis process,
testing EDHUMS in the CONUS training environment,
and beginning to field EDHUMS in operational
units outside of CONUS, including the U.S.
Central Command’s area of responsibility. AMSAA
is currently developing a solution for the information
management process.
Phase 3 (also underway) consists of identifying
a small, inexpensive focused HUMS (FHUMS)—a
HUMS with limited but very specific capabilities
that can be deployed across larger fleets because
of its lower cost and that can be programmed with
algorithms developed on the EDHUMS. Phase 4
of AMSAA’s strategy includes integrating proven
FHUMS hardware into platforms by the original
equipment manufacturer at the time of manufacture
or into other appropriate proven hardware.
AMSAA’s approach to implementing CBM also
includes the use of two data “prongs,” which are
diagnostics and prognostics and usage. AMSAA collects
data from imbedded onboard sensors and added
sensors, including the data bus, GPS, and terrain
sensor. The data are fed into diagnostic and prognostic
algorithms that then report impending failures
and unsafe or damaging usage to the vehicle driver,
maintainers, and commanders. The usage data are
compiled and reported to fleet managers, engineers,
and maintainers.
The final part of AMSAA’s strategy for CBM
implementation is testing in “parallel environments.”
Testing in both controlled and intheater environments
allows for heavily-instrumented vehicles to be evaluated
under many scenarios. Measurements can be
made in a controlled and well-defined environment on
known test courses. Such testing provides large data
sets for developing damage models and prognostic
JULY–AUGUST 2008