BEST PRACTICES
Implementing Predictive Maintenance
Hiring experts / Troubleshooting / Interdepartmental cooperation
HE PREDICTIVE MAINTENANCE
program at the Greater Cleveland
Regional Transit Authority
began in 2015. Now in our
fourth year with the program,
we have had successes and lessons
learned. Ultimately, we view this
maintenance philosophy as our
strategy going forward and are
committed to it for the long term.
Our fi rst step toward implementation
was to hire a consultant
group who had experience
with predictive maintenance at
other transit properties so that
we could learn the methodology.
Together with the consultant, we
analyzed historical maintenance
records and parts usage of our
highest profi le but least reliable
bus fl eet: the HealthLine. Th is
eff ort identifi ed parts to be replaced
Cleveland, Ohio
MOLD, VIRUSES,
BACTERIA, GERMS,
DUST MITES, ODORS,
ROACHES, FLEAS,
TICKS, BEDBUGS
70 | Mass Transit | MassTransitmag.com | SEPTEMBER/OCTOBER 2018
and maintenance tasks
to be performed at annualized
intervals throughout the life of
the fl eet. We also needed to educate
the workforce on our shift
in maintenance strategy. We did
this through a series of
newsletters explaining
the premise of predictive
maintenance and why we
felt that this was the best
path forward.
Our implementation
of predicative maintenance
on the HealthLine
fl eet was not without
challenges. By starting
on a fl eet that had been in
operation since 2008, we
essentially performed a
mid-life overhaul of each
bus at our fi rst maintenance
interval. Th is led
to delays in production.
To address these delays,
we decided to combine
maintenance intervals
to ensure future compliance
with the program.
Th is required great collaboration
amongst several
departments.
While rolling out predictive
maintenance to the
HealthLine vehicles, we
began expanding this program
to include our newest
bus fl eets, purchased in
2015 and 2017. Th rough the course
of our expansion, we have become
smarter and more nimble in our
approach. We have identifi ed a
“prototype bus” that goes through
each maintenance interval several
months ahead of the rest of the
fl eet. Th is allows us to identify any
issues with the parts kits before
moving into production on the
rest of the fl eet. Earlier this year,
we added a formal Quality Assurance
check performed by our Fleet
Management department, to ensure
that all work was completed
correctly and that all work orders
are closed.
To analyze the eff ectiveness of
this program, we track our ROI
through the service reliability of
our fl eets (miles between service
interruption) and inventory costs.
Parts costs per vehicle mile has increased,
as expected, but so has vehicle
reliability. We view this as an
acceptable cost for a more reliable
service. In 2017, GCRTA achieved
15,000 miles between service interruption
system-wide, far surpassing
our 8,000 MBSI goal. Th e
2015 bus fl eet, the fi rst fl eet to go
through our predictive maintenance
program since inception,
averaged more than 30,000 miles
between service interruption.
Our implementation of predictive
maintenance was not
without its road bumps. Ultimately
we view this approach as
our standard maintenance strategy
for the future. We currently
have three fl eets enrolled in our
predictive maintenance program
and are committed to adopting
this model for all new fl eets going
forward.
T
Nicholas Biggar
Hayden District
Director
Greater Cleveland
Regional Transit
Authority
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