Page 345 - Intro Predictive Maintenance

336 An Introduction to Predictive Maintenance

vibration, process, and other data that will provide a viable predictive maintenance
database. Therefore, the system must be able to automatically select and set monitor-
ing parameters without user input. The ideal system would limit user input to a single
operation, but this is not totally possible with today’s technology.

Automated Data Management and Trending
The amount of data required to support a total-plant predictive maintenance program
is massive and will continue to increase over the life of the program. The system must
be able to store, trend, and recall the data in multiple formats that will enable the user
to monitor, trend, and analyze the condition of all plant equipment included in the
program. The system should be able to provide long-term trend data for the life of the
program. Some of the microprocessor-based systems limit trends to a maximum of 26
data sets and will severely limit the decision-making capabilities of the predictive
maintenance staff. Limiting trend data to a finite number of data sets eliminated the
ability to determine the most cost-effective point to replace a machine rather than let
it continue in operation.

Flexibility
Not all machines or plant equipment are the same, and neither are the best methods
of monitoring their condition equal. Therefore, the selected system must be able to
support as many of the different techniques as possible. At a minimum, the system
should be capable of obtaining, storing, and presenting data acquired from all vibra-
tion and process transducers and provide an accurate interpretation of the measured
values in user-friendly terms. The minimum requirement for vibration-monitoring
systems must include the ability to acquire filter broadband, select narrowband, time
traces, and high-resolution signature data using any commercially available trans-
ducer. Systems that are limited to broadband monitoring or to a single type of trans-
ducer cannot support the minimum requirements of a predictive maintenance program.

The added capability of calculating unknown values based on measured inputs will
greatly enhance the system’s capabilities. For example, neither fouling factor nor effi-
ciency of a heat exchanger can be directly measured. A predictive maintenance system
that can automatically calculate these values based on the measured flow, pressure,
and temperature data would enable the program to automatically trend, log, and alarm
deviations in these unknown, critical parameters.

Reliability
The selected hardware and software must be proven in actual field use to ensure their
reliability. The introduction of microprocessor-based predictive maintenance systems
is still relatively new, and it is important that you evaluate the field history of a system
before purchase. Ask for a list of users and talk to the people who are already using
the systems. This is a sure way to evaluate the strengths and weaknesses of a partic-
ular system before you make a capital investment.