INTERNET OF THINGS_proof 26/10/2016 11:05 Page 12
INTERNET OF THINGS
At a coal operation,
vibration data was
collected with an
Azima DLI TRIO™
vibration analyser
and transmitted to a
cloud database where
it was run through
Azima DLI’s
automated diagnostic
system and reviewed
by a live analyst
causes behind the
failure, in order to
circumvent similar
failures in the future.
Root cause analysis,
performed by
personnel at the facility, takes into account the machine, the environment,
the personnel involved and their level of expertise, the tools, parts, and
treatments (including grease and lubricant) used, as well as any potential
contaminants or maintenance issues that may have contributed to the
failure. Root cause analysis, when performed correctly, minimises the
possibility of reoccurrence.
A vibration data collection work order was implemented on a monthly
basis, and Azima DLI was authorised to train mine personnel in vibration
data collection. These maintenance strategies, built on root cause analysis,
minimise the risk of the reoccurrence of similar faults in mine machinery.
The coal facility hired Azima DLI as their primary predictive maintenance
provider. Azima DLI analysts returned to the mine to prepare 100 machines
for vibration data collection and analysis. Azima DLI analysts also trained
personnel to operate TRIO data collectors, ensuring independence and
another level of vigilance safeguarding secure and efficient mine
production. Azima DLI continues to monitor, analyse, and recommend
actions regarding the information mine personnel upload to the Azima DLI
cloud database.
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