What questions can be answered from Predictive Maintenance?
What is the probability that a piece of equipment fails in the near future?
What is the remaining useful life of the equipment (RUL)?
What are the causes of failures and what maintenance actions should be performed to fix these issues?
Why Predictive Maintenance?
Utilizing machine learning through regression and classification algorithms, Predictive Maintenance can analyze patterns and determine the remaining useful life of any equipment. An Apriori algorithm is used to recommend whether a particular equipment will be needing maintenance alongside other equipment based on historical maintenance data.
According to a study by the Department of Energy (US), a facility that leans heavily on predictive (rather than reactive or preventative) performance management methods can save 30-40% in equipment maintenance costs.
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Reduced equipment costs
Instead of replacement of the entire piece of equipment due to
critical failure, a repair is made prior to failure and cost is minimized
to the price of the component and the labor needed for the repair.
REDUCE LABOUR COSTS
When repairs are scheduled, the amount of time needed for repair is
reduced because of a smaller number of component replacements
instead of entire equipment replacement. Also, the frequency of
repair for critical failure of equipment will be reduced and the
amount of “critical callouts” will be greatly reduced..
Predictive maintenance would allow potential problems to be fied
before failure occurs, which would create safer operating
conditions for employees and customers..
Reduces lost production time
Component only replacement is scheduled with production to
take place during scheduled downtime. Unscheduled
downtime may cost thousands of dollars per hour. A proactive
maintenance department can head of critical failure
downtime by scheduling repair during non-productive times.