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Problem with Your Droid? Predictive Maintenance to the Rescue

July 18, 2016

What does Star Wars have in common with predictive maintenance software? In Episode IV, there’s a moment where C-3PO saves himself and R2-D2 from the Storm Troopers by claiming, “All the excitement has overloaded the circuitry of my counterpart. If you don’t mind, I’m going to take him down to maintenance.” C-3PO may have been fudging the truth about the need to repair R2-D2 to an absurd extreme in that instance, but preventative maintenance software for machines is serious business.

Maintenance workers every day face the problem of ensuring the maximum number of devices are in working order, while simultaneously reducing the number of repairs and materials consumed. In response to this demand, predictive maintenance software opens up innovative new possibilities for firms.

Why is Predictive Maintenance Software Important?

All around us, machines are helping us. Airplanes and hospitals use monitoring systems to save lives. Sensors used in the Internet of Things, such as blood pressure monitors, water management, and even fitness devices monitor machine conditions. Predictive maintenance software can then help humans automatically review data from the machines to pick up any indication of potential errors. Problems can be spotted earlier, and corrective measures can be planned and introduced in before it’s too late. Minimizing and eliminating unplanned downtime maximizes human and financial resource potential.  The best predictive maintenance software systems are even “smart;” using information on past problems to predict what’s coming.

Predictive maintenance aims first to identify when an equipment failure might occur, and second, to prevent the occurrence of the failure through early intervention.

Predicting Equipment Failure with Predictive Maintenance

Software designed for predictive maintenance, combined with data wrangling tools like Trifacta, allow analysts to monitor for future issues and perform maintenance before a potential failure occurs. Ideally, predictive maintenance minimizes the frequency of repairs, circumventing unplanned reactive maintenance, without incurring additional costs associated with extraneous maintenance.

Failure can be predicted during the predictive maintenance process using vibration analysis, thermal imaging, and equipment observation. Metrics like these can provide warning signs through the software to alert workers of needed maintenance. When combined with a data wrangling tool like Trifacta that can save data integrity as well as repeat data transformation, speed to insight can be improved dramatically. Choosing the correct method for performing condition monitoring is best done in consultation with equipment manufacturers as well as condition monitoring workers. But not everyone is comfortable using advanced data analysis techniques. Tools like Trifacta allow team decisions and input about data transformation to be saved and reused; so that all users can interpret data consistently throughout large organizations.

Preventing Failure Occurrence with Predictive Maintenance 

Compared with preventative maintenance, predictive maintenance software ensures that a piece of equipment requiring maintenance is only shut down right before imminent failure. Customer satisfaction and in some cases even consumer safety is then at risk. Data analysts can use predictive modeling to forecast when a failure is more likely to occur, thus reducing the likelihood of a failure in the first place.  

The skill level and experience required to interpret condition monitoring data accurately is also high. Tools like Trifacta make it easier for non-technical employees to understand and report on data, which can allow teams of mixed abilities to find transformational insight faster than ever.   

Of course, if the machines never fail, then C-3PO’s excuse won’t be believed, but we know he’s smart enough to find another way to save the day.  

To learn more about data wrangling, try out our free cloud product, Trifacta Wrangler!

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