As predictive maintenance converges with IoT, data science will play a pivotal role in driving innovation in equipment maintenance and related business outcomes. Just think of the billions of data points we capture across devices from infrared tomography, sonic and ultrasonic analysis, motor current analysis, vibration analysis etc. Yet according to Gartner 72% of manufacturing industry data goes unused due to the complexity of today's systems and processes. Why? Historically It has been a challenge to reliably and cost effectively collect and manage the IoT big data – further it takes data science and mathematics to help a human identify patterns, derive insight and take action on the data. Together MapR and RapidMiner are radically simplifying the picture and helping deliver the future of maintenance operations today.
Predictive maintenance is a bit ahead in the hype cycle, having past its peak. Now is the time for it to slowly penetrate business. However most executives are not clear on difference of predictive maintenance from the preventative maintenance they have been conducting for years so it would be great to start off with the definitions. Predictive maintenance: Perform maintenance when you predict that issues will arise. Keeps maintenance costs minimum since maintenance will only be completed when predicted and maintenance will be planned preventing urgent resource allocation inefficiencies.