Achieving distributed device situational awareness through cloud-based data management
Predictive Maintenance
Predictive maintenance and asset management are examples where advanced OT/IT integration can directly affect revenue. Compared to preventive maintenance, this approach reduces costs because work is performed only when needed. Predictive maintenance is predicated on continuous, real-time monitoring of in-service equipment to predict when maintenance is required.
But it is not enough to just monitor the state of equipment. Just like with the windmill example, the real-time data on its own does not provide enough information to determine whether the equipment is operating within normal bounds. A sensor indicating high temperature may be caused by equipment malfunction, or it may be caused by an increase in factory output.
To be certain, the monitoring data must be analyzed in the context of historical information to determine whether any action needs to be taken.
The same techniques and algorithms used to solve the maintenance problem can also be used to provide long-term business intelligence. Besides preventing unexpected equipment failure, spotting long-term trends in equipment usage increases availability and improves equipment lifetime.
The Needle In The Haystack
Many companies have already demonstrated the value of using Big Data technologies to sift through the vast amounts of information generated by IT systems. The technology has proven adept at finding the proverbial needle in a haystack. At the same time, organizations are just beginning to realize the business value of data produced by OT systems.
Merging OT and IT data is the next logical step, but the integration brings forth many technical challenges. OT systems generate even more data to analyze (more “hay” to hide the needle), while IT systems must be integrated in such a way as to not impact the time sensitivity of OT real-time data flows.
By selecting the right mix of technologies to solve the problems of data management, the power of both OT and IT can be unleashed to capture opportunity as it happens; that is, to find the needle before it even goes into the haystack.
Sumeet Shendrikar is principal applications engineer, services at RTI. He serves as a technology consultant building distributed real-time systems. His technical expertise spans large-scale real-time distributed systems, high performance computing, networking, and embedded systems. Sumeet holds an MS in Computer Science from Stanford University, and a BS EECS from the University of California at Berkeley. Prior to RTI, Sumeet worked at Trilogy Software as a consultant for Global 1000 companies.
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