The IBM cloud-based environment combines National Instruments' monitoring and data acquisition capabilities with IBM’s analytics tools to help organizations across industries better monitor complex automation devices, predict failures and help reduce maintenance costs.
According to Jamie Smith, NI’s Director of Embedded Systems, "the goal [of the testbed] is to try new approaches and new practices that will help the industry and to do it in a real collaborative way." And the collaboration between IBM and NI on this endeavor has been a great example, he adds.
Condition monitoring is the use of sensors in equipment to gather data and enable users to centrally monitor the data in real-time. Predictive maintenance applies analytical models and rules against the data to proactively predict an impending issue; it then delivers recommendations to operations, maintenance and IT departments to address the issue. The testbed collaboration uses both.
"[Condition monitoring and predictive maintenance] have existed, but they've always been very siloed and haven't previously been cloud-based," says Smith. "And if they were an online monitoring system, they were contained to one specific facility."
Taking the monitoring and predictive maintenance to the cloud means enabling new capabilities, such as new ways to monitor the operation of equipment and processes and the ability to adopt proactive maintenance and repair procedures rather than fixed schedule-based procedures. These new capabilities can potentially aid in saving money on maintenance and repairs and lost productivity of downtime caused by equipment failures.
"It brings greater visibility and integrates the system more richly so information can be better understood," says Chris O'Connor, IBM’s General Manager of IoT. "You are able to run system levels, analytics and more ... so you can run cheaper, faster, better — that's the unique part of [IBM and NI] coming together. We both have specialty areas that complement each other."
O'Conner explained that the world today operates on a schedule with mechanical devices, and you have to be able to rely on a system. Condition-based monitoring and predictive maintenance keep the system reliable and running. The system tells you when it needs to be updated, upgraded or maintained.
"It also helps to provide greater insight," says Smith." And the best insights will come from bringing together many different databases to improve overall operations." For example, combining sensor data from multiple pieces of equipment and/or multiple processes can provide deeper insight into the overall impact of faulty or sub-optimal equipment, allowing organizations to identify and resolve problems before they impact operations and improve the quality and efficiency of industrial processes.
The testbed application has initially been deployed to a power plant facility where performance and progress are reported. Based on results, additional energy equipment will be added and new models will be developed. It will also then be expanded to other, yet-to-be-determined industries.
Smith says the outcomes of the testbed so far spark a lot of questions about what's possible in the future. "It's making a cloud-based, predictive system a reality. It's groundbreaking work."
The testbed was first demonstrated earlier this month at the IIC Quarterly meeting and will also be showcased at NI Week 2015 in Austin, Texas, Aug. 3 to 6 and at the loT Solutions World Congress this September.
For additional information about the collaboration, visit iiconsortium.org.