Download PDF

IIC Condition Monitoring & Predictive Maintenance Testbed

The current state of condition monitoring requires manual measurements that are compounded with aging equipment and the retirement of knowledgeable personnel.

  • National Instruments
    NI provides powerful, flexible technology solutions that accelerate productivity and drive rapid innovation. From daily tasks to grand challenges, NI helps engineers and scientists overcome complexity to exceed even their own expectations. Customers in nearly every industry—from healthcare and automotive to consumer electronics and particle physics—use NI’s integrated hardware and software platform to improve our world.
  • Equipment & Machinery
  • Maintenance
  • *This is an IIC testbed currently in progress.* SUPPORTING MEMBERS IBM, National Instruments, SparkCognition SOLUTION Provide a multi-vendor, cloud-based predictive maintenance solution that proves out new business models. The Condition Monitoring and Predictive Maintenance Testbed will offer continuous online measurements, automated analysis, and balance of plant coverage. TESTBED INTRODUCTION The Condition Monitoring and Predictive Maintenance Testbed (CM/PM) will demonstrate the value and benefits of continuously monitoring industrial equipment to detect early signs of performance degradation or failure. CM/PM will also use modern analytical technologies to allow organizations to not only detect problems but proactively recommend actions for operations and maintenance personnel to correct the problem. Condition Monitoring (CM) is the use of sensors in equipment to gather data and enable users to centrally monitor the data in real-time. Predictive Maintenance (PM) applies analytical models and rules against the data to proactively predict an impending issue; then deliver recommendations to operations, maintenance and IT departments to address the issue. These capabilities enable new ways to monitor the operation of the equipment - such as turbines and generators - and processes and to adopt proactive maintenance and repair procedures rather than fixed schedule-based procedures, potentially saving money on maintenance and repair, and saving cost and lost productivity of downtime caused by equipment failures. Furthermore, 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. Through this testbed, the testbed leaders IBM and National Instruments will explore the application of a variety of analytics technologies for condition monitoring and predictive maintenance. The testbed application will initially be deployed to a power plant facility where performance and progress will be reported on, additional energy equipment will be added and new models will be developed. It will then be expanded to adjacent, as yet to be determined, industries.

  • Fault Detection, Idle Time, Maintenance Requirements, Operation Performance, Production Efficiency
  • Emerging (technology has been on the market for > 2 years)
  • Impact #1

    Develop new predictive maintenance analytics modeling techniques

    Impact #2

    Document standard and secure architecture patterns and data formats for predictive maintenance in the Industrial Internet era.

    Impact #3
  • Machine Condition Monitoring
    Machine condition monitoring is the process of monitoring parameters such as vibration and temperature in order to identify changes that indicate a reduction in performance or impending fault. It is a necessary component of predictive maintenance solutions and allows maintenance to be scheduled prior to failure, or other actions to be taken to prevent damages to the machine and loss of production. Condition monitoring also provides value beyond improving maintenance schedules. For example, improved visibility into machine operations can indicate the root causes of product defects and can support optimization of energy consumption.
© 2020 IoT ONE