asset condition information

TRC-2018 Learning Zone 37:41
by Matthew Bollom, National Instruments

There are many large pieces of equipment which are critical to your business, and keeping these running in a reliable and predictable manner is key to minimizing downtime and operational costs, improving machinery reliability, and ultimately increasing revenue. Real-time asset monitoring tools can help operations and maintenance management to predict faults, and to respond appropriately to any fault that occurs. However, it requires massive amounts of data and the transition from manual route-based measurements to a fleet-wide surveillance program, which ultimately touches many elements from sensors to networked data acquisition nodes to servers to historians and predictive technologies. Lastly, an aging workforce reaching retirement in the next few years exponentially increases the need for effective and automatic knowledge transfer, management and training. How does the system help the maintenance and reliability engineer to be more effective and efficient? The system automatically predicts equipment failure and notifies a person or system so that pro-active steps can be taken to avoid equipment damage and unscheduled downtime. At this session, get a behind-the-scenes look at how one of the largest power generation companies in the USA, as well as, a leading pump manufacturer, is leveraging National Instruments open and flexible hardware platforms to integrate more advanced sensors into OSIsoft PI System as well PTC ThingWorx IIoT platform that goes beyond vibration measurements. Examples of these sensors, measurements that make existing assets smart include vibration, thermography, motor-current signature analysis, and electrical magnetic signature analysis.

TRC-2018 Learning Zone 46:50
by David Auton, C&W Services and Abhinav Khushraj, Petasense

After a successful pilot, C&W Services implemented a machine-learning based predictive maintenance system in order to address the limitations of a manual walk around program at one of its leading pharmaceutical client facility. Over the past year, by leveraging wireless sensors, secure cloud infrastructure and predictive analytics, C&W Services has been able to automate data collection, improve asset reliability, reduce equipment downtime, dramatically cut down on maintenance spending and achieve better resource allocation. Soon after deployment, the IIoT solution enabled the reliability engineers at C&W Services to detect a defect in an AHU, thereby preventing a catastrophic failure. The asset in question displayed an increase in the amplitude of fan shaft harmonics. Upon investigation, it was verified that the belts were running loose and the shafts were out of alignment. In another instance, the pillow block bearings were extremely noisy on the fan. There was a step increase in the acceleration spectrum, relatively small but noticeable that was returned to its normal vibration signature upon lubrication. With the continuous monitoring of critical rotating machinery, C&W Services has been able to achieve a competitive edge through a strong ROI along with other tangible benefits. Today it is inspiring others in the industrial world to make a transition towards IIoT.

TRC-2018 Learning Zone 20:46
by John Langskov, Palo Verde, Arizona Public Service

On-line monitoring can provide significant cost savings through improved reliability (such as production line shutdown avoidance), but additional savings can also be obtained through reduced PM tasks. The question is: do the savings in PM reduction balance the cost of adding continuous monitoring to sensors to your equipment? Palo Verde uses a parameter-based approach, that focuses on degradation mechanisms affecting a specific component type, and a sensors ability to monitor them continuously for degradation, periodic PM tasks can be eliminated, or extended based on changes in failure probability.

TRC-2018 Learning Zone 40:27
by Stuart Gillen, SparkCognition

Many organizations today are facing challenges in increasing reliability and uptime. Current industry solutions do not offer advanced notice for performing proactive, predictive maintenance. The use of intelligent edge devices to acquire asset sensory data, along with machine learning algorithms to predict when an asset will fail, is becoming more attractive to maintenance managers as they seek new methods to get maintenance costs under control. The use of this technology can augment or even supplement human subject matter experts while providing significant advanced notice of asset health issues by analyzing and learning from past asset health data. In this presentation, we will discuss practical ways in which utilities can get started today and see how others are implementing this technology.

TRC-2018 Learning Zone 42:20
by Randy Jones, Southern Company Generation

Everyone wants to achieve a connected plant and to leverage IoT, APR, predictive analytics, and other enhancements to improve business results. This presentation will discuss some practical steps to be considered and some basic foundational elements that will enable moving forward along this journey.