asset condition information

TRC-2018 Learning Zone 45:40
by Rudy Wodrich, IRISS

Online Monitoring and the use of IoT devices to facilitate easier data collection and collation can also be referred to as Critical Asset Surveillance Technologies (CAST). Traditional online monitoring techniques including Power Quality and Partial Discharge (PD) monitoring will be briefly examined. For industrial and commercial buildings, temperature monitoring may hold the most promise as a surveillance technique to catch problems early in the P-F curve. There are many options to consider when considering the implementation of online temperature monitoring. Keep in mind that the goal of temperature monitoring is to DETECT a potential, ANALYZE the data and determine criticality of further investigation, INSPECT the equipment in question to pinpoint the problem and finally to REPAIR the problem. With this in mind, we will weigh the technology options to be considered when choosing a temperature monitoring solution including:

• Contact (point based) or non-contact (area based) monitoring
• Wired or Wireless Communication
• Wired or Battery Powered
• CLOUD based or Firewalled Data
• Data Collection Frequency
• Stand-alone or part of Building Management System (BMS) or SCADA Platform
• Alarm Communication Methodology
• Reporting features / Data Bridging

TRC-2018 Learning Zone 41:11
by Scotty McLean, Bentley Systems

Today, maintenance professionals can work in a realistic 3D visualization environment, set up quickly with reality modeling software to create 3D models of your assets from photographs you take of your assets. Wouldn’t your job of maintenance be easier if you could see the assets in 3D to plan and understand the context within which the asset was designed, built, and operated? Now you can work in an immersive 3D environment for asset inspections and health monitoring, reliability and maintenance planning as well as work execution. Other benefits include simplifying training for young maintenance professionals and simplified and more understandable standard procedures. Attend this session and see how much better it will be when you can pinpoint locations and understand the geo-coordinates of the asset and know specifically where you need to inspect and/ or address an issue.

TRC-2018 Learning Zone 41:12
by Aaron Beazley, Bentley Systems

While machine learning has been researched for decades, its use in applying artificial intelligence in industrial plants and infrastructure asset operations is now advancing exponentially. This is due to the growth in big data and the expansion of the Internet of Things (IoT); the ability to provide the processing power needed to analyze larger data sets, and the need for superior predictive and prescriptive capabilities required to manage today’s complex assets as well as the availability of machine learning methods. While machine learning has typically been linked with industries such as transportation and banking (think self-driving cars and fraud monitoring respectively), there are many uses for machine learning within the industrial sector. This presentation will focus on some of the principles within machine learning, and how four such industries that are primed to take advantage of the application of machine learning can maximize the benefits it brings to improve situational intelligence, performance, and reliability.

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.