In a world where remote work is the norm and data are the determining factor in decision making, utilizing IIoT technologies that enable machine condition monitoring and predictive maintenance is imperative. Enacting this type of paradigm shift, however, requires more than just installing sensors and collecting infinite amounts of data. You need to determine which assets to monitor, what technologies to use, and how to apply all the data being captured.
Here are four helpful tips from experts throughout the industry on how to better plan and utilize actionable sensor data and extend the lives of vital assets.
Practical aspects of IIoT deployment
The main purpose of a PdM system in most cases is to avoid unexpected equipment failure to minimize plant downtime. Therefore, the cost of a motor or pump or compressor that is being monitored is typically not as important as the function it performs in the manufacturing process. A cost per hour for each area of the operation or each process will be very helpful as well as an overall cost per hour for the entire facility. Those figures will be used in ROI calculations when creating your PdM strategy.
Simply deploying sensors and collecting vast amounts of data is not enough. Because of the ease of use of cloud-based platforms, it is very tempting to collect data as often as possible “just in case.” Managing and interpreting the large amounts of data collected can become a very difficult and costly task. It is therefore important to only collect relevant data to perform the required analysis. One may start with collecting data at a high rate and then once the system behavior is understood, reduce the rate to a level that is enough to perform the required analysis.
Pranesh Rao, Thomas Schardt, Nidec Motor Corp; and Justin Lesley, Motion Industries
Practical magic: Integrating IIoT into your PdM strategy
Remote monitoring and analysis offers a number of benefits over having traditional, on-site engineers performing machinery health data analysis. For organizations that are currently performing little or no machinery condition monitoring, looking to an outside source to provide analysis services can be a low-risk way to move from reactive to proactive maintenance. For organizations that are already performing a significant amount of analysis, using remote monitoring can mean bringing machinery health analysis to underserved sites or easily supplementing available resources during high production opportunities or peak volume seasons.
One of the most significant values of remote monitoring and analysis is that it doesn’t require organizations to make drastic changes to the way they already collect machine health data. Whether operators are collecting data manually with portable analyzers, or a plant is set up with a top-of-the-line continuous prediction and protection monitoring system, the collected data can be retrieved from off-site and analyzed.
Jacob Swafford, Emerson Process Management
How to maximize your resources using remote analysis
Are you in or out?
Remote monitoring can be managed in one of two ways: in-house or by a third party. When it’s kept in-house, remotely collected data can be fed into a central location where experts can collaborate with their colleagues around the world to analyze, diagnose, and troubleshoot problems. Alternatively, the company’s automated systems could filter through the data and return it to on-site workers in the form of contextualized information.
When managed by a third party such as an OEM or automation supplier, a dedicated team of specialists can monitor and respond to production issues. These services are typically scalable, meaning they can be applied to a single machine or across a global production operation. The service provider also may offer remote monitoring in combination with other services, such as recovery support and on-site support with guaranteed response times.
Phil Bush, Rockwell Automation
Perfect vision: Remote monitoring
Reduce complexity with edge computing
While data siloing has become the status quo for industrial applications, the consumer and enterprise sectors have experienced an explosion in the number of connected devices in recent years. The approach that IT experts have developed for designing networks in response to this change is called edge computing. This distributed architecture places lightweight computing resources at the local network level that help to process and transmit data at its source, improving local responsiveness and increasing the efficiency of data transfer to central computing resources and data consumers.
Applied to automation networks, edge computing places more connectivity and processing power on the plant floor, which can be used to facilitate integration so that moving data across the organization no longer requires a deep technology hierarchy. With embedded support for multiple OT and IT protocols, industrial edge computing devices can process data from sensors and transmitters on the plant floor, then send it directly to on-premises or cloud-based applications and databases without intermediary hardware or software.
Josh Eastburn, Opto 22
Overcome obstacles to remote monitoring