Organizations are spending more money than ever to collect and store data. While collecting data from equipment and processes is nothing new, what has changed is the accessibility of data. Because the Internet is everywhere, more plants are connected than ever before. Plants that used to be considered too remote to manage are now participating in organizational asset monitoring and are constantly collecting data on the status and condition of their equipment.
What’s more, sensing technology has come down in price dramatically. It is now no longer cost prohibitive to put sensors on devices that in the past would have been left unmonitored. Coupled with the surge in cloud computing and a steep drop in data storage costs, this increase in sensor installation means that operators are expected to both collect and manage more data than ever before.
Technology advances come with new challenges, as these same operators have less and less time to evaluate that data. Also, effective data analysis requires a specific and defined skill set, and many process engineers are retiring without a surge of new analysts to replace them.
With a shortage of available analysts and the constant need to do more with less, many organizations are looking at options for managing machinery health and condition remotely. Whether an organization relies on its own in-house experts who are situated at a headquarters location a great distance from the plant, or looks to an external organization to monitor its resources and keep them apprised of impending incidents, there are many options that don’t involve keeping expensive, hard-to-find analysts and engineers on site.
The risks of languishing data
If collected machine health data languishes in storage without being analyzed, an organization is missing a key step toward improving operations, quality, safety, and environmental performance. Not analyzing machine health data means potentially missing indicators of imminent upset or process events.
In the case of an organization that is doing no analysis whatsoever, the outcome of missing these important trends can be catastrophic. In worst-case scenarios, essential equipment can run to failure because of a missed issue, resulting in major loss to equipment and product as well as the possibility of a disaster that creates damage and safety risk at the plant.
Even in a situation where process failure is not catastrophic, the upset to the organization’s workflow can mean the inability to meet essential commitments, resulting in fines and lost business. While the risk of equipment repair costs is great, it often pales in comparison to the business costs of the downtime associated with a failure.
But while plant management knows the risks of running an organization without proper machinery health analysis, finding the resources to accomplish the task often seems out of reach. Beyond the cost of hiring qualified engineers, it can easily take six to seven years to get newly hired engineers trained to a level where they can function on their own.
Even if a plant has qualified engineers on staff who are trained and ready to analyze machinery health data, these engineers often do not have the time to sit in an office poring over data readouts. High-level, experienced engineers are going to be out in the field, solving complex problems. While they are doing that, the engineers are not in the office looking at data and considering the problems that may be on the horizon.
Moreover, some organizations have essential equipment in hard to reach locations, or locations that are difficult to live in. In these situations, the process of hiring experienced engineers becomes even more difficult. Even if management is willing to pay a premium for engineers who will work in undesirable locations, often the engineers themselves are unwilling to accept these positions, leaving these sites seriously underserved.
The benefits of remote monitoring and analysis
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.