October 2017, Vol. 244, No. 10
Features
Enhancing Infrastructure Maintenance with IoT
Across pipeline operations worldwide, the term “unplanned downtime” is akin to a dirty word. Between heavy repair expenses and hundreds of thousands in lost revenue, the logistics of correcting an unforeseen supply chain interruption carry major implications – both financially and in terms of operational efficiency. No matter the cause, the clock starts running immediately when production stalls – and the price tag swells every minute. Fortunately, companies can mitigate these risks with the right tools and insights.
The Internet of Things (IoT) is changing the way countless industrial businesses operate. And as production environments have grown increasingly digital, pipeline operators are gaining access to previously unavailable IoT opportunities – many of which can help reduce unplanned downtime. One such IoT opportunity that can improve efficiency, in addition to uptime, is advanced equipment monitoring and intelligence that facilitates smarter asset maintenance.
Complex midstream infrastructure relies on critical equipment such as pumps, filters, and compressors to function properly. Often distributed over a large area, these assets require regular upkeep to maintain peak performance and avoid a failure event. But even with proper maintenance at set intervals, as recommended by the manufacturer, reliable performance isn’t guaranteed.
The Problem with Fixed-Interval Maintenance
Anyone who has ever owned a car is familiar with fixed-interval service schedules. Manufacturers recommend oil changes at specific mileage or time intervals as a preventative measure. These time- or usage-based schedules rely on assumptions around pre-defined, one-size-fits-all “normal” operating conditions to estimate when a car will need service. The car’s actual use and operating conditions are not a factor.
While the intentions are good, the lack of insight into the true state of the car can lead to over- or under-maintenance, depending on a range of disparate factors such as how it is driven and ambient environmental conditions. In this case, the consequences are likely limited to more trivial nuisances like extra trips to the service station. But extreme scenarios, such as the risk of breaking down on the side of a remote road or shortening the life of the car, are also possible.
Scale out this approach to maintenance for a large crude oil or LNG pipeline and the stakes get significantly higher. Servicing equipment too frequently can stretch limited personnel and resources thin, waste time, and slow production with unnecessary planned downtime. Too infrequently, and a small problem could go unaddressed and turn into a big problem – triggering a surprise equipment failure and an emergency repair scenario. And when equipment failures can cost upward of $500,000 per day in lost production alone, playing a maintenance guessing game is just bad business.
For example, say the weather has been much warmer than normal at a remote boost station. This forces the compressor system to operate in temperatures well above specified thresholds for a sustained period of time. These adverse environmental conditions place additional stress on equipment components, accelerating wear and fatigue. As a result, the system engine’s water pump fails two weeks before the next scheduled service event. There will be a lag time between when the dispatch center finds out about the issue, and when they can get a compressor technician physically on site. And even then, the technician will have limited information on the root cause of the problem or any parts that may be necessary to complete the repair.
Insight into actual conditions could have prompted updates to the planned maintenance schedule and provided details on what’s wrong and how to correct the issue. Instead, you face an emergency ordeal that can cost three or four times more than regular maintenance. Meanwhile, that pipeline remains down for an extended period of time, wreaking havoc on production efficiency and hitting the bottom line hard.
Take a Condition-Based Approach
One important step to avoiding maintenance inefficiencies and unnecessary repair expenses is to move away from servicing equipment at set intervals that are not tied to actual usage and real-time status updates. Taking this step requires an IoT ecosystem that will provide the insights necessary to perform condition-based maintenance (CBM). With CBM, service schedules are tailored to specific pieces of equipment based on real-time telemetry data monitoring physical factors like run-time, performance, and environment. So in the car example, it would be possible to service the vehicle when the lubricating properties in the oil break down, instead of oil change guesstimates every 3,000 miles.
Given that equipment maintenance is one of the largest asset expenses that pipeline operators can control – in addition to the benefits of identifying failure potential early and avoiding the high costs of emergency repairs – achieving effective CBM should be a top priority across the industry. Unfortunately, many companies get stuck on the complex task of implementing the technology architecture necessary to successfully perform data-based maintenance. Others fail to fully utilize the data already collected to employ CBM, or struggle to manage and analyze increasing data volume to scale adoption. Fortunately, while the data analytics required to effectively execute CBM are extremely complicated, advances in IoT technology make CBM more accessible to pipeline operators than ever.
Effective CBM Through IoT
Achieving CBM in any industrial setting is incredibly difficult because the conditions involved are based on more variables than the human mind can effectively process. This is especially true with the amount of related equipment and systems associated with any pipeline infrastructure. Even the most experienced technician can only account for a limited number of variable factors, and only those directly affecting equipment performance.
So, in addition to major fault indicators that can fall through the cracks, tangential factors get completely ignored. While these minor details may only exert minimal influence on equipment performance on their own, taken as a whole, they can add up to significantly impact performance. In addition, manual analysis only reaches a certain level of detail. Again, the inability to differentiate between fractions of a degree isn’t a major issue in a vacuum, but scaled out over time these tiny data points can accumulate to influence a failure event in a significant way.
This is where the right IoT solution can provide immense value through data analytics. Specifically, operators can benefit exponentially from an IoT platform’s ability to collect operational data from a fully digitized pipeline infrastructure and apply machine learning to identify patterns and characteristics that indicate a maintenance need. And since equipment operations are not linear, you need an IoT system capable of modeling the current status of equipment performance and how it reached that point. That information, along with related factors like environmental conditions, allows it to recognize trends and diagnose when a failure event is likely to occur, and what parts will require repair.
With the increasing number of IoT solutions available, it’s also important for operators to take a strategic approach to technology adoption. For example, employing a “rip and replace” attitude to implementation can negate previous infrastructure investments and progress – which is often unnecessarily disruptive, time-consuming, and expensive. Instead, look for IoT systems that can adapt to your existing technology foundation for data collection.
Finally, remember that IoT adoption is not an “all or nothing” proposition. In fact, a piecemeal strategy is usually a smarter way to upgrade technology, instead of attempting to do it all at once. Focusing initial IoT upgrades on one or two important pieces of equipment is a great way to manage project scope, costs, and operational disruption. The result is a reasonable initial investment that allows operators to prove out the viability of IoT and justify additional investments with tangible ROI data.
Author: Prasantha Jayakody is a senior product manager for Bsquare and has over 20 years of experience in the software industry. He holds both a BA in Mathematics and a BS in Computer Science from the University of Pennsylvania.
Comments