How Predictive Analytics Helps Improve Critical Equipment Reliability

In the pipeline industry, a significant portion of lost profit is due to poor equipment reliability and unplanned production losses. Despite investments in infrastructure, systems, and people, critical equipment reliability problems still persist.

Now there is available a tremendous potential to leverage Predictive Analytics to address them.

Predictive Analytics helps increase revenue, reduce maintenance costs, and improve safety through reduction of equipment failure. It is a proven technology for detecting and diagnosing emerging reliability problems far earlier than traditional methods.

This innovative technology allows you to migrate your maintenance strategy to a predictive, proactive strategy, where the morning water-cooler talk is about how to prevent an identified problem from happening—not how to fix one that already has occurred.

Predictive Analytics improves productivity of key engineering and maintenance personnel. It also reduces maintenance expenses by fixing problems while they’re still minor instead of having to make major repairs. Industry studies have documented cost reductions in the 3-5X range for catching problems early as opposed to catching a problem once there is significant damage to the equipment. Early warning of failures also allows you to better plan maintenance operations, resulting in reduced “windshield time,” a key contributor to safety in the geographically dispersed world of the pipeline technician.

SmartSignal leverages existing infrastructure investment.

Predictive Analytics complements and improves on traditional DCS, vibration, and other systems. These systems are not intended to provide early warning. Rather, they are deployed to prevent significant equipment damage or failure. Predictive-Analytic software leverages existing infrastructure and systems to provide analysis and earlier warning of emerging issues.

Traditional monitoring systems are set to detect and prevent catastrophic damage. Alert levels must be set relatively broadly in order to prevent false alarms due to a wide range of operating conditions. Once a potential failure is detected, the time an operator has before equipment functional failure is often very brief, thereby limiting the options available to correct the situation.

Yet even before potential failure symptoms begin to trigger trips and alarms, there are undetected minor events leading up to eventual failure. Post-mortem analysis of data preceding a catastrophic failure almost always shows early indicators that could have alerted the operator to impending failure. Often these early indicators are detectable weeks and months in advance, which is where Predictive Analytics comes to play. Predictive-Analytic technology dovetails with approaches such as Reliability Centered Maintenance (RCM) by analyzing vast amounts of data and focusing on finding early indicators that are precursors to eventual failure and are undetectable by traditional methods.

Advanced warning allows operators to take deliberate and strategic actions to prevent or mitigate failures, rather than reacting in an expedited mode to a crisis. Proactive maintenance strategies might entail changing equipment operating conditions or re-sequencing work backlog or potentially rescheduling technicians and materials. Early detection of the loss of efficiency allows refinements to ensure equipment is driving the most horsepower with minimal electricity use.