What Is Advanced Pattern Recognition 

Modern plants have access to a large amount of process and equipment data. The existence of the data is not the end of the story though. Organizations need to convert that data into actionable insights that help improve operations, reduce costs, and generate new opportunities. That’s where Advanced Pattern Recognition (APR) comes into operations. APR is a process in cognitive science and artificial intelligence that enables computers to recognize and predict patterns in data. APR and machine learning (ML) algorithms are effective methods for identifying potential equipment failures at the equipment, site, and fleet levels. 

To read more about APR and the value it brings to predictive maintenance strategies, read our blog post: The Power of Advanced Pattern Recognition. 

Why APR Solutions Matter 

APR software detects hidden trends, early warning signs, and subtle anomalies in equipment or process behavior long before setpoint alarms are triggered or failures occur. It analyzes historical and real-time data to uncover what we can’t easily see with our own eyes: gradual degradation, inconsistent performance, or abnormal conditions that signal an imminent problem. 

With the right tools, teams can: 

  • Detect issues earlier, even days or weeks before failure 
  • Reduce unplanned downtime 
  • Maximize equipment life and performance 

So here are five clear warning signs that your plant might need pattern recognition now. If you’re experiencing these problems and are tired of unexpected failures, it’s time to stop reacting and start predicting. 

The Maintenance Warning Signs

Each of the following signs is more than just a nuisance; they are red flags that traditional monitoring and fixed maintenance schedules aren’t enough to keep your operations running smoothly. Here’s what to watch for:

The Problem: You’re still reacting to unexpected breakdowns. 

Why It Matters: Surprise failures mean costly downtime, emergency repairs, and safety risks. 

How Pattern Recognition Helps: It flags subtle changes in equipment behavior long before a threshold is crossed or an alarm sounds. Think of it as your equipment raising its hand to tell you it’s not feeling well before it gets sick and must rest. 

Consider this: Most equipment failures have symptoms that go unnoticed. A traditional monitoring system typically misses these symptoms. 

Ask yourself: How many hours of unplanned downtime have you experienced this quarter?

The Problem: Your historian is full of data, but you still miss changes or new trends. 

Why It Matters: Data overload leads to missed signals, delayed responses, and ineffective analysis all around the plant. 

How Pattern Recognition Helps: It continuously scans critical data points and highlights only what matters, saving you time, energy, and resources. 

Imagine this: You’re able to spot a pressure anomaly 48 hours before an alarm goes off. During this time, on-site operators and your subject matter experts can collaborate to identify the root cause. 

Ask yourself: What failures would you have avoided if you had even a few more hours prior to a functional failure?

The Problem: You’re only doing preventive maintenance on a set calendar, regardless of the equipment’s actual condition. 

Why It Matters: This does not prioritize time and resources on critical equipment or early failure modes. 

How Pattern Recognition Helps: It identifies signs of degradation, so you can perform maintenance when needed rather than wait until the calendar says so. 

Consider this: Most failures, unfortunately, are not time-based and can happen randomly. To overcome these random risks, predictive maintenance provides an additional layer of security to your maintenance strategy. 

Ask yourself: How many failures occurred this quarter that preceded the OEM-scheduled maintenance recommendations? 

The Problem: An asset fails in a way that could have easily been avoided during routine maintenance or overhauls. 

Why It Matters: Some failure patterns develop over time and can be monitored closely to ensure they don’t move down the P-F failure curve quickly. 

How Pattern Recognition Helps: It monitors 24/7 and detects subtle behavior shifts that humans can’t see. 

Avoid this: A bearing fails after a scheduled overhaul. What would have been a simple replacement during the overhaul has resulted in unnecessary rework and lost time. 

Ask yourself: Do you have supplemental items to address during an overhaul? 

The Problem: It’s hard to connect equipment issues to real business impact. 

Why It Matters: Without clear data, it’s tough to build a case for investment or prioritize action. 

How Pattern Recognition Helps: It links anomalies to KPIs such as downtime hours, energy waste, and safety risks, so you can measure the cost of doing nothing. 

Consider this: a power generation organization estimated saving over $4 million in operational and maintenance costs over a 2-year period after implementing an APR solution. 

Ask yourself: How much budget could you redirect to growth initiatives if you reduced maintenance costs?

Ready to Catch Failures Before They Happen?

Recognizing these signs is the first step. The next step is understanding where APR fits into your existing systems and how to start small with quick wins. If you’re interested and want to know how to get started, we’re here to help you along with your digital transformation journey! 

We’ve worked with industrial organizations around the globe to identify equipment deviations in advance. Reach out to get our Quick Self-Review Worksheet with tips on getting started, or schedule a demo or meeting with us to find out more.