False Positive Rate


What is a false positive rate in process plants?

In process plants, the false positive rate refers to the rate at which a system or sensor incorrectly detects the presence of a particular condition or event, when that condition or event does not exist. It is a measure of the system’s or sensor’s propensity to generate false alarms or alerts.

Any industry that monitors conditions for anomalies or carries out scientific tests cares about the incidence of false positives, because this tells you how reliable the system is. Hospitals, cybersecurity teams, lab testing teams, process manufacturing plants, and more all keep track of this rate.

Plants also track their false negative rates, which shows how often monitoring equipment or testing systems fail to pick up on an abnormality or incident, and report that everything is fine when there’s actually a problem that engineering teams need to know about.


Why does the rate of false positives matter in process manufacturing?

In process manufacturing plants, equipment maintenance, production monitoring, and quality control monitoring are all extremely important. They ensure a safe environment, guarantee that product meets the standard required by customers and industry regulations, and help confirm that all the equipment is operating correctly.

All these processes require trustworthy results. Engineering and maintenance teams rely on alarms, alerts, and test results to direct them to issues that need fixing and machinery that needs repair or maintenance. If the false positive rate is high, it means that the results aren’t reliable, which undermines trust in the entire system.

False alarms and false alerts can lead to unnecessary downtime and resource allocation. For example, if a temperature monitoring system in a chemical plant frequently triggers false alarms about high temperatures, it can lead to unwarranted shutdowns that cause production losses and increase costs. Similarly, in quality control processes, a high false alarm rate in a sensor could result in the rejection and wastage of good products.

High alarm rates lead to frequent production disruptions, and unnecessary work for engineers and maintenance teams. Over time, stress can rise, burnout can increase, and “alert fatigue” or alert blindness can set in, where a team learns to ignore alerts because they assume they are false.


How can process plants change this rate?

It’s not easy to lower the false positive rate. Some plants end up turning off monitoring systems or reducing their sensitivity thresholds, but that could mean you miss vital alerts and end up compromising plant safety or production quality.

Artificial intelligence (AI) technologies offer a new and better option. Predictive monitoring and maintenance systems use AI and machine learning (ML) to learn how to distinguish a true alert or alarm from a false one. Precognize, for example, combines human domain knowledge with ML to map the plant, understand which sensors are the most important, and aggregate anomalies to create a clear definition of “normal” circumstances that allows them to better distinguish true anomalies.

ML alarm and alert systems are only as reliable as the data they are trained on. It’s vital to gather clean, accurate, and up to date data, so the system won’t make mistakes and produce its own false positives. Even the smartest ML monitoring systems can cause false positives if its trained on poor quality data.


How do process plants benefit from reducing false alert rates?

Lower false positive rates mean that maintenance teams and product engineers don’t waste their time and energy on alerts that turn out to be false, you won’t mistakenly discard high quality product, and production won’t be disrupted by unnecessary downtime. Instead, employees can spend more time on real issues to boost plant productivity. A lower false alarm rate means reduced expense, increased safety, and more productivity, ultimately strengthening your bottom line.