Operators of critical machines and systems have no time for downtime. Connected mechanical systems equipped with industrial internet of things (IIoT) sensors will constantly stream data about their condition in the hopes that someone will detect clues about their future state. Processing the massive volume of data and converting it into timely measures to extinguish threats is supported by condition monitoring and analytics solutions.
The best solutions employ advanced risk and fault detection strategies that expedite analytics, diagnostics, and decision making. The Tignis solution contains four automated mechanisms for detecting risks and faults, each of which requires a different set of available information:
Powered by ML, anomaly detection involves taking historical sensor data, picking a measurable outcome that matters (usually one sensor that measures a particularly important physical property), and then training an ML model to predict that property based on the measurements of other correlated properties across the system.
Anomaly detection can be applied to any digital twin with sensor data, even when Tignis data scientists personally have no idea how the system or machine works before seeing it. This minimizes barriers to value.
Engineering principles require encoded engineering/physics rules for at least one of the components in the system being modeled. Fortunately, many basic components are common everywhere, including pumps, fans, tanks, compressors, and more.
Tignis works with each new customer to add new rules for the processes and assets they really care about, whether they are common or not. The efforts are facilitated by the increasingly large library of encoded engineering knowledge we are amassing.
Supervised ML requires well-documented and annotated digital records of past failures along with associated sensor data. That data often does not exist but is incredibly effective when it does.
High-fidelity simulators are not widely used in industry at the moment. They are most often reserved for the most risky or complicated plants or processes, such as nuclear plants or some hydrocarbon plants, because they can cost millions to build and will only apply to a single plant. That will change as Tignis has developed techniques and tools that make simulation more accessible. Tignis has world-class experts in chemical engineering, mechanical engineering, and physics who can build simulations for high-value processes. For example, Tignis recently built a simulator for the physics of one process step within semiconductor processing.
Threats will become a reality if they remain unseen. Using an advanced condition monitoring and analytics solution with a multifaceted blend of automated threat detection mechanisms helps to expose equipment risks, enable predictive maintenance and reliability optimization measures, and keep the operation running as expected.