AI means artificial intelligence . . .
A great example of AI in action is in scanning vast amounts Internet traffic in real time, helping identify potential cybersecurity threats that have never even been seen before – which allows us to take mitigating actions before a threat has taken hold. For humans, it would be simply impossible to scan through hundreds of thousands of internet logs to spot the precise pattern that could signal a cyberattack.
AI Role in manufacturing . . .
Part of AI’s appeal stems from its ability to determine the condition of in-service equipment, which allows it to estimate when maintenance should be performed on the machinery – predictive maintenance.
Though each industry faces its own unique set of maintenance challenges, one common challenge across industries is unscheduled downtime.
Reconsidering maintenance Strategy…
The goal is to fix the assets before their issues become acute
When an asset fails unexpectedly, your course of action is dictated by the asset.
But when you foresee an outage, you are in control of your operational strategy
Moving from reactive to predictive maintenance . . .
Instead of waiting for assets to breach predetermined thresholds, good predictive maintenance solution applies Automated Machine Learning to big data to uncover data anomalies that indicate an evolving failure.
One way to achieve this is via implementation of SKF Enlight AI. It is an industrial analytics solution based on Automated Machine Learning (AutoML). Self-learning algorithms continuously analyze Big Data captured from asset sensors to detect anomalies and provide O&M personnel with real-time alerts of evolving machine failure.
Industrial plants have hundreds and thousands of sensors continuously collecting assets’ data. With the goal of identifying irregular data patterns that indicate upcoming machine failure, Automated Machine Learning sifts through the massive amounts of data and chooses the optimal algorithms for analyzing the specific data stream.
Automated Machine Learning uses AI algorithms to choose the best Machine Learning models for analysing different datasets and is continuously evaluating its own performance to ensure the right models are always being used.
Access to SKF’s extensive, growing library of algorithms enables constant improvement of the AutoML performance. By shifting many repetitive Machine Learning tasks from data scientists to algorithms, the speed and accuracy of asset failure alerts are increased. Specific knowledge of which sensors detected anomalies aids O&M repair staff in establishing the root cause of machine failure more quickly and accelerates the remediation process.
Artificial Intelligence for Predictive Maintenance helps companies save money by tailoring maintenance routines to each piece of equipment’s needs, rather than having them conform to a set schedule.
AI will change the way industry looks at reliability. Making AI an integrated part of production is the future trend.