The last few years saw an exponential interest and applications in AI and Machine Learning. While in the beginning a lot of this was marketing hype the dust has now settled, and the benefits of AI and ML are now accessible and tangible. While a good portion of AI is focused on image recognition, voice and text analysis AI is being applied increasingly in analyzing time series data and complex datasets.
Data science is continuously advancing and providing us with more and better insights into our data. The currently available algorithms can be used to:
- Forecast future trends based on past historical data. An algorithm may include external data such as weather or seasonal production data. To forecast future trends neural networks can be used in addition to the more traditional yet powerful linear regression algorithms. Forecasting energy and water requirements are very typical applications that widely used.
- Correlate complex data. A typical industrial application can have hundreds to thousands of variables that are collected at regular intervals. An AI system can identify a relationship between two variables that at first glance would seem unrelated and as most industrial systems and processes are connected this can be an invaluable tool for operators in designing as well as in troubleshooting processes. This can be applied to identify preventative maintenance opportunities fir example, or to troubleshoot complex process issues.
- Detect anomalies. Industrial systems have traditionally used alarms and alarm levels to alert on abnormal or error conditions. This requires that each value in the system must have lower and upper limits programmed. While this is doable for most legacy systems, newer systems with hundreds or thousands more sensors become increasingly difficult to program and update. An AI system can detect abnormal behavior with ease and the need to program each individual sensor is no longer required.
Of course, applications of artificial intelligence in industry and in operational technology contexts are not limited to data analysis. Computer vision for example can be leveraged in a multitude of applications such as detecting hazards and voice recognition can be used to simplify human to machine interfaces.
While challenges in applying AI to industrial processes are not to be underestimated (security for example) the potential of AI systems to positively contribute in industrial settings is undeniable and is attainable now.