How AI/ML Can Improve Manufacturing Operations Posted on : Jul 05 - 2021

In this special guest feature, Stuart Gillen, Senior Manager at Kalypso, offers a few ways manufacturing organizations can leverage predictive maintenance to identify potential issues, reduce the occurrence and length of unplanned downtime, and get the most value from assets and budgets. Stuart is a proven leader passionate about AI and able to successfully work through the hype to provide clients actual implementation in IoT and Machine Learning projects which provide true business value and positive ROI. His areas of specialty include IoT architectures, platforms, and technologies. With testimonial success applying leading innovation capabilities, Stuart has a unique perspective on how clients can enhance their creative aptitude and maximize their return on innovation investments..

As manufacturers become increasingly connected, their systems, machines, sensors and other devices are generating a wealth of new data, and given the sheer volume of data generated, that isn’t easily analyzed. It is a challenge that traditional manufacturing systems are not designed for – and manufacturers are missing out on valuable insights as a result.

Machine learning (ML) and Artificial Intelligence (AI) technology can help, when implemented in support of an IoT strategy and validated through a strategic experiment that proves the potential value. Manufacturers should take a comprehensive approach to machine learning and analytics, integrating equipment, systems and people into a highly collaborative environment that rapidly adapts to changing operational requirements and operates on a scale much larger than simple IoT applications.

Here are a few ways manufacturing organizations can leverage predictive maintenance to identify potential issues, reduce the occurrence and length of unplanned downtime, and get the most value from assets and budgets.

Integrate with IIoT platforms to monitor machine health and performance

Enterprises can integrate predictive maintenance models into their manufacturing systems to actively monitor asset health and send alerts at optimal maintenance periods. For example, a worker installs sensors on machines and connects them to an IIoT platform. The sensors send machinery health data to the IIoT platform in real time and observe patterns of operation. The IIoT platform remotely monitors the health of the machinery – monitoring for anomalies or deviations. When conditions exceed machine learned thresholds, plant personnel are notified automatically through email/SMS. This allows organizations to react quickly to otherwise unknown events thus improving overall operations. And by understanding the health of the machines, asset owners can act on issues before they become critical. View More