IMC-2017 Learning Session - 32:30
by Alfred Yu, Sanofi Pasteur, and Blair Fraser, Lakeside Process Controls

The impacts of unexpected downtime causing poor reliability is common and usually accepted in the pharmaceutical industry mainly due to the complexity, application specific design of production equipment and rigorous qualification steps involved for changes. This lead traditional condition monitoring technologies such as vibration and ultrasound to be ineffective when applied in silos. Also batch process with multiple phases in each cycle makes conventional condition monitoring almost impossibility to implement. As a result, it is common in this industry to rely overhauls, overtime and the overstocking of parts to reduce the impact of downtime.

The Reliability team at Sanofi Pasteur in Toronto, Ontario is challenging the status quo by thinking outside the box to develop solutions to increase the reliability of their critical assets by applying the new cycle-based condition monitoring methodology that can be used in various batch production processes. This new method will then be able to connect with Industrial IOT (Internet of Things), Big Data combined with Machine Learning (Pattern Recognition and Anomaly detection) technologies to provide predictive analytics.

This presentation will demonstrate, using an ongoing, real implementation how most of the misconceptions and challenges in batch process Condition Monitoring can be achieved and a path to IoCM (Internet of Condition Monitoring) can be established.