Digital Support for Maintenance Teams: Utilizing Machine Learning and IoT to Assist in Equipment Reliability, TAPPICon24

In response to workforce shortages, the pulp and paper industry is turning to data-driven solutions that enhance operational efficiency. One promising approach is the integration of machine learning and equipment health monitoring IoT devices, like vibration sensors, which can proactively alert maintenance and reliability teams to potential production-crippling failures. These innovative solutions continuously monitor and learn equipment behavior, detecting deviations and patterns that might be missed by humans.

However, implementing and extracting value from such solutions is not a straightforward process. Success requires the establishment of the right infrastructure, a corporate culture open to change, and a committed maintenance and reliability team willing to train machine learning models. This white paper explores the substantial value that equipment health monitoring devices, driven by machine learning, can bring to paper mills. It also provides practical strategies and considerations to ensure the successful implementation of these solutions, addressing the essential requirements for improved operational outcomes.

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