Predictive maintenance through data-driven decision making

Sammanfattning

This thesis investigates the deployment of Predictive Maintenance (PdM) systems andsolutions at Parker Hannifin Manufacturing, a producer of control valves. PdM uses advancedanalytics, machine learning algorithms, and real-time sensor data to predict maintenanceneeds before machine breakdowns occur. This approach contrasts with traditionalpreventive maintenance (PM), which follows predetermined schedules. The research aimsto identify critical parameters for equipment monitoring, the necessary data infrastructure,and the potential cost benefits of PdM implementation. The study involves a case study ofspool cell machines at Parker Hannifin Manufacturing, addressing challenges in data collection,algorithm development, and cultural shifts within the organization. The expectedoutcome is strategic roadmaps for transitioning from PM to PdM. The study concludesthat applying PdM can reduce maintenance costs and enhance operational efficiency