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Asset intensive industries are all around us. Any enterprise using machinery to create, enhance, or transport products requires rotating and stationary assets to operate at maximum capacity with the lowest operational costs. The integration of asset data, analytics, and visualization empowers organizations to improve and maintain the reliability and availability of physical assets. Condition monitoring and predictive maintenance forecasting work to provide maximum production capacity and the lowest operational costs by tracking the health and performance of assets and by advising us of future maintenance and adjustment needs.
This presentation underscores machinery and equipment data sources as the backbone for condition monitoring and predictive maintenance analytics. Using several case studies, we will explore levels of analytics from thresholds, to business KPIs, to advanced statistics, and the opportunity for machine learning.