By Chris Barnes, Senior Manager, Data Science & IoT Consulting, Kalypso: A Rockwell Automation Business
When it comes to connecting, collecting and analyzing data, we’ve seen macrotrends among many industries that include the democratization of machine learning (ML), increased accessibility of edge computing resources and the proliferation of connected devices or sensors.
Manufacturers no longer need an internal data science team. They also now have access to all kinds of information about their assets. However, many still don’t know how to make the most use of this data, including only using reactive maintenance operations instead of predictive.
According to a U.S. Department of Energy Operations and Maintenance report, instituting a proper preventive maintenance program can result in energy savings of as much as 12%.
Asset Performance Management & Predictive Maintenance
Asset Performance Management (APM) encompasses the capabilities of data capture, integration, visualization and analytics tied together for the explicit purpose of improving the reliability and availability of physical assets.
Predictive maintenance (PdM) is a component of an APM system that uses artificial intelligence (AI) and ML techniques to try and predict asset health issues before they arise.
This includes the components of:
- Condition-Based Monitoring.
- Operations Management.
- Reliability-Centered Maintenance (RCM).
PdM uses condition-based data from various sources to deliver real-time insights on asset performance. This type of solution is designed to improve the reliability of assets and maintenance strategies using a combination of first-principles analysis and data science.