Organizations such as energy and utilities are ramping up their use of the Internet of Things (IoT) when it comes to asset reliability. One of those important assets is your EAM, specifically, IBM Maximo. This is where Asset Performance Management (APM) comes in.
Having APM Energy means that you are taking charge of your APM journey! While the traditional demands for safety and reliability persist, industry disruptions underway mean that you must deliver new efficiencies to keep costs low and stay agile. And you have begun doing just that!
You recognize the importance of finding opportunities for operational improvements using your existing data. However, while you may be collecting huge volumes of data, most of that data is used within a narrow context, often providing only a portion of the required information needed to make decisions. Data is collected but not used, or not used to its max potential. This is often due to data silos across the organization, each with different systems and tools for analytics.
The true potential of data is rarely realized. At a time when utilities increasingly rely on accurate forecasts to operate efficiently, decisions based on all of the available information and analysis are critical to both stay head and transform. In order to maintain reliability, safety and efficiency, you must be listening to – and acting on – what your data is telling you.
What is Asset Performance Management (APM)?
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. APM includes the concepts of condition monitoring, predictive forecasting and reliability-centered maintenance (RCM).
IBM Maximo® APM – Predictive Maintenance Insights, part of the IBM Maximo Asset Performance Management (APM) suite, helps maintenance managers predict the likelihood of future failures and determine asset failure factors that could impact plant or business operations. It uses Watson™ to look for patterns in asset data, usage and the environment, and correlates with any known issues to help predict failures.
Key capabilities of Predictive Maintenance
- Uses data from IoT sensors, OT, EAM and ERP systems
- Includes five out-of-the-box, popular predictive model templates and associated visualizations
- Includes a comprehensive library of analytics APIs to build custom models
- Scores predictive models using Watson Machine Learning
- Model scores are easily integrated with Asset Health Insights for improved condition monitoring
Watch this demo to see how this solution helps you understand health, risk, criticality, and effective age of your energy & utility assets. (opens in new window)
-Information collected from IBM