Optimize asset health and predict failures while extending the useful life of your assets through a strategy that prioritizes repairs and replacements. Conditional-based predictive maintenance based on health insights from operational data and analytics helps you put your asset data to work.
IBM Maximo Health helps you understand the status of critical equipment and assets with insights from data and analytics to make smarter decisions about management and maintenance. Additionally, IBM Maximo Predict unifies disparate operational data into analytics-driven predictive maintenance models that help you optimize maintenance planning to improve asset reliability. In this short demo, see how a reliability engineer uses IBM Maximo Application suite’s prebuilt health scoring methodologies and customizable dashboards to make data-driven maintenance decisions to improve the stability of their energy grid.
Explore IBM Maximo to learn how IoT data, analytics and AI can help streamline your asset operations.
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Manage the health of your assets by using IoT data from asset sensors and other sources, such as the weather, asset records and work history, to increase asset availability and improve replacement planning. Now part of the integrated IBM Maximo Application Suite, Maximo Health enables a consolidated, global view. With insight into asset health, you can increase asset availability and find which assets require attention.
Key capabilities include:
- Single, intuitive dashboard with fleetwide view and health drilldown
- Sensor data integration
- Health-based notifications and actions
- Flexible health scores for assets by type or group
- Reduce the cost and frequency of asset failures: Monitor asset conditions with registered IoT devices that send sensor data and trigger automated actions that help reduce costly failures.
- Increase asset availability: Keep track of conditions, costs, performance and remaining useful life to reduce asset failures and minimize downtime.
- Optimize preventive maintenance: Combine asset records and sensor data to take action based on insight into asset health instead of doing unnecessary preventive maintenance.
- Reduce operational risk: A deeper view of asset status helps you focus on the right assets.
Reduce time to make capital replacement decision: Make replacement planning more accurate and efficient with powerful analytics that provide a clear view of your assets.
Use condition-based action to predict the likelihood of future failures by applying machine learning and data analytics to reduce asset failures and their costs. Now part of the integrated IBM Maximo Application Suite, Maximo Predict looks for patterns in asset data, usage and the environment, and correlates those patterns with any known issues to help reliability engineers and maintenance managers predict failures and share data and scoring.
Key capabilities include:
- Use data from IoT sensors, operations, IT and EAM systems
- Includes five popular predictive model templates and associated visualization
- Includes a comprehensive library of analytics APIs to build custom models
- Scores predictive models using IBM Watson® Machine Learning
- Model scores are easily integrated with IBM Maximo Health for improved condition monitoring
- Reduce unplanned downtime and risks: Predict asset failure and initiate action to avoid breakdowns.
- Reduce maintenance costs: Avoid over-maintaining assets by enabling scheduled maintenance before failure based on early warnings.
- Improve asset utilization: Enable more efficient use of existing assets when you can predict maintenance issues and reduce failures on the job.
- Extend asset life: Identify operational performance factors and improve maintenance practices and reliability.
- Increase production output: Make industrial manufacturing, product processes and products more efficient and dependable to deliver higher output.