What is predictive maintenance?

Predictive maintenance is a maintenance strategy that uses a combination of data and analytics techniques to predict when an in-service machine requires service.

To implement a predictive maintenance strategy, investments in hardware and software to capture and persist data are required. The organization needs to maintain a quality stream of operational data, typically from sensors installed on the machine. Other sources of data, such as maintenance records, failure events, inspection records, and engineering diagrams, among others, enable a more complete predictive maintenance strategy with holistic monitoring. Analytical or machine learning techniques are then applied on top of the data to create predictive models.

Why is predictive maintenance important?

Predictive maintenance is an important operating strategy that helps organizations reduce risk, control costs, and improve productivity. Predictive maintenance is especially important for assets that have long replacement lead time, costly service interruptions, and high safety or productivity risks associated with over-maintenance.

With a predictive maintenance strategy, organizations can:

• Reduce downtime by predicting when equipment is likely to fail.
• Minimize unplanned interruptions to operations.
• Control costs by reducing unnecessary repairs and optimize availability of part replacements.
• Improve productivity by enabling teams to focus on critical risks and reducing interruptions.
• Improve productivity by guiding teams to prescriptive failure modes versus conducting lengthy root cause investigations.
• Avoid unnecessary outages associated with over-maintaining machines on time-based schedules.
• Enhance safety by preventing emergency repairs and catastrophic events.

Why use AI for predictive maintenance?

AI-powered predictive maintenance software brings together operational data and advanced machine learning techniques to help organizations accurately predict the risk of asset failures. Rooted in the concepts of condition-based maintenance, AI-powered predictive maintenance goes a step further by using AI to uncover patterns that cannot be easily uncovered with traditional condition-monitoring techniques and feedback loop to improve over time.

How Does C3 AI Enable AI-Powered Predictive Maintenance?

C3 AI provides a pre-built AI-powered predictive maintenance application, C3 AI Reliability, to help organizations implement a predictive maintenance strategy. The application unifies operational data from multiple sources, such as sensors, asset templates, maintenance records, and operating manuals, and applies advanced machine learning and generative AI techniques to identify equipment risks in advance and providing recommend actions to prevent unplanned downtime and emergency repairs.

Examples of customers using C3 AI Reliability to implement AI-powered predictive maintenance include:

• Oil & Gas: Shell deploys C3 AI Reliability application to monitor more than 14,000 critical pieces of equipment across its global operations.
• Aerospace & Defense: The US Department of Defense deploys C3 AI Reliability application to predict subsystem failures in multiple aircraft platforms.
• Industrial Manufacturing: Georgia-Pacific, subsidiary of Koch Industries deploys C3 AI Reliability application to predict failure of manufacturing equipment.

C3 AI Reliability is used to monitor tens of thousands of assets in operation globally across these customers and many more.

The application is built on the C3 AI Platform, an end-to-end platform for developing enterprise AI applications. The C3 AI Platform provides a scalable and secure approach to enterprise AI. It provides the tools and capabilities required to rapidly design, develop, and operate advanced, industrial-scale enterprise AI applications.

The unique model-driven architecture of the C3 AI Platform allows organizations to build AI applications with less code, time, and effort than other approaches. It includes an end-to-end architecture to integrate with existing enterprise software systems, ingest into domain object models, apply out-of-the-box and configurable AI algorithms, and enable end user interaction in intuitive, simple user interface applications. C3 AI also provides a proven methodology and best practices for customer developers to configure, extend, and develop proprietary AI applications. C3 AI shares this methodology in a center of excellence collaboration model, delivering proven results as demonstrated through a decade of experience in working with some of the world’s largest organizations on industrial-scale use cases.