Predictive Maintenance

What is Predictive Maintenance?

Predictive Maintenance is the application of data analytics, including AI machine learning, to predict when an asset will fail or otherwise deteriorate, so that it can be serviced or replaced before failing. The benefits include lower costs, reduced unplanned downtime, increased safety, higher rates of asset utilization, and extended asset life. Enterprise AI predictive maintenance software applications are widely used in many industries including manufacturing, aerospace, defense, energy, oil and gas, chemicals, and pharmaceuticals.

A machine learning model for AI-based predictive maintenance can be trained using historical data from a number of data sources, including telemetry from sensors embedded in the asset and sensors in associated systems and subsystems. Other relevant data sources may include service records, maintenance and operator logs, weather and location data, photographic and video images, and other operational data. The machine learning model (or models) apply sophisticated mathematical and statistical methods to calculate a probability score that an asset will fail within a specific time frame, allowing operators to take action in advance.


Why is Predictive Maintenance important?

Predictive maintenance is one of the highest value use cases for enterprise AI software, with a proven track record of delivering measurable economic, environmental, and human safety benefits. Modern AI machine learning techniques can be applied to a vast range of asset types and environments to create models that can predict failure with a high degree of precision, with low rates of false positives and false negatives. These models can be incorporated into applications to trigger automatic actions and alert operators to schedule required maintenance.

Some examples of AI-driven predictive maintenance include:

  • Oil & Gas: Shell deploys AI predictive maintenance software to monitor more than 500,000 valves across its global operations.
  • Aerospace & Defense: The US Department of Defense deploys AI predictive maintenance software to predict subsystem failures in multiple aircraft platforms
  • Industrial Manufacturing: Koch Industries deploys enterprise AI software to predict failure of paper manufacturing equipment
  • Electric Utilities: Enel deploys predictive maintenance software to monitor generation, transmission, and distribution assets throughout its grid


Predictive Maintenance with C3 AI® Predictive Maintenance Application and the C3 AI Suite provides both a pre-built SaaS predictive maintenance application, C3 AI Predictive Maintenance, as well as the C3 AI Suite, a complete, end-to-end platform for developing enterprise AI applications. The C3 AI Predictive Maintenance application provides a full array of pre-built functionality than can be rapidly configured and deployed to accelerate implementation and time to value. The application provides a closed-loop workflow enabling organizations to monitor asset health in real time, predict asset failure with a high degree of confidence and consistency using advance AI algorithms, visualize risk across asset portfolios, diagnose asset-level conditions, take preventive action ahead of failures, and track performance.

For organizations that want to build custom predictive maintenance applications, the C3 AI Suite provides all 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 Suite allows organizations to build AI applications with far less code, time, and effort than other approaches. also provides a proven methodology and best practices for developing AI applications, based on a decade of experience in working with some of the world ’s largest organizations on industrial-scale use cases.