C3 Predictive Maintenance

Predict asset failures for aircraft systems

C3 Predictive Maintenance

Predict asset failures for generation assets

C3 Predictive Maintenance

Predict asset failures for oil extraction assets

C3 Predictive Maintenance

Predict asset failures for industrial equipment

C3 Predictive Maintenance

Predict asset failures for substation equipment

Prioritize and Optimize Asset Maintenance and Planning

C3 Predictive Maintenance™ provides maintenance planners and equipment operators with comprehensive insight into asset risk, enabling them to maintain higher levels of asset availability across their entire portfolio.

Features

system status

Proactively assess real-time asset health

Monitor asset health based on telemetry data and use machine learning models for failure predictions, anomaly scores, and maintenance expense projections.
Peak demand forecasting

Apply next-generation asset failure prediction algorithms

Assess probability and impact of equipment failure with a high degree of confidence and consistency, based on actual operating conditions and asset performance details.
High priority

Visualize risks across asset portfolios

Manage risk across asset portfolios by viewing risk scores within and across portfolios. View details of asset risk across critical business and operating dimensions.
diagnostics and projections

Enable asset-level diagnostics and projections

Learn from individual asset failures and improve accuracy of failure predictions for the entire asset portfolio. Diagnose the conditions affecting individual asset failures and translate findings to the full asset portfolio.
Measure

Track, benchmark, and rank performance

Rank individual assets based on probability and impact of failure using intuitive KPIs, including probability of failure (PoF), mean time to failure (MTTF), and risk score (combining PoF with economic impact of failure).
Alerts

Use comprehensive closed-loop workflow support

Enable operators to construct maintenance packages based on risk scores and use bi-directional integration with work order management systems to define and launch work orders directly from C3 Predictive Maintenance.
Coordinate

Coordinate with alerts and notification functionality

Facilitate effective coordination through configurable alerts and thresholds. Summarize and present alerts within the application and configure for delivery to designated contacts via SMS and/or email.

Testimonials

Dan Jeavons

Dan Jeavons

GM Data Science

"The combination of our data science expertise and the software development expertise that C3.ai brings is really powerful."

Fabio Veronese

Fabio Veronese

Head of Infrastructure & Networks Digital Hub

“With C3.ai, we embarked on a platform journey – a widespread adoption of the platform-based model for machine learning and artificial intelligence.”

Lt. Col. Dave Harden

Lt. Col. Dave Harden

COO and Architect, AFWERX

“The great thing in working with C3.ai has been the talent. A lot of times businesses come down to people, people, and people.”

Benefits

Identify

Identify high-risk assets before they fail, prioritize maintenance expenditures, and operationalize identified maintenance needs.

Analyze

Analyze equipment at any level of aggregation, from individual equipment units to groups or families of equipment to geographical aggregations of equipment.

Prioritize

Prioritize maintenance across equipment and directly initiate maintenance activities through existing work order management systems.

Minimize

Minimize downtime due to early identification and resolution of equipment at high risk of failure.

Reduce

Reduce operational costs by shifting reactive maintenance to predictive maintenance.

Streamline

Streamline workflows by defining maintenance packages that enable  planners to bundle high-priority work and schedule it optimally in the equipment operating cycle.

Optimize

Optimize capital expenditures by driving asset replacement decisions using asset risk scores.

Data Sources

C3 Predictive Maintenance aggregates petabyte-scale data from individual sensors, smart devices, enterprise systems (e.g., asset management, work management, outage management) and operational systems (e.g., SCADA, OMS, GIS) to generate accurate predictions of asset failure.

C3 Predictive Maintenance uses advanced machine learning algorithms to compute asset risk scores. The algorithms are trained using historical failure data and can be configured to estimate probability of failure over different operating horizons (e.g., 14 days, 30 days, or 6 months).

In addition to supervised machine learning techniques that require historical failure data to train algorithms, C3 Predictive Maintenance also includes unsupervised learning techniques to identify and predict anomalous operating states without the use of historical failures. C3 Predictive Maintenance provides closed-loop work order integration that enables continuous improvement of the underlying machine learning models.
 

C3 Model-driven architecture for C3 Predictive Maintenance

Demo

Proven results in weeks, not years

timeline
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