BHC3 Reliability

Prevent process failure in filtration

BHC3 Reliability

Prevent process failure in compression

BHC3 Reliability

Prevent process failure in power generation

Take Early Action to Identify and Prevent Anomalies

BHC3 Reliability™ is a comprehensive software solution that provides reliability engineers, process engineers and maintenance managers with AI-enabled insights to address process and equipment performance risks. The application identifies anomalies, provides prioritized alerts to operators, recommends prescriptive actions, and enables collaboration across the enterprise. The application delivers value through increased revenue from recovered production, reduced costs of unplanned downtime, extended equipment life, and improved safety in operations.

Features

System-of-systems AI approach

Leverage AI to identify equipment and process issues that impact system-level health and operational performance. Understand how individual tags across independent systems are related to overall system health. Assess system and subsystem health trends over varying time intervals across configurable risk indicators.

Unsupervised anomaly detection

Leverage sophisticated deep learning and machine learning technology to identify anomalies in process flow and equipment performance. Respond to risks and anomalies in process flow and equipment performance, along with failure process upset predictions.

Root cause identification

Prescribe root cause remediation to guide reliability engineers to enable faster, more consistent, and traceable root cause investigations.

Continuous learning

Continuously train and improve AI models based on new data and user feedback. Increase the accuracy of failure mode recommendations and anomaly detection alerts over time.

Prioritized alerting

Focus operations on prioritized alerts and reduce the number of unnecessary alerts through AI-enabled detection and categorization of process risks. Investigate and take action using AI-recommended failure mode assessments for each identified risk.

Visualization across process equipment

View and traverse unified process data at the aggregate level or drill down to understand individual equipment performance. Aggregate process data to view all relevant data for interdependent process equipment.

Seamless integration with existing tools

Aggregate process data to view all relevant data for interdependent process equipment. Understand how tags from independent systems correlate to distinct process steps. Collaborate across the enterprise with case management tools, including data investigations, messaging, user tagging, file upload, and external messaging (e.g., email or text).

Benefits

Reduce

Reduce energy costs by 15-30% using predictive analytics and optimization.

Forecast

Forecast energy demand more accurately with tailored machine learning analytics that achieve greater than 80% accuracy.

Reduce

Reduced unplanned downtime by proactively addressing process and equipment reliability issues.

Increase

Increase CapEx investment ROI by optimizing investment in building and energy infrastructure (e.g., solar, smart lighting, storage, EVs).

Automate

Automate facility management with streaming analytics and AI-algorithms that predict loads to dynamically optimize building operations.

Optimize

Optimize operations and capital expenditures by proactively planning reliability improvement projects and minimizing unplanned downtime.

Track

Track, benchmark, and rank performance of regions, facilities, systems, and equipment based on configurable health and reliability metrics.

Data Sources

The application leverages the C3 AI Suite to integrate terabyte-scale data from disparate sources such as individual sensors, enterprise systems, and data historians. C3 Reliability uses unsupervised and supervised machine learning algorithms to predict system health of processes and process equipment. Processes monitored include off-shore oil platforms, downstream refineries, and connected field equipment.

BHC3 Reliability Data Sources

The application leverages the BHC3 AI Suite to integrate terabyte-scale data from disparate sources such as sensor networks, enterprise systems, and data historians. BHC3 Reliability uses unsupervised and supervised machine learning algorithms to predict system health of processes and process equipment. Processes monitored include off-shore oil platforms, downstream refineries, and connected field equipment.

Model-driven architecture for BHC3 Reliability

Demo

Proven results in weeks, not years

timeline
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Understand BHC3 AI Suite's capabilities, its model-driven architecture, and test it against your company's sample data set.
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