BHGE and C3.ai today announced the launch of BHC3 Reliability, the first artificial intelligence software application developed by the BakerHughesC3.ai joint venture.
Unveiled at BHGE’s annual digital conference, UNIFY2019, the now generally available application uses deep learning predictive models, natural language processing, and machine vision to continuously aggregate data from plant-wide sensor networks, enterprise systems, maintenance notes, and piping and instrumentation schematics. Using historical and real-time data from entire systems, the BHC3 Reliability machine learning models identify anomalous conditions that lead to equipment failure and process upsets. Application alerts enable proactive action by operators to reduce downtime and lost revenue.
Applicable to operations across all sectors of the energy value chain, BHC3 Reliability’s system-of-systems approach scales to any number of assets and processes across offshore and onshore platforms, compressor stations, refineries, and petrochemical plants, reducing downtime and increasing productivity.
The AI-enabled BHC3 Reliability application, powered by the BHC3 AI Suite, draws on BHGE’s domain expertise by augmenting application alerts with failure prevention recommendations and prescriptive actions.
“This application is a demonstration of how the BakerHughesC3.ai team is moving with speed to address the need for AI applications that deliver increased productivity, efficiency, and safety for oil and gas businesses,” said Derek Mathieson, chief marketing and technology officer, BHGE. “BHC3 Reliability delivers the system-wide insights from data that are only possible with the use of leading AI and machine learning technology.”
“The rapid release of BHC3 Reliability soon after the BHGE and C3.ai joint venture agreement sends a clear signal that BakerHughesC3.ai is a transformative force for the oil and gas industry,” said Ed Abbo, president and CTO, C3.ai. “Through our work together, we are uniquely positioned to deliver significant value to oil and gas companies by quickly deploying domain-specific advanced AI applications for diverse use cases across the energy value chain.”
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.
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