Job
Description
Role Overview: As a Proficy Consultant, your main objective will be to lead the development of a data-driven early warning and analytics system aimed at detecting and mitigating bridging of scrap metal during the melting process in induction furnaces. This solution will utilize GE Proficy tools such as Operations Hub, Historian, and CSense to ingest, visualize, and analyze process and batch data. The ultimate goal is to enhance efficiency, reduce downtime, and implement the solution across multiple furnaces within the organization. Key Responsibilities: - Initiate a proof-of-concept on Induction Furnace #1 at the Johnstown plant, focusing on its high bridging frequency. - Configure GE Proficy Historian to collect time-series data including voltage, current, power, frequency, and ground faults. Integrate batch metadata from systems like M3, DASH, etc. - Align batch records with furnace data and apply sequencing logic using refractory liner heat counts for accurate chronological ordering. - Collect datasets from regular and sintering heats from Furnace #3, as well as known bridged heats for pattern recognition. Differentiate sintering heats based on thermal characteristics. - Segment melting cycles into phases and utilize electrical signal patterns and timing markers. - Perform data cleaning and preprocessing in GE CSense including noise filtering, outlier handling, and missing data interpolation. Apply preprocessing specific to different heat types. - Develop predictive machine learning models in GE CSense for regular heats and sintering heats to identify early warning signs of bridging. - Study electrical parameter trends across furnace types and chemistries, calibrate models for generalization, and define real-time bridging risk alerts. - Correlate bridging incidents with electrical anomalies, chemistry, timing irregularities, and operator behavior for root cause analysis. - Provide actionable recommendations for operational enhancements and develop visualization dashboards for real-time metrics, historical trends, alerts, batch chemistry, and process phases. - Document all data flows, model logic, assumptions, and procedures for scalability. Track and analyze energy usage by phase and batch type to identify inefficiencies. Qualification Required: - Proven experience with GE Proficy Historian, Operations Hub, and CSense. - Strong background in machine learning, data preprocessing, and signal analysis. - Understanding of induction furnace operations or similar industrial processes. - Expertise in data integration, visualization tools (Power BI preferred), and root cause analysis. - Familiarity with batch processing, manufacturing data systems (M3, DASH), and time-series data. Role Overview: As a Proficy Consultant, your main objective will be to lead the development of a data-driven early warning and analytics system aimed at detecting and mitigating bridging of scrap metal during the melting process in induction furnaces. This solution will utilize GE Proficy tools such as Operations Hub, Historian, and CSense to ingest, visualize, and analyze process and batch data. The ultimate goal is to enhance efficiency, reduce downtime, and implement the solution across multiple furnaces within the organization. Key Responsibilities: - Initiate a proof-of-concept on Induction Furnace #1 at the Johnstown plant, focusing on its high bridging frequency. - Configure GE Proficy Historian to collect time-series data including voltage, current, power, frequency, and ground faults. Integrate batch metadata from systems like M3, DASH, etc. - Align batch records with furnace data and apply sequencing logic using refractory liner heat counts for accurate chronological ordering. - Collect datasets from regular and sintering heats from Furnace #3, as well as known bridged heats for pattern recognition. Differentiate sintering heats based on thermal characteristics. - Segment melting cycles into phases and utilize electrical signal patterns and timing markers. - Perform data cleaning and preprocessing in GE CSense including noise filtering, outlier handling, and missing data interpolation. Apply preprocessing specific to different heat types. - Develop predictive machine learning models in GE CSense for regular heats and sintering heats to identify early warning signs of bridging. - Study electrical parameter trends across furnace types and chemistries, calibrate models for generalization, and define real-time bridging risk alerts. - Correlate bridging incidents with electrical anomalies, chemistry, timing irregularities, and operator behavior for root cause analysis. - Provide actionable recommendations for operational enhancements and develop visualization dashboards for real-time metrics, historical trends, alerts, batch chemistry, and process phases. - Document all data flows, model logic, assumptions, and procedures for scalability. Track and analyze energy usage by phase and batch type to identify inefficiencies. Qualif
