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Power-generating assets (e.g., jet engines, gas turbines) are ofteninstrumented with tens to hundreds of sensors for monitoring physical andperformance degradation. Anomaly detection algorithms highlight deviations frompredetermined benchmarks with the goal of detecting incipient faults. We aredeveloping an integrated system to address three key challenges withinanalyzing sensor data from power-generating assets: (1) difficulty in ingestingand analyzing data from large numbers of machines; (2) prevalence of falsealarms generated by anomaly detection algorithms resulting in unnecessarydowntime and maintenance; and (3) lack of an integrated visualization thathelps users understand and explore the flagged anomalies and relevant sensorcontext in the energy domain. We present preliminary results and our keyfindings in addressing these challenges. Our system's scalable event ingestionframework, based on OpenTSDB, ingests nearly 400,000 sensor data samples perseconds using a 30 machine cluster. To reduce false alarm rates, we leveragethe False Discovery Rate (FDR) algorithm which significantly reduces the numberof false alarms. Our visualization tool presents the anomalies and associatedcontent flagged by the FDR algorithm to inform users and practitioners in theirdecision making process. We believe our integrated platform will help reducemaintenance costs significantly while increasing asset lifespan. We are workingto extend our system on multiple fronts, such as scaling to more data and morecompute nodes (70 in total).

Scalable Architecture for Anomaly Detection and Visualization in Power Generating Assets