  Power-generating assets (e.g., jet engines, gas turbines) are often
instrumented with tens to hundreds of sensors for monitoring physical and
performance degradation. Anomaly detection algorithms highlight deviations from
predetermined benchmarks with the goal of detecting incipient faults. We are
developing an integrated system to address three key challenges within
analyzing sensor data from power-generating assets: (1) difficulty in ingesting
and analyzing data from large numbers of machines; (2) prevalence of false
alarms generated by anomaly detection algorithms resulting in unnecessary
downtime and maintenance; and (3) lack of an integrated visualization that
helps users understand and explore the flagged anomalies and relevant sensor
context in the energy domain. We present preliminary results and our key
findings in addressing these challenges. Our system's scalable event ingestion
framework, based on OpenTSDB, ingests nearly 400,000 sensor data samples per
seconds using a 30 machine cluster. To reduce false alarm rates, we leverage
the False Discovery Rate (FDR) algorithm which significantly reduces the number
of false alarms. Our visualization tool presents the anomalies and associated
content flagged by the FDR algorithm to inform users and practitioners in their
decision making process. We believe our integrated platform will help reduce
maintenance costs significantly while increasing asset lifespan. We are working
to extend our system on multiple fronts, such as scaling to more data and more
compute nodes (70 in total).
