Performed real-time analysis of electrical data and made process adjustment recommendations to reduce or eliminate damageImproved and deployed a method for detecting and pinpointing subsea flowline leaksCoordinated with exception-based surveillance teams to deploy a variety of alarming scripts on various platforms to alert to abnormal equipment conditions.Leveraged signal processing techniques to provide statistical insight into physical damage and failure modes by correlating anomalies with equipment maintenance and failure reports.Developed an automated system for using sensor data to recognize and quantify physical motor damage due to unexpected backspin using advanced analytics and machine learning techniques.

Established methods for predicting rotating equipment failure through combined spectral and statistical analysis of electrical and process time-series data.Provided statistical support for data cleansing and alerting on abnormal conditions.Successfully designed and programmed methods for real-time adaptive trend approximation and subsea flowline leak detection.Designed pattern matching models using shape language techniques to detect anomalous values and shapes in data.

Effectively pursued specific value-adding analytics goals including tailoring and applying statistical and signal processing concepts to implement solutions to engineering and business problems.Investigated and developed methods for predictive analysis of Subsea Electrical Submersible Pump failures.