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Anomaly Detection in Highly Noisy Signals from Electrical Rotating Machines
Equipment failure is the primary source of unplanned downtime in industries working with rotating electrical machines. Fault detection at the early stages is an essential solution for reducing this downtime. Condition monitoring of machinery is the process of capturing and monitoring parameters such as vibrations to identify a developing fault. This project uses the data resulting from condition monitoring to develop anomaly detection algorithms for improving early-stage fault detection and diagnosis processes.
Faculty Supervisor:
Faramarz Samavati
Student:
Roghayeh Heidari
Partner:
AB Cognitive System Inc.
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
University of Calgary
Program:
Accelerate
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