Vibration Analysis: Fault detection, classification, prediction

Vibration analysis is probably the most widely used technique to perform health monitoring of mechanical machinery. Specifically, we are interested in monitoring ‘Vibrating screens’, machines that are for example used by the mining industry to sort aggregate by size. Over the last 10 years the research group of Dr. v. Mohrenschildt has developed hardware, software and theory to accomplish this. The goal is to further the understanding of feature extraction and classification to perform effective predictive maintenance. Several activities contribute to this overall initiative: Data Mining: perform feature extraction on the data sets we recorded data sets to obtain machine health information. These topics are very timely as recent successes in machine learning sparked significant interest in industry and academia. Develop methods to perform additional machine measurement: impact based modal analysis: determine the resonance frequencies of a machine and bearing analysis again with the goal to determine bearing health.

Faculty Supervisor:

Martin V Mohrenschildt

Student:

Hassan Elaghoury;Elizabeth Hofer

Partner:

Haver & Boecker

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McMaster University