Machine Health Monitoring and Condition Based Maintenance through Industrial big data and Deep Learning

Manufacturing system reliability is significant in modern industry that requires high speed and low cost in production. The proposed work is aiming to establish a systematic and applicable approach for machine condition monitoring and condition based maintenance with the help of large amount of data from the industrial internet of things. Deep learning, as an emerging technics successfully applied in many areas will be utilized to automatically extract the knowledge behind the massive industrial data for more accurate machine fault diagnosis and prognosis. With the developed system, a more accurate and appropriate maintenance strategy can be carried out to ensuring the system reliability and low maintenance cost.

Faculty Supervisor:

Clarence de Silva

Student:

Partner:

Istuary Innovation Labs Inc (Vancouver, BC)

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program: