Data-Driven Smart Manufacturing

Chansu Yu, PhD | Biography
Electrical Engineering and Computer Science

Keywords: Data science; Smart manufacturing; Workforce training

Description
  • The factory of the future: Smart maintenance; Better product development; Quality improvement; Market adaptation
  • Upskill the workforce: Data visualization; Model building, fitting, and sensitivity analysis; Advanced sensors; Manufacturing process control; Machine vision; Advanced data analysis
 
Business Applications

Smart enterprise at small- and mid-scale

  • Smart technology strategies from modeling to manufacturing systems to advanced data analytics
  • Highly-connected, knowledge-enabled industrial enterprise
  • Enhanced productivity, sustainability, and economic performance

Other Applications

  • Big data storage and processing infrastructure (manufacturing data format, protocol, networking & computing)
  • Building APIs for data consumption ("data pipeline")
  • Building predictive algorithms using machine learning
  • Creating visualizations and dashboards to help interpret data
 
Case Study

Purpose:

  • Classify good/bad motors based on time-series sensor data

Data & Results

  • Dataset: motor vibrational noise sensor (Ford)
  • Data collection: sensor 1~500
  • Data size: 4921 (30MB)
  • Analysis algorithm: auto-sklearn and auto-keras (automated machine learning)
  • Result: Characterize normal and abnormal data
    Accuracy - 75% (1-hour run); 88% (8-hour run)

Observation:

  • Visual inspection of the time-series vibration data does not tell good/bad products (shown on the right; good at the top and bad at the bottom). But ML does.