DATA ENGINEERING for PREDICTIVE MAINTENANCE

The Machine Instrumentation Group was originally founded to help machine makers (OEMs) develop their own Condition Monitoring instrumentation as part of a Predictive Maintenance program.

A necessary core competency for this purpose is the application of data engineering (AI or Machine Learning) approaches to the development of Health Indicators (HI’s) specifically for the machine. We partner with the machine OEM Subject Matter Experts to expand the scope of emerging anomalies detected and diagnosed.

Unlike general purpose data analytics, we come from a Predictive Maintenance background, focused on machine health as our contribution to the digital factory.

Sample projects include -

  • Transportation

    • Anomaly detection for marine diesel engines

    • Anomaly detection for new vehicles

    • Automated health monitoring and diagnosis of vehicle problems using acoustic signals

    • Relating steering settings to warranty claims

    • Railway brake system health monitoring

    • Advanced analytics for vehicle test data

  • Industrial Equipment

    • Anomaly detection for agriculture vehicles

    • Operating regime recognition and classification for mining excavators

    • Anomaly detection for construction and mining vehicles

    • Health monitoring of reciprocating pumps for oil & gas

  • Manufacturing

    • predictive health maintenance of stamping press machines

    • predictive maintenance of machine tools

    • Predictive maintenance software for factory automation and robotics

    • Early detection of chatter in a rolling process

    • Rolling element bearing health monitoring in steel manufacturing

    • Prediction of stepper output angle (virtual metrology) in semiconductor manufacturing

    • Control setting optimization for increased yield in semiconductor manufacturing

    • Predictive maintenance for critical semiconductor manufacturing equipment

  • MIL / AERO

    • Fleet based fault detection using a clustering approach (NSF SBIR Phase 1)

    • AI-based Continuous Learning and Humanin-the-Loop Solution for Improved Failure Diagnosis and Predictive Maintenance of Aircraft to Impact Mission Success (Navy SBIR Phase 1 / Phase 2)

  • Predictive quality solutions

    • Process optimization via predictive part quality for glass manufacturing

    • Manufacturing Process Settings Optimization using Process Settings and Warranty Claims (automotive)

    • Early Detection of Quality Issues to Reduce Wasted Production Time (automotive parts casting)

    • Preventing faulty products through anomaly detection in casting process

  • Other applications

    • Health monitoring system for CT scan electro-mechanical system

    • Advanced analytics for multi-function printers

    • Advanced analytics for dispensing machines