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