Condition Based Monitoring,MFM and Asset management,TroubleshootBot,Crankshaft In Line Gauge History Analysis & Grinding Wheel Life Optimization

•Sensor Driven Condition Based Maintenance for Predictive Maintenance: Developed Statistical and ML Models for CBM to improve equipment uptime using machine signals and real-time streaming IIoT sensor data to reduce catastrophic failures leading to significant losses in production and high repair costs. •Anamoly Detection: Used Statistical and Multivariate ML models to predict abnormalities in manufacturing equipments through RMS values of sensor signals from conveyor motors. •Scalable & Re-usable Model: Utilized capabilities of IIOT/GCP framework and analytics architecture to deploy and scale/replicate CBM.•Model Evaluation: Integrated Maximo data source and developed confusion matrix for unsupervised ML models to reduce False Positives.•Successful Implementation: Rolled out solutions at Kentucky Truck Plant, Hermosillo Assembly Plant and Cuautitlán Assembly Plant delivering Ford F-150, Bronco, Mustang Mach-e etc; enabling costsavings of up to $15M annually.

• Subject Matter Expert: Demonstrated as an SME for Python, Data Analysis and Machine Learning for aspiring Data Scientists.•Data-driven Content Creation: Analyzed student data and curated new projects on data science based on student performance and preferences.