Research Assistant @ Intelligent Sensing Lab
Highlights & Achievements
• Developed a predictive maintenance tool for manufacturing machines which can detect faults and its types based on the vibration signal a machine generates.
• Used 5 different Machine Learning Classifiers on the actual data set from a drill machine to achieve fault detection & prediction accuracies over 85%.
• Conducted vibration analysis of two different machines using accelerometers, NI DAQ devices LabVIEW & MATLAB. Eliminated system noise by analyzing a large amount of data for preventing tool & equipment failures by implementing DOE & SPC principles.
• Used 5 different Machine Learning Classifiers on the actual data set from a drill machine to achieve fault detection & prediction accuracies over 85%.
• Conducted vibration analysis of two different machines using accelerometers, NI DAQ devices LabVIEW & MATLAB. Eliminated system noise by analyzing a large amount of data for preventing tool & equipment failures by implementing DOE & SPC principles.