Prateek Shrivastava
Prateek Shrivastava
Principal Data Scientist at Cummins
Track: Predictive Analytics
Enhanced Association Rules Mining in Predictive Maintenance
This talk discusses Cummins’ commitment to tackling engine failures in logistics. Cummins, a leading engine manufacturer, aims to leverage telematics technology to predict engine failures days in advance. The talk proposes using customized association rules mining techniques to analyze telematics data, establishing connections between diagnostic troubleshooting codes (DTCs) and various engine failures. The goal is to create a proactive predictive model that identifies impending engine issues. The study anticipates improving Cummins’ maintenance strategies, reducing downtime, enhancing operational efficiency, and cutting costs. The study’s broader impact lies in contributing insights to predictive maintenance for heavy truck engines, emphasizing the transformative potential of technology in revolutionizing logistics industry practices.