Wheel Remaining Useful Life (RUL) Analytics
One of the largest railroad company wants to change their scrap & keep policies on locomotive wheels to avoid waste caused by early replacement. This new policy needs to be determined by a benefit-risk analysis on a large amount of historical locomotive wheel data.
Calculated decay rates for different locomotive types. Performed a benefit-risk analysis on shifting policy limits. Derived a detailed decision diagram that helps shop manage replacement & truing events.
Wheel life is systematically extended by relaxing the scrap & keep policy on wheels with an acceptable risk. Additionally, at least 400 years of wheel life are extended every year by applying the detailed decision diagram.
Scope of Engagement
2 Member multi-disciplinary team for 3 months.
Data engineering, Data Science, Benefit-Risk Analysis