Wheel Profile Wear Prediction
Railway operators, wagon holders, and workshops around the world are now using a combination of data from wayside detectors, workshop measurement, and onboard systems to implement vehicle maintenance strategies and in turn increase safety, reliability, and availability of railway assets by detecting and mitigating the effect of damages or worn assets.
While current wheel profile measurement can provide insights on reactive measures on wheels based on reached limits, the prediction of wheel wear and an understanding of the remaining useful life enable more proactive ways of performing maintenance on wheels. Moreover, a prescription system which provides an integrated decision support, setting priorities on the maintenance of wheel assets and wagons enables a systematic and efficient way of performing wheel maintenance.
The wheel wear prediction solution replicates every wheel as a digital twin tracking the condition of the individual wheel, self-correcting itself and performing analytics as new data comes in but also predicts what next measurement should be expected from the field. The eco system of digital twins in the E365 Analytics platform enables aggregation of the digital twins in relation to the operational and maintenance principles that are in place and thereby aligns the decision support.
The prescriptions are then based on operational principles of the operator together with the prediction of the wheel wear, yielding a hot list of wheels and wagons requiring timely action by the maintenance stakeholder or team. The prescriptions and up to date condition information of wheels, wagons, trains and fleet is provided using SaaS.
Sources
[1] Karim, R., Birk, W., & Larsson-Kråik, P. O. (2015). Cloud-based emaintenance solutions for condition-based maintenance of wheels in heavy haul operation. In International Heavy Haul Association: The 11th International Heavy Haul Association Conference will be held 21-24 June 2015 in Perth 21/06/2015-24/06/2015. International Heavy Haul Association.
[2] Jonsson, B., Westerberg, J., Pallari, R., (2019) Analytics for Wayside-detector data in Sweden and Norway – Useful for LKAB’s work in preventive maintenance? In International Heavy Haul Association: The 13th International Heavy Haul STS Conference, June 10-15, Narvik, Norway.