PROSPECTS FOR APPLYİNG ARTİFİCİAL INTELLİGENCE METHODS TO ASSESS THE TECHNİCAL CONDİTİON OF RAİLWAY ROLLİNG STOCK
Keywords:
rolling stock, technical condition, artificial intelligence, machine learning, strain-gauge measurement, residual life, condition-based maintenance.Abstract
The thesis considers the transition from periodic, labour-intensive assessment of the technical condition of railway rolling stock to continuous assessment based on artificial intelligence. The experience of assessing the frame of the ADM-1 special self-propelled railcar by ultrasonic flaw detection, strain-gauge measurements and the finite element method is summarised. It is shown that the measured dependence of frame stress on running speed is well described by simple models, which makes it suitable for machine learning. A three-stage scheme is proposed in which on-board data, learned models and a decision layer support condition-based maintenance and energy-efficient operation of electric rolling stock.
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