GATX is testing novel types of sensors on 50 of its intermodal railcars – one of the latest innovations in predictive maintenance. Currently in the proof-of-concept phase, the sensors aim to remotely calculate a railcar brake systems’ real wear and tear.
How does predictive maintenance for brake blocks work?
Installed in different places along the brake systems, the multiple pressure sensors are key in monitoring and registering brake cycles. The un/load sensors also measure the wagon’s load, consequently ensuring for overload detection. In tandem with the familiar GPS sensors, the new sensors analyze the railcars’ route topography, providing essential information about the brake’s use of force on any given route. Such topography and brake force data is invaluable for the building of route models, all used to better assess key parameters in the brake’s wear and tear.
What can deteriorate a brake’s condition? All places that require the brake’s usage in slowing down will lead to its natural wear. This includes hills, downhill routes, curves, bridges, tunnels, and stations. The route’s topography measurements are then used to translate the break block’s wear and tear. How? Wayside monitoring pictures are taken to keep track of the brake block’s size and usage level.
To build a superior machine learning model means to train the applied algorithm. The tool is bound to be indispensable in the future predictions of brake wear. This means that using the algorithm with any route topography, brake force, atmospheric conditions (such as weather), and mileage (by making deductions from the brake’s current size), would provide an accurate estimation of the brake’s expected level of wear and tear. This is an example of the innovate technologies we use to give you significant advantages.
What are the advantages of brake block sensors?
The machine’s testing phase will continue until the end of the year, and we look forward to providing you with more updates on its progress. Reach out to your key Account Manager or contact us if you would like more details on this proof of concept.
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