This program will use a tablet's camera (e.g. iPad or Android tablet) to scan mining truck tyres to determine their remaining life span based on a predictive maintenance algorithm. Before scanning occurs, the user will select what tyres are being scanned e.g. mud tyres, sand tyres etc since there are different life spans for these tyres. Also the track the truck drove will be selected, too. This means the truck's driving path is required e.g. sourced from the map used for schematics.
The required deliverable is development of a unique Predictive Maintenance (PM) algorithm which has an edge over the competitors. This may be through edge detection on the image to determine how much rubber is left on the tyre, calculating the scale based on part of the tyre, tyre profiling, route optimisation with route tracking etc.
Since the algorithm hasn't been created yet, how would you be tackling this problem to optimise the algorithm because there's no substantial competitive advantage with similar-performing algorithms from different companies?
Currently, the following factors have been identified when a tablet's camera scans tyres for PdM but how would your team tackle these factors in dev please?
-Edge detection on the image to determine how much rubber is left on the tyre
-Calculating the scale based on part of the tyre
-Route optimisation with route tracking
-Tyre thread depth
This app ideall could run on a tablet e.g. iOS, Surface or Android and use the tablet's camera. A peripheral device which attaches to the tablet is also acceptable but can be bulky to carry around a mine site.
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