IM FEB 23 February 2023 | Page 29

HIGH PROFILE

Mine in the cloud as possibly a standard part of it if the value proposition is borne out .

Proven value so far

At the mentioned African operation , Wenco had access to near real time data with customer permission via the site-based ReadyLine that was already running there . The data was stored on a beta version of the new cloud based ReadyLine that is in development – and Rithmik was then allowed to access the cloud and run its advanced algorithms on that data . The customer wanted to focus on the trucks – in this case Caterpillar and Hitachi dump trucks . First off , Rithmik ’ s approach is to first ask the customer if there were any particular issues – in this case it was recurring turbo , strut and final drive failures on some of the trucks . These were the focus then for the initial analysis . But because Rithmik ’ s AI creates a digital twin of the entire truck and analyses all available data , they will invariably identify other problems that the customer was not even aware of , as well as root causes that were also unexpected .

For some of the known problems , the customer had seen 370 brake overstroke events , creating over 600 hours a year in lost production . These overstroke events along with malfunctioning traction control systems contributed to eight final drive failures per year and the associated replacement costs . The Rithmik analysis was able

to show that the root causes were slack adjuster and wheel speed sensor failures that were impacting braking and traction control systems . But a problem was also identified that wasn ’ t known about – regulator failures – 76 % of the OEM fleet had regulators failing open leading to inadequate lubrication and poor fuel economy costing $ 678,000 / y in wasted fuel . Calibration issues on the transmission and torque converter were also singled out . Resolution of these issues therefore resulted in major savings , as well as a reduction in GHG emissions that result from inefficiently burned diesel fuel .

Another problem given by the customer was turbo failures causing 20 + hours downtime for a particular model of truck . While Rithmik ’ s AI algorithms predicted the turbocharger predicted failures up to 20 days in advance , including the same shift where the failures happened , the OEM sensor did not give an alarm at failure so the operator continued to drive the truck unaware . The Rithimk Asset Health Analyser and analytics team also determined the root cause of the problem . There was no shutdown timer for the truck and operators were not allowing adequate spool down before shutdown . IM

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