![]() While this is still a win in comparison to not knowing there was an issue until the machine pulls itself up and becomes a breakdown event, it’s still reactive maintenance. As the failure mode has progressed to a point where damage is now being caused, the window of opportunity to plan repairs has also reduced and the machine will likely need to be removed from production asap to complete repairs, resulting in unplanned downtime. Potentially the failures are well advanced along the PF curve before they become apparent in the data. While all of these systems can provide valuable information and can identify certain failure modes, they have limitations to what failure modes can be detected and lack the sensitivity to identify the issue at the earliest possible onset, resulting in the failure being progressed to the point where damage is already being done. Again, this is a huge improvement over daily averages, but the averaging of the data still suffers from the flattening out the highs and lows and again can mask any developing issues. Other platforms are using hourly averages of the same data. via satellite, and while this is a huge leap from the fortnightly downloads, the daily averages have a nasty habit of removing the peaks and valleys and can mask developing issues until it’s too late. Other solutions out in the market, are transmitting a daily average for temperatures and pressures etc. For them, that was remote monitoring and yes, they were having wins on slow burning issues such as cooling system issues and such, however overlooked predicting things such as an injector or turbo failure. Over the years, Monico has been engaged with a number of different companies that allude to capturing machine data and doing remote monitoring, only to find out that they were manually downloading information from the machines once a fortnight, reviewing and storing the data and at best running a couple of reports. To begin, what is remote condition monitoring for mobile assets? It should be an easy answer but it’s surprising how many different models or versions are out there. With correctly matched sampling and transmission rates, the issue can be identified sometimes months in advance and well before any permanent damage is done. ![]()
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