This week’s blog is about data collection. Specifically, oil analysis data collection, however, the learning is relevant to all forms of condition monitoring data.
Our work this week identified some results that we just had to highlight.
Part of the unique aspect of the Relialytics analysis process is the data visualisations we construct as a result of comparing relationships between the textual data contained within the oil lab technicians sample comments.
One of the advantages of this approach is its ability to allow us to very quickly drill down to the main issues experienced by each component type.
During the review of this oil sample data, we found a high number of samples (15%), over the lifespan of the units we were investigating, had missing (hours) or potentially incorrect (oil type and grade) information provided with the sample labels.
We collect condition monitoring data to allow us to make informed decisions on the operation and maintenance of our equipment. There’s no point in doing this if the information collected is not reliable. It will eventually end with flawed decision making.
Maintenance engineers know that the information provided on samples plays an important role in the ability of the laboratory to interpret sample results. If the information is wrong then the sample interpretation can be wrong and the money spent on obtaining the sample wasted.
In our last blog, we showed that it was not hard to imagine a medium-sized mine spending in excess of $500k per annum on oil sampling across a fleet of around 50 trucks. If 15% of these samples have incorrect information or are sampled incorrectly (e.g. with contaminated kits), then approximately $75k in sampling would be wasted.
You spend significant amounts of time and money monitoring the condition of your equipment.
You have to make the oil samples you collect count by providing the correct supporting information.
What percentage of your samples are you wasting?