I have data over a single, a machine includes different components, all the parts are interacting, the data are tracked for those parts, it tracks power consumption and many other relevant feature measurements. The longevity of the machine, from the seller, is specified to be around 2000-15000 hours. Due to this large range, it would be ideal to constrain the target for machine failure, with machine learning. Because we have tracked all the data, with the different components in the system. One specific component fails. Over a period of 9 months, the machine fails 5 times. Monthly average about 3-4 month. It is this, specific target we want to predict when the chances for failure are high.
Thus the specific instances for failure are limited. How do we deal with this, from a Machine Learning perspective, as opposed to giving a monthly average, it is not a safe bet? How do we train on limited instances for failure? This problem might not have one right answer, but many possible advice.