search for: pump2

...45.27 140.028 pump1 01-07-2017 00:20 0 2.031 52.9 137.698 pump1 01-07-2017 00:30 0 2.267 60.12 139.054 pump1 01-07-2017 00:40 1 2.267 60.12 139.054 pump1 01-07-2017 00:50 0 2.267 60.12 139.054 pump2 01-07-2017 00:00 0 3.443 69.6 139.806 pump2 01-07-2017 00:10 0 0.501 45.27 140.028 pump2 01-07-2017 00:20 0 2.031 52.9 137.698 pump2 01-07-2017 00:30 0 2.267 60.12 139.054 pump2 01-07-2017 00:40...

A small example data set that illustrates your question will be of great value to those trying to help. This appears to be a transformation that you are wanting to do (timestamp to units of time) so a data representing what you have (dput() is handy for this) and one representing what you want to have with any guidance regarding how to use the other columns in you data set (e.g., the event(0/1)).

...#39;s failure. Below is thought process to simulate the data: - For each Pump readings are captured via sensors each minute for period of 3 months. There are 10 pumps in total. - The failure rate or occurrences of event in 3 months for each Pump is defined. i.e Pump1 would fail 3 times in 3 months, Pump2 would fail 2 times in 3 months, etc. - The failure modes are defined based on the combination of below variables: Failure modes:: -- Bearing damage --> +ve_corr('temprature' + vibration) -- Cavitation --> -ve_corr( +ve_corr(discharge_pressure + flow) + temprature) -- worn...