search for: pump_id

Mark, Below is the sampled simulated granular data format for pumps for trial period of 3 months that I need to transform for survival analysis: 3 months = (60*24*90) minutes i.e 129600 minutes pump_id timings events vibration temprature flow pump1 01-07-2017 00:00 0 3.443 69.6 139.806 pump1 01-07-2017 00:10 1 0.501 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 AM, Sunny Singha <sunnysingha.analytics at gmail.com> wrote: > > Thanks Boris and Bret, > I was successful in simulating granular/transactional data. > Now I need some guidance to transform the same data in format acceptable > for survival analysis i.e below format: > > pump_id | event_episode_no. | event(0/1) | start | stop | time_to_dropout > > The challenge I'm experience is to generate the 'start' and 'stop' in units > of minutes/days from single column of 'Timestamp' which is > the column from transactional/granular data based...

Thanks Boris and Bret, I was successful in simulating granular/transactional data. Now I need some guidance to transform the same data in format acceptable for survival analysis i.e below format: pump_id | event_episode_no. | event(0/1) | start | stop | time_to_dropout The challenge I'm experience is to generate the 'start' and 'stop' in units of minutes/days from single column of 'Timestamp' which is the column from transactional/granular data based on condition tagged...

In principle what you need to do is the following: - break down the time you wish to simulate into intervals. - for each interval, and each failure mode, determine the probability of an event. Determining the probability is the fun part, where you make your domain knowledge explicit and include all the factors into your model: cumulative load, failure history, pressure, temperature,

Hi friends, I haven't done such a simulation before and any help would be greatly appreciated. I need your guidance. I need to simulate end to end data for Reliability/survival analysis of a Pump ,with correlation in place, that is at 'Transactional level' or at the granularity of time-minutes, where each observation is a reading captured via Pump's sensors each minute. Once