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 2.267 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 1 2.267 60.12 139.054
pump2 01-07-2017 00:50 0 2.267 60.12 139.054
The above data set records observations and timings where 'pumps'
experienced failure, tagged as '1' in column 'events'.
In the above granular dataset the pump1 experiences 2 "event episodes."
Below is the desired transformed format. the covariates in this data
set will have the mean value:
pump_id event_episodes event_status start(minutes)
stop(minutes)
pump1 1 1
0 10
pump1 2 1
10 40
pump1 3 0
40 129600
pump2 1 1
0 40
pump2 2 0
0 129600
.........
.........
The 'start' and 'stop' columns are evaluated from the 'timings'
columns. I need help in performing such transformation in 'R'.
Please guide and help.
Regards,
Sandeep
On Wed, Jul 5, 2017 at 7:26 AM, Mark Sharp <[email protected]> wrote:
> 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)).
>
> Mark
> R. Mark Sharp, Ph.D.
> [email protected]
>
>
>
>
>
>> On Jul 4, 2017, at 7:02 AM, Sunny Singha <[email protected]>
>> 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 on condition tagged in
>> separate column, 'event 0/1, (i.e event ).
>>
>> Please guide how to do such transformation in 'R'.
>>
>> Regards,
>> Sandeep
>>
>>
>>
>> On Wed, Jun 28, 2017 at 2:51 PM, Boris Steipe <[email protected]>
>> wrote:
>>
>>> 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, phase of the moon ...
>>> - once you have a probability of failure, use the runif() function to
>>> give you
>>> a uniformly distributed random number in [0, 1]. If the number is
>>> smaller than
>>> your failure probability, accept the failure event, and record it.
>>> - Repeat many times.
>>>
>>> Hope this helps.
>>> B.
>>>
>>>
>>>
>>>
>>>> On Jun 27, 2017, at 10:58 AM, sandeep Rana <[email protected]> wrote:
>>>>
>>>> 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 transactional data is prepared I Then need to summarise above data
>>> for reliability/ survival analysis.
>>>>
>>>> To begin with below is the transactional data format that i want prepare:
>>>> Pump-id| Timestamp | temp | vibration | suction pressure| discharge
>>> pressure | Flow
>>>>
>>>> Above transactional data has to be prepared with below failure modes
>>>> Defects :
>>>> (1) Cavitation – very high in frequency but low impact
>>>> (2) Bearing Damage – very low in frequency but high impact
>>>> (3) Worn Shaft – medium frequency but medium impact
>>>>
>>>> I have used survsim package but that's not what I need here.
>>>> Please help and guide.
>>>>
>>>> Regards,
>>>> Sandeep
>>>>
>>>> ______________________________________________
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>>>> PLEASE do read the posting guide http://www.R-project.org/
>>> posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>> ______________________________________________
>>> [email protected] mailing list -- To UNSUBSCRIBE and more, see
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/
>>> posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [email protected] mailing list -- To UNSUBSCRIBE and more, see
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>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
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