R help - Jul 2017 - Please help(urgent) - How to simulate transactional data for reliability/survival analysis

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 <sandykido at gmail.com>
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 
> 
> ______________________________________________
> R-help at r-project.org 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.

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.
msharp at TxBiomed.org




> On Jul 4, 2017, at 7:02 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 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 <boris.steipe at
utoronto.ca>
> 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 <sandykido at
gmail.com> 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
>>>
>>> ______________________________________________
>>> R-help at r-project.org 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.
>>
>> ______________________________________________
>> R-help at r-project.org 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]]
>
> ______________________________________________
> R-help at r-project.org 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.
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