Applying predictive maintenance to predict labor, what kind of test data should be used?
$begingroup$
I am trying to make a machine learning based program which can give an early alert of cow's calving/labor time (few hours before). As I search for examples and technique, I am interested in using the predictive maintenance, especially after I read this one in Azure machine learning studio: https://gallery.azure.ai/Experiment/df7c518dcba7407fb855377339d6589f
It has a very clear explanation on the data preparation, etc.
And the research questions I would like to adopt are:
- Regression models: How many more cycles an in-service engine will
last before it fails? - Binary classification: Is this engine going to fail within w1 cycles?
Which I will try to do on calving prediction, e.g. regression models: how many more hours/days a cow will calve?, etc. In my case, each cow has information on activity feature per minute and per hour (such as acceleration, activity type), and previous calving time which I will use for prediction.
However, I have some confusion on the test data that I should use. As far as I understand, usually if we want to use regression or binary classification, for one ID (machine or cow), there will be only one record for each engine/cow. In the Azure case, they also used only one record which has maximum cycle time for each engine.
My question is, to be able to predict when one specific cow will calve, I would like to use its historical activity data (for example using their activity data for last 48 hours, which will be 48 records/cow) and from several records for one cow, I need one prediction output (hours to calve). Is it possible to do that?
Any help will be appreciated.
machine-learning python classification regression predictive-modeling
asked 3 mins ago
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$begingroup$
I am trying to make a machine learning based program which can give an early alert of cow's calving/labor time (few hours before). As I search for examples and technique, I am interested in using the predictive maintenance, especially after I read this one in Azure machine learning studio: https://gallery.azure.ai/Experiment/df7c518dcba7407fb855377339d6589f
It has a very clear explanation on the data preparation, etc.
And the research questions I would like to adopt are:
- Regression models: How many more cycles an in-service engine will
last before it fails? - Binary classification: Is this engine going to fail within w1 cycles?
Which I will try to do on calving prediction, e.g. regression models: how many more hours/days a cow will calve?, etc. In my case, each cow has information on activity feature per minute and per hour (such as acceleration, activity type), and previous calving time which I will use for prediction.
However, I have some confusion on the test data that I should use. As far as I understand, usually if we want to use regression or binary classification, for one ID (machine or cow), there will be only one record for each engine/cow. In the Azure case, they also used only one record which has maximum cycle time for each engine.
My question is, to be able to predict when one specific cow will calve, I would like to use its historical activity data (for example using their activity data for last 48 hours, which will be 48 records/cow) and from several records for one cow, I need one prediction output (hours to calve). Is it possible to do that?
Any help will be appreciated.
machine-learning python classification regression predictive-modeling
asked 3 mins ago
npmnpm
1
New contributor
npm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am trying to make a machine learning based program which can give an early alert of cow's calving/labor time (few hours before). As I search for examples and technique, I am interested in using the predictive maintenance, especially after I read this one in Azure machine learning studio: https://gallery.azure.ai/Experiment/df7c518dcba7407fb855377339d6589f
It has a very clear explanation on the data preparation, etc.
And the research questions I would like to adopt are:
- Regression models: How many more cycles an in-service engine will
last before it fails? - Binary classification: Is this engine going to fail within w1 cycles?
Which I will try to do on calving prediction, e.g. regression models: how many more hours/days a cow will calve?, etc. In my case, each cow has information on activity feature per minute and per hour (such as acceleration, activity type), and previous calving time which I will use for prediction.
However, I have some confusion on the test data that I should use. As far as I understand, usually if we want to use regression or binary classification, for one ID (machine or cow), there will be only one record for each engine/cow. In the Azure case, they also used only one record which has maximum cycle time for each engine.
My question is, to be able to predict when one specific cow will calve, I would like to use its historical activity data (for example using their activity data for last 48 hours, which will be 48 records/cow) and from several records for one cow, I need one prediction output (hours to calve). Is it possible to do that?
Any help will be appreciated.
machine-learning python classification regression predictive-modeling
asked 3 mins ago
npmnpm
1
New contributor
npm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to make a machine learning based program which can give an early alert of cow's calving/labor time (few hours before). As I search for examples and technique, I am interested in using the predictive maintenance, especially after I read this one in Azure machine learning studio: https://gallery.azure.ai/Experiment/df7c518dcba7407fb855377339d6589f
It has a very clear explanation on the data preparation, etc.
And the research questions I would like to adopt are:
- Regression models: How many more cycles an in-service engine will
last before it fails? - Binary classification: Is this engine going to fail within w1 cycles?
Which I will try to do on calving prediction, e.g. regression models: how many more hours/days a cow will calve?, etc. In my case, each cow has information on activity feature per minute and per hour (such as acceleration, activity type), and previous calving time which I will use for prediction.
However, I have some confusion on the test data that I should use. As far as I understand, usually if we want to use regression or binary classification, for one ID (machine or cow), there will be only one record for each engine/cow. In the Azure case, they also used only one record which has maximum cycle time for each engine.
My question is, to be able to predict when one specific cow will calve, I would like to use its historical activity data (for example using their activity data for last 48 hours, which will be 48 records/cow) and from several records for one cow, I need one prediction output (hours to calve). Is it possible to do that?
Any help will be appreciated.
machine-learning python classification regression predictive-modeling
machine-learning python classification regression predictive-modeling
asked 3 mins ago
npmnpm
1
New contributor
npm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
npmnpm
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npm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
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asked 3 mins ago
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