Computing system with event prediction mechanism and method of operation thereof
A computing system includes a processor configured to: generate a first artificial intelligence (AI) model for S.M.A.R.T. diagnostic information for a storage enclosure; generate a second artificial intelligence (AI) model for device temperature information for the storage enclosure; generate a third artificial intelligence (AI) model for device self-test information for the storage enclosure; generate a fourth artificial intelligence (AI) model for device-detected issues for the storage enclosure; generate a fifth artificial intelligence (AI) model for host-detected issues for the storage enclosure; generate an event prediction artificial intelligence (AI) model from the aggregation of a feature selection from the first AI model, the second AI model, the third AI model, the fourth AI model, and the fifth AI model; and operate the event prediction AI model to generate an event prediction for communicating an upcoming negative operational status for the storage enclosure.
1 . A computing system comprising:
a processor configured to:
generate a first artificial intelligence (AI) model for Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T.) diagnostic information for a storage enclosure;
generate a second artificial intelligence (AI) model for device temperature information for the storage enclosure;
generate a third artificial intelligence (AI) model for device self-test information for the storage enclosure;
generate a fourth artificial intelligence (AI) model for device-detected issues for the storage enclosure;
generate a fifth artificial intelligence (AI) model for host-detected issues for the storage enclosure;
generate an event prediction artificial intelligence (AI) model from an aggregation of a feature selection from the first AI model, the second AI model, the third AI model, the fourth AI model, and the fifth AI model;
operate the event prediction AI model to generate an event prediction for communicating an upcoming negative operational status for the storage enclosure;
generate an event prediction AI chart including a S.M.A.R.T. axis, a device self-test axis, a device temperature axis, a host-detected issues axis, and a device-detected issues axis by the event prediction AI model; and
apply a grading overlay to the event prediction AI chart indicating values of the event prediction by the event prediction AI model for attributes for displaying on a device.
2 . The computing system as claimed in claim 1 wherein the processor further configured to generate the first AI model provides a S.M.A.R.T. feature selection, the second AI model provides a device temperature feature selection, the third AI model provides a device self-test feature selection, the fourth AI model provides a device-detected feature selection, and the fifth AI model provides a host-detected feature selection.
3 . The computing system as claimed in claim 1 wherein the processor is further configured to generate the event prediction includes calculate a remaining usable life (RUL) including a functional indicator for displaying on the device.
4 . The computing system as claimed in claim 1 wherein the processor is further configured to perform an AI update to refine an AI model.
5 . The computing system as claimed in claim 1 wherein the processor is further configured to generate a S.M.A.R.T. prediction, a device temperature prediction, a device self-test prediction, a device-detected issues prediction, and a host-detected issues prediction different from the event prediction.
6 . The computing system as claimed in claim 1 further comprising a communication interface configured to display a table of features selected for the S.M.A.R.T. axis, the device self-test axis, the device temperature axis, the host-detected issues axis, or the device-detected issues axis selected by a user includes displaying a problem feature on the device.
7 . A method of operation of a computing system comprising:
generating a first artificial intelligence (AI) model for Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T.) diagnostic information for a storage enclosure;
generating a second artificial intelligence (AI) model for device temperature information for the storage enclosure;
generating a third artificial intelligence (AI) model for device self-test information for the storage enclosure;
generating a fourth artificial intelligence (AI) model for device-detected issues for the storage enclosure;
generating a fifth artificial intelligence (AI) model for host-detected issues for the storage enclosure;
generating an event prediction artificial intelligence (AI) model from an aggregation of a feature selection from the first AI model, the second AI model, the third AI model, the fourth AI model, and the fifth AI model;
operating the event prediction AI model for generating an event prediction for communicating an upcoming negative operational status for the storage enclosure;
generating an event prediction AI chart including a S.M.A.R.T. axis, a device self-test axis, a device temperature axis, a host-detected issues axis, and a device-detected issues axis by the event prediction AI model; and
applying a grading overlay to the event prediction AI chart indicating values of the event prediction by the event prediction AI model for attributes for displaying on a device.
8 . The method as claimed in claim 7 wherein:
generating the first AI model provides a S.M.A.R.T. feature selection;
generating the second AI model provides a device temperature feature selection;
generating the third AI model provides a device self-test feature selection;
generating the fourth AI model provides a device-detected feature selection; and
generating the fifth AI model provides a host-detected feature selection.
9 . The method as claimed in claim 7 wherein generating the event prediction includes calculating a remaining usable life (RUL) including displaying a functional indicator on the device.
10 . The method as claimed in claim 7 further comprising performing an AI update to refine an AI model.
11 . The method as claimed in claim 7 further comprising generating a S.M.A.R.T. prediction, a device temperature prediction, a device self-test prediction, a device-detected issues prediction, and a host-detected issues prediction different from the event prediction.
12 . The method as claimed in claim 7 further comprising displaying a table of features selected for the S.M.A.R.T. axis, the device self-test axis, the device temperature axis, the host-detected issues axis, or the device-detected issues axis selected by a user including displaying a problem feature on the device.
13 . A non-transitory computer readable medium including instructions for a computing system, the instructions when executed by a processor cause the processor to perform functions comprising:
generating a first artificial intelligence (AI) model for Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T.) diagnostic information for a storage enclosure;
generating a second artificial intelligence (AI) model for device temperature information for the storage enclosure;
generating a third artificial intelligence (AI) model for device self-test information for the storage enclosure;
generating a fourth artificial intelligence (AI) model for device-detected issues for the storage enclosure;
generating a fifth artificial intelligence (AI) model for host-detected issues for the storage enclosure;
generating an event prediction artificial intelligence (AI) model from an aggregation of a feature selection from the first AI model, the second AI model, the third AI model, the fourth AI model, and the fifth AI model;
operating the event prediction AI model for generating an event prediction for communicating an upcoming negative operational status for the storage enclosure;
generating an event prediction AI chart including a S.M.A.R.T. axis, a device self-test axis, a device temperature axis, a host-detected issues axis, and a device-detected issues axis by the event prediction AI model; and
applying a grading overlay to the event prediction AI chart indicating values of the event prediction by the event prediction AI model for attributes for displaying on a device.
14 . The non-transitory computer readable medium including the instructions as claimed in claim 13 wherein:
generating the first AI model provides a S.M.A.R.T. feature selection;
generating the second AI model provides a device temperature feature selection;
generating the third AI model provides a device self-test feature selection;
generating the fourth AI model provides a device-detected feature selection; and
generating the fifth AI model provides a host-detected feature selection.
15 . The non-transitory computer readable medium including the instructions as claimed in claim 13 wherein generating the event prediction includes calculating a remaining usable life (RUL) including displaying a functional indicator.
16 . The non-transitory computer readable medium including the instructions as claimed in claim 13 further comprising generating a S.M.A.R.T. prediction, a device temperature prediction, a device self-test prediction, a device-detected issues prediction, and a host-detected issues prediction different from the event prediction.
17 . The non-transitory computer readable medium including the instructions as claimed in claim 13 further comprising displaying a table of features selected for the S.M.A.R.T. axis, the device self-test axis, the device temperature axis, the host-detected issues axis, or the device-detected issues axis selected by a user including displaying a problem feature on the device.