Apparatus and method for system profile learning in an information handling system

1. An information handling system, comprising:

a processing system including:

a first sensor configured to provide first sensor data; and

a second sensor configured to provide second sensor data; and

a management system coupled to the processing system and including:

an anomaly table;

a learned model table entry associated with the processing system and including a learned model and a first sensor data history; and

a prediction module to implement a prediction algorithm; and

wherein the management system is configured to:

receive the first sensor data and the second sensor data;

determine, based on the prediction algorithm and the learned model, an estimate of a first value of the first sensor data using a second value of the second sensor data;

determine a residual of the first value by a comparison of the estimate to the first value;

determine, based on the learned model and the first sensor data history, a significance of the residual, wherein the significance having a significant value is associated with a predicted anomaly;

determine that an anomaly table entry in the anomaly table has a known anomaly class for the predicted anomaly, based on the first value, the second value, and known anomaly classes in the anomaly table, in response to the significance having the significant value; and

perform a remediation plan of the anomaly table entry to resolve the predicted anomaly in response to determining the known anomaly class;

wherein he known anomaly classes include an exceeded threshold, an outlier, a processing system degradation, a processing system failure, a thermal trip, a fan failure, a fan rotor lock, a processor throttling, a dust clogged heat-sink, an inlet vent obstruction, a thermal grease degradation, a laptop base feet removal/imbalance, a processing system performance degradation, a sub-optimal processing system-performance/system-loading event, a power over/under current event, and a high/low system load condition.

2. The information handling system of claim 1 , wherein the management system is further configured to:

define a new anomaly class for the predicted anomaly in response to determining that the predicted anomaly has an unknown anomaly class;

add the new anomaly class to a new anomaly table entry in the anomaly table; and

add a severity and a remediation plan to the new anomaly table entry.

3. The information handling system of claim 1 , wherein the management system is further configured to:

update the learned model to account for at least one of an age of the processing system, a degradation in processing system performance, and a processing system wear-out condition.

4. The information handling system of claim 1 , wherein the first sensor and the second sensor comprises at least one of a fan sensor, a power sensor, a thermal sensor, a performance sensor, and a system load sensor.

5. The information handling system of claim 1 , wherein the first sensor data and the second sensor data comprises at least one of a fan speed, a fan operating state, a voltage level, a current level, a power supply operating state, a temperature of the processing system, the temperature of a device, an ambient temperature of the processing system, the ambient temperature of the device, a processor performance, a memory access performance, an input/output device performance, a processor utilization, a cache state residency, a memory utilization, a built in operating system (BIOS) log of the processing system, and a communication log between the processing system and a remote information handling system.

6. The information handling system of claim 1 , wherein the prediction algorithm comprises a machine learning algorithm.

7. The information handling system of claim 6 , wherein the prediction algorithm further comprises a Bayesian average, a weighted average, a basic average, a rule of succession, and other forms of additive smoothing.

8. The information handling system of claim 1 , wherein the remediation plan comprises a fan replacement, a heat-sink dust removal process, an inlet vent clearing process, a power supply replacement, a re-application of heat-sink thermal grease, a replacement/balancing of laptop base feet, and a device replacement.

9. The information handling system of claim 1 , wherein:

the predicted anomaly comprises a clogged heat-sink;

the first sensor comprises a fan sensor configured to provide a fan speed of a fan;

the second sensor comprises a thermal sensor configured to provide a processor temperature of a processor;

the processing system further comprises a performance sensor configured to provide a processor utilization of the processor;

the first sensor data history comprises a fan sensor data history;

the prediction algorithm comprises a multi-variant least square algorithm; and

the management system is further configured to:

receive the fan speed, the processor temperature, and the processor utilization; and

determine, based on the multi-variant least square algorithm and the learned model, the estimate of the fan speed using the processor temperature and the processor utilization;

wherein:

determining the significance further comprises utilizing fan speed outlier information and fan speed threshold information from the fan sensor data history; and

determining that the anomaly table entry has the known anomaly class for the clogged heat-sink is further based on the fan speed, the processor temperature, the processor utilization, and the known anomaly classes in the anomaly table.

10. A method comprising:

providing, by a processing system of an information handling system, first sensor data from a first sensor of the processing system to a management system of the information handling system;

providing, by the processing system, second sensor data from a second sensor of the processing system to the management system;

determining, by a prediction module of the management system, based on a prediction algorithm and a learned model of a learned model table entry associated with the processing system, an estimate of a first value of the first sensor data using a second value of the second sensor data;

determining, by the prediction module, a residual of the first value by a comparison of the estimate to the first value;

determining, by the prediction module, a significance of the residual, based on the learned model and a first sensor data history of the learned model table entry, wherein the significance having a significant value is associated with a predicted anomaly;

determining, by the prediction module, that an anomaly table entry in an anomaly table has a known anomaly class for the predicted anomaly, based on the first value, the second value, and known anomaly classes in the anomaly table, in response to the significance having a significant value; and

performing, by the prediction module, a remediation plan of the anomaly table entry to resolve the predicted anomaly in response to determining the known anomaly class;

receiving, by the management system, third sensor data from a third sensor of the processing system, wherein the first sensor data comprises a fan speed of a fan, the second sensor data comprises a processor temperature of a processor, the third sensor data comprises a processor utilization of the processor, and the predicted anomaly comprises a clogged heat-sink; and

determining, by the prediction module, the estimate of the fan speed using the processor temperature and the processor utilization based on the prediction algorithm and the learned model, wherein the prediction algorithm comprises a multi-variant least square algorithm;

wherein:

determining the significance further comprises utilizing fan speed outlier information and fan speed threshold information from the first sensor data history;

the first sensor data history comprises a fan sensor data history; and

determining that the anomaly table entry has the known anomaly class for the clogged heat-sink is further based on the fan speed, the processor temperature, the processor utilization, and the known anomaly classes in the anomaly table.

11. The method of claim 10 , further comprising:

defining, by the prediction module, a new anomaly class for the predicted anomaly in response to determining that the predicted anomaly has an unknown anomaly class;

adding, by the prediction module, the new anomaly class to a new anomaly table entry in the anomaly table; and

adding, by the prediction module, a severity and a remediation plan to the new anomaly table entry.

12. The method of claim 10 , further comprising:

updating, by the prediction module, the learned model to account for at least one of an age of the processing system, a degradation in processing system performance, and a processing system wear-out condition.

13. The method of claim 10 , wherein the known anomaly classes comprises an exceeded threshold, an outlier, a processing system degradation, a processing system failure, a thermal trip, a fan failure, a fan rotor lock, a processor throttling, a dust clogged heat-sink, an inlet vent obstruction, a thermal grease degradation, a laptop base feet removal/imbalance, a processing system performance degradation, a sub-optimal processing system-performance/system-loading event, a power over/under current event, and a high/low system load condition.

14. An information handling system, comprising:

a processing system including:

a first sensor configured to provide first sensor data; and

a second sensor configured to provide second sensor data; and

a management system coupled to the processing system and including:

an anomaly table;

a learned model table entry associated with the processing system and including a learned model and a first sensor data history; and

a prediction module to implement a prediction algorithm; and

wherein the management system is configured to:

receive the first sensor data and the second sensor data;

determine, based on the prediction algorithm and the learned model, an estimate of a first value of the first sensor data using a second value of the second sensor data;

determine a residual of the first value by a comparison of the estimate to the first value;

determine, based on the learned model and the first sensor data history, a significance of the residual, wherein the significance having a significant value is associated with a predicted anomaly;

determine that an anomaly table entry in the anomaly table has a known anomaly class for the predicted anomaly, based on the first value, the second value, and known anomaly classes in the anomaly table, in response to the significance having the significant value; and

perform a remediation plan of the anomaly table entry to resolve the predicted anomaly in response to determining the known anomaly class;

wherein the remediation plan comprises a fan replacement, a heat-sink dust removal process, an inlet vent clearing process, a power supply replacement, a re-application of heat-sink thermal grease, a replacement/balancing of laptop base feet, and a device replacement.

15. The information handling system of claim 14 , wherein the management system is further configured to:

define a new anomaly class for the predicted anomaly in response to determining that the predicted anomaly has an unknown anomaly class;

add the new anomaly class to a new anomaly table entry in the anomaly table; and

add a severity and a remediation plan to the new anomaly table entry.

16. The information handling system of claim 14 , wherein the management system is further configured to:

update the learned model to account for at least one of an age of the processing system, a degradation in processing system performance, and a processing system wear-out condition.

17. The information handling system of claim 14 , wherein the first sensor and the second sensor comprises at least one of a fan sensor, a power sensor, a thermal sensor, a performance sensor, and a system load sensor.

18. The information handling system of claim 14 , wherein the first sensor data and the second sensor data comprises at least one of a fan speed, a fan operating state, a voltage level, a current level, a power supply operating state, a temperature of the processing system, the temperature of a device, an ambient temperature of the processing system, the ambient temperature of the device, a processor performance, a memory access performance, an input/output device performance, a processor utilization, a cache state residency, a memory utilization, a built in operating system (BIOS) log of the processing system, and a communication log between the processing system and a remote information handling system.

19. The information handling system of claim 14 , wherein the prediction algorithm comprises a machine learning algorithm.

20. The information handling system of claim 19 , wherein the prediction algorithm further comprises a Bayesian average, a weighted average, a basic average, a rule of succession, and other forms of additive smoothing.