Patent for Predicting Hard Disk Drive Failures Using Machine Learning and SMART Attributes
MIE Professor Sagar Kamarthi was awarded a patent for “Hard disk drive failure prediction method.”
Abstract Source: USPTO
Provided herein are methods and systems for a hard drive failure prediction system to determine a predicted hard drive failure within a time-in-advance determined time period. The system includes a machine-learning model, such as an XGBoost model, trained using historical hard drive operational data from a data center. The historical hard drive operation data corresponds to SMART attributes. Statistical analysis is performed to determine a set of SMART attributes most indicative of hard drive failure. The model is trained using the historical hard drive operational data corresponding to the set of SMART attributes. The accuracy results of the model are used to determine a time-in-advance value that balances accuracy with failure lead time. A signal length of operational data for a particular hard drive is provided as input to the trained model and an output indicates if the hard drive will fail within the time period of the time-in-advance value.
