Predicting disk drive failure at a central processing facility using an evolving disk drive failure prediction algorithm
First Claim
1. A method for improving disk drive failure prediction at a central processing facility, the method comprising:
- receiving a set of quality metric values and a failure indicator from one of a plurality of disk drives that are remote to the central processing facility; and
evolving a disk failure prediction algorithm (DFPA) comprising a neural network to detect an impending failure of at least one of the remote disk drives, the evolving comprising;
(a) applying a function to a set of primary quality metrics to generate a set of secondary quality metrics;
(b) using a genetic algorithm to select a subset of the secondary quality metrics;
(c) applying the quality metric values corresponding to the selected subset of secondary quality metrics to the neural network to generate an output indicative of the fitness of the secondary selected subset of secondary quality metrics to predict drive failure, the applying comprising;
applying, to the inputs quality metric values, a process element function to generate the output; and
comparing the output to a reference value based at least in part on the received set of quality metric values and failure indicator; and
(d) repeating steps (b) and (c) at least once to determine a subset of secondary quality metrics to be used in the DFPA.
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Abstract
A method of predicting disk drive failure at a central processing facility using an evolving drive failure prediction algorithm (DFPA) is disclosed. A set of quality metric values are transmitted from each of a plurality of remote disk drives to the central processing facility. The DFPA is executed at the central processing facility in response to the quality metric values to detect an impending failure of at least one of the remote disk drives. The DFPA is evolved at the central processing facility in response to a reference data base of quality metric values and a corresponding failure indicator. The processes is repeated so as to improve the accuracy of the DFPA over time.
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Citations
14 Claims
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1. A method for improving disk drive failure prediction at a central processing facility, the method comprising:
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receiving a set of quality metric values and a failure indicator from one of a plurality of disk drives that are remote to the central processing facility; and evolving a disk failure prediction algorithm (DFPA) comprising a neural network to detect an impending failure of at least one of the remote disk drives, the evolving comprising; (a) applying a function to a set of primary quality metrics to generate a set of secondary quality metrics; (b) using a genetic algorithm to select a subset of the secondary quality metrics; (c) applying the quality metric values corresponding to the selected subset of secondary quality metrics to the neural network to generate an output indicative of the fitness of the secondary selected subset of secondary quality metrics to predict drive failure, the applying comprising; applying, to the inputs quality metric values, a process element function to generate the output; and comparing the output to a reference value based at least in part on the received set of quality metric values and failure indicator; and (d) repeating steps (b) and (c) at least once to determine a subset of secondary quality metrics to be used in the DFPA. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An apparatus for improving disk drive failure prediction at a central processing facility, the apparatus comprising:
a processor configured to execute instructions causing it to; receive a set of quality metric values and a failure indicator from one of a plurality of disk drives that are remote to the central processing facility; and evolve a disk failure prediction algorithm (DFPA) comprising a neural network to detect an impending failure of at least one of the remote disk drives, the evolving comprising; (a) applying a function to a set of primary quality metrics to generate a set of secondary quality metrics; (b) using a genetic algorithm to select a subset of the secondary quality metrics as inputs to processing elements of a neural network; (c) applying the quality metric values corresponding to the selected subset of secondary quality metrics to the neural network to generate an output indicative of the fitness of the selected subset of secondary quality metrics to predict drive failure, the applying comprising; applying, to the quality metric values, a process element function to generate the output; and comparing the output to a reference value based at least in part on the received set of quality metric values and failure indicator; and (d) repeating steps (b) and (c) at least once to determine a subset of secondary quality metrics to be used in the DFPA. - View Dependent Claims (9, 10, 11, 12, 13, 14)
Specification