Hard Disk Head Failure Prognosis via Signal Quality and Floating Quantity
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Solution Overview
Problem
Conventional methods fail to distinguish between head failures and surface abnormalities in hard disk apparatuses, leading to inadequate maintenance strategies as they cannot differentiate between errors caused by head failures and those caused by surface flaws or impurities, resulting in improper maintenance.
Innovation Solution
A failure prognosis device that determines the presence of a failure in a head by analyzing signal quality values and floating quantities, using a signal quality value based on the error between a reproducing signal and a target signal, and outputs a determination result to facilitate appropriate maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error rate detection method is used, then failure detection capability is provided, but inability to distinguish between head failure and surface abnormality occurs
Solution Approach 1:
The patent segments the error detection function into two distinct analysis paths: one for head failure detection (using signal quality values) and another for surface abnormality detection (using error rate). This segmentation allows the system to differentiate between the two causes while maintaining comprehensive failure detection capability.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that receives error data and routes it to appropriate analysis methods. This intermediary component enables the system to distinguish between head failures and surface abnormalities by analyzing the characteristics of errors and applying different evaluation criteria accordingly.
2Reliability
If conventional error rate method is used, then general failure detection is achieved, but improper maintenance decisions result
Solution Approach 1:
The patent implements a feedback mechanism where the classification results and analysis outcomes are fed back to the maintenance decision-making process. This feedback loop enables the system to provide specific maintenance recommendations based on the identified cause (head failure vs. surface abnormality), thereby improving maintenance decision accuracy.
Solution Approach 2:
The patent changes the parameters used for failure analysis from a single error rate metric to multiple parameters including signal quality values, error rates, and their combinations. This multi-parameter approach enables more accurate cause identification and consequently improves maintenance decision-making by providing specific actionable insights.
3Reliability
If head failure is detected, then whole storage surface becomes unusable, but premature replacement may occur
Solution Approach 1:
The patent performs preliminary classification of errors to determine whether they originate from head failure or surface abnormalities before taking maintenance actions. This preliminary action prevents premature head replacement by first verifying the actual cause of errors, thereby maintaining storage capacity utilization while ensuring data preservation when appropriate.
Data Source
AI summary
According to one embodiment, a failure prognosis device includes circuitry configured to determine whether a sign of failure exists in a head, based on a signal quality value and a floating quantity of the head, the signal quality value being based on an error between a reproducing signal acquired from the head when reading data stored on a storage surface of a disk and a predetermined target signal, and output a determination result.