Storage Device Failure Prediction via Log Analysis

The patent applies preliminary action by collecting log data from storage device components and analyzing it with machine learning models before failures occur. The system proactively identifies potential failures by examining patterns in log data such as temperature, power consumption, and operational statistics, enabling preventive measures to be taken before actual failures impact system reliability.

The patent implements feedback by continuously monitoring log data from storage components and using machine learning analysis to generate failure possibility assessments. This feedback loop provides ongoing information about component health status, allowing the system to adapt and respond to changing conditions that may indicate impending failures, thereby improving proactive reliability management.

The patent applies universality by designing the machine learning module to perform multiple functions: collecting log data from various sources, analyzing patterns across different component types, predicting failures for multiple components simultaneously, and providing comprehensive health assessments. This multi-functional approach consolidates what could be multiple separate systems into a single versatile module.

The patent implements self-service by enabling the storage device to autonomously monitor its own components, collect its own log data, and perform self-diagnosis through machine learning analysis. The system automatically identifies potential failures without requiring external intervention, allowing the device to manage its own reliability assessment and trigger appropriate responses.