Samsung electronics co., ltd. (20240345906). STORAGE DEVICE PREDICTING FAILURE USING MACHINE LEARNING AND METHOD OF OPERATING THE SAME simplified abstract
Contents
STORAGE DEVICE PREDICTING FAILURE USING MACHINE LEARNING AND METHOD OF OPERATING THE SAME
Organization Name
Inventor(s)
Yongwong Kwon of Suwon-si (KR)
STORAGE DEVICE PREDICTING FAILURE USING MACHINE LEARNING AND METHOD OF OPERATING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240345906 titled 'STORAGE DEVICE PREDICTING FAILURE USING MACHINE LEARNING AND METHOD OF OPERATING THE SAME
The abstract describes a method for predicting failures of storage devices using telemetry data and machine learning models.
- Identifying risk data from telemetry information based on predetermined criteria.
- Inputting data from the risk data into a machine learning model.
- Obtaining anomaly scores from the model to detect anomalies in the data.
- Transmitting alerts to a host when anomalies are detected.
- Receiving feedback from the host to improve the prediction model.
Potential Applications: - Predictive maintenance in storage devices. - Improving reliability and efficiency of storage systems.
Problems Solved: - Early detection of potential failures in storage devices. - Minimizing downtime and data loss.
Benefits: - Cost savings through proactive maintenance. - Enhanced data security and system performance.
Commercial Applications: Title: "Predictive Failure Analysis for Storage Devices" This technology can be used in data centers, cloud storage facilities, and other industries reliant on storage systems.
Prior Art: Research existing methods for predicting storage device failures using telemetry data and machine learning models.
Frequently Updated Research: Stay informed on advancements in predictive maintenance for storage devices and machine learning algorithms.
Questions about Predictive Failure Analysis for Storage Devices: 1. How does this method compare to traditional maintenance approaches? 2. What are the key factors influencing the accuracy of failure predictions in storage devices?
Original Abstract Submitted
a failure prediction method of predicting a failure of a storage device includes: identifying at least a portion of telemetry information, stored in a memory, as risk data based on a predetermined first criterion; inputting first data of a first attribute, among the risk data, to a machine learning model; obtaining a first anomaly score output from the machine learning model; detecting whether an anomaly is present in the first attribute, based on whether the first anomaly score satisfies a predetermined second criterion; transmitting an alert, associated with the first attribute, to a host when an anomaly is detected for the first attribute, among the risk data; and receiving feedback, corresponding to the alert, from the host. the machine learning model may receive the risk data to learn a pattern of data, and may output an anomaly score of the received data based on the learned pattern of the data.
