AI Audio Monitoring for Early Device Failure Prediction
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Solution Overview
Problem
Conventional device management approaches in data centers and cloud environments rely on reactive analysis of text-based log information, leading to data loss, device downtime, and energy inefficiencies due to the gradual failure and degradation of hardware devices.
Innovation Solution
The implementation of an audio data-based device failure prediction method using artificial intelligence techniques, which involves obtaining audio data from devices, modifying it using data processing techniques, classifying potential failures into device failure-related categories, and performing automated actions based on the classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive analysis of text-based log information is used, then device management can be performed, but device downtime and data loss occur due to gradual failure and degradation
Solution Approach 1:
The patent applies preliminary action by analyzing audio data to detect early signs of device degradation and failure before they occur. The system continuously monitors audio signals from devices, identifies anomalies indicating potential failures, and triggers alerts or automated responses in advance, allowing maintenance to be performed before actual failure and downtime occur.
2Reliability
If reactive analysis of text-based log information is used, then device management can be performed, but data loss occurs due to gradual failure and degradation
Solution Approach 1:
The system performs preliminary detection of device failures through audio analysis before data loss occurs. By identifying degradation patterns in audio signals early in the failure process, the system can trigger preventive actions such as data backups, device replacement, or maintenance interventions that prevent data loss entirely.
3Loss of energy
If reactive analysis of text-based log information is used, then device management can be performed, but energy inefficiencies occur due to gradual failure and degradation
Solution Approach 1:
The patent applies preliminary action by detecting device degradation through audio analysis before complete failure occurs. This allows for timely maintenance or replacement of failing devices, preventing them from continuing to operate inefficiently and consume excessive energy. The system can proactively optimize energy usage by addressing degradation issues before they lead to complete device failure and total energy waste.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for audio data-based device failure prediction using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining audio data associated with at least one device; modifying at least a portion of the obtained audio data using one or more data processing techniques; predicting at least one failure associated with the at least one device by classifying, into at least one of multiple device failure-related categories, at least a portion of the modified audio data using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least a portion of the modified audio data.