Deep Learning Device Component Lifecycle Prediction
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Solution Overview
Problem
Existing device management approaches are reactive and fail to consider cross-correlations between device components and attributes, leading to inaccurate replacement policies that decrease productivity and increase costs.
Innovation Solution
The use of deep learning techniques to process telemetry data from enterprise devices, generating predictions for device component lifecycle information and automating actions based on these predictions, such as updating replacement policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If reactive alert-based approaches are used for device component management, then implementation simplicity is maintained, but prediction accuracy and proactive management capability deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously analyzing device telemetry data and generating failure predictions before actual failures occur. The deep learning model processes device attributes and generates predictions about component lifecycle and potential failures, enabling proactive replacement policies to be implemented before failures impact productivity.
2Measurement precision
If deep learning techniques are implemented for processing device attributes, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary deep learning model that acts as a mediator between raw device telemetry data and replacement policy decisions. The model processes multiple device attributes and cross-correlations, transforming complex data into actionable predictions that inform replacement policies without requiring direct complex analysis in the policy enforcement layer.
Solution Approach 2:
The system implements self-service through automated prediction generation and policy recommendation. The deep learning model autonomously analyzes device data, generates failure predictions, and recommends replacement policies without requiring manual intervention for each device assessment, reducing operational complexity despite the sophisticated analysis performed.
3Measurement precision
If cross-correlations between device components and attributes are considered, then replacement policy accuracy improves, but computational requirements increase
Solution Approach 1:
The system segments the analysis by focusing on specific device attributes and their cross-correlations relevant to component failures, rather than processing all possible data. The deep learning model is trained to identify and process only the most predictive attribute combinations, reducing computational requirements while maintaining high replacement policy accuracy.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.