IoT Device Lifespan Prediction Using Machine Learning

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Solution Overview

Problem

In multi-user enterprise environments, devices reach the end of their lifespan without proactive notification, leading to reactive maintenance approaches that negatively impact user experience and productivity.

Innovation Solution

Implementing a machine learning-based system that automatically obtains device telemetry data from IoT-enabled devices to determine lifespan-related information and initiate automated actions, such as predictive maintenance, by applying machine learning models like logistic regression to predict end-of-life scenarios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional reactive maintenance approaches are used, then device management is simpler, but user experience and productivity deteriorate due to unexpected outages

Engineering Contradiction:

Improvedevice availabilityVSAvoidmaintenance system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by continuously monitoring device telemetry data and applying machine learning models to predict end-of-life conditions before they occur. This enables proactive scheduling of maintenance activities, preventing unexpected outages and improving device availability while maintaining manageable system complexity through automated predictions.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If proactive predictive maintenance is implemented, then user experience and productivity improve, but system complexity and computational requirements increase

Engineering Contradiction:

Improvedevice uptimeVSAvoidmaintenance system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system enables self-service by allowing devices to automatically transmit their own telemetry data and by using machine learning models that autonomously analyze this data to generate predictions. This automation reduces manual intervention requirements and manages system complexity while significantly improving device uptime through proactive maintenance scheduling.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If machine learning models are applied to device telemetry data, then lifespan prediction accuracy improves, but data processing time and computational resources increase

Engineering Contradiction:

Improveend-of-life prediction accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial action by focusing machine learning analysis on specific critical telemetry parameters most indicative of end-of-life conditions rather than processing all available data equally. This selective approach maintains high prediction accuracy while reducing overall data processing time and computational resource requirements.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11201801B2Machine learning-based determinations of lifespan information for devices in an internet of things environment

Publication Date: 2021.12.14 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for machine learning-based determinations of lifespan information for devices in an Internet of Things (IoT) environment are provided herein. An example computer-implemented method includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network, automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data, and initiating at least one automated action in response to the determined lifespan-related information.