Predictive Insulation Condition Monitoring for Cable Leakage

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Solution Overview

Problem

Current insulation layers in cable-powered electronic devices deteriorate over time and due to environmental conditions, leading to potential current leakage, which can damage equipment and pose safety risks.

Innovation Solution

A predictive insulation condition recommendation policy using a supervised machine learning model that determines the insulation level and generates a decision framework based on current leakage and confidence scores, recommending actions such as repair or replacement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the insulation layer is used to conduct current, then the device can operate normally, but current leakage increases as the insulation layer deteriorates

Engineering Contradiction:

Improveinsulation effectivenessVSAvoidcurrent leakage

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system performs preliminary insulation testing and machine learning analysis to predict future insulation deterioration and current leakage risks before they become hazardous. By proactively identifying degradation trends, the system enables preventive maintenance actions that eliminate harmful current leakage before it can damage equipment or pose safety risks.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors insulation conditions, analyzes test results through machine learning models, and provides feedback recommendations for maintenance actions. This closed-loop feedback mechanism tracks insulation degradation over time and adjusts maintenance recommendations based on predicted future conditions, effectively preventing current leakage by addressing insulation issues before they become critical.

Inventive Principle:

Principle #23Feedback

2Reliability

If insulation testing is performed frequently to detect deterioration early, then safety is improved, but device downtime and operational disruption increase

Engineering Contradiction:

ImprovesafetyVSAvoiddevice downtime

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs partial insulation testing at scheduled intervals rather than continuous testing, using machine learning to analyze the partial data sets and predict overall insulation conditions. This partial action approach provides sufficient safety monitoring while minimizing device downtime and operational disruption.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The machine learning model performs preliminary analysis of insulation test results to predict future deterioration trends, enabling the system to determine when full testing is necessary versus when scheduled maintenance can proceed without extensive re-testing. This reduces unnecessary device downtime while maintaining safety.

Inventive Principle:

Principle #10Preliminary action

3Object-generated harmful factors

If the insulation layer resistance is high, then current leakage is minimal, but the insulation layer deteriorates over time due to age and environmental conditions

Engineering Contradiction:

Improvecurrent leakage levelVSAvoidinsulation layer lifespan

Core Design Contradiction:

Object-generated harmful factorsVSDuration of action of stationary object

Solution Approach 1:

The system establishes a feedback loop that continuously monitors insulation resistance, tracks degradation trends over time, and provides feedback recommendations for maintenance interventions. By monitoring the decline in insulation resistance and acting before it reaches hazardous levels, the system extends the effective lifespan of the insulation layer while maintaining low current leakage.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model performs preliminary prediction of insulation deterioration based on environmental conditions and usage patterns, enabling proactive maintenance actions that preserve insulation integrity and extend its service life before natural deterioration causes harmful current leakage.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20230401462A1Predicting device insulation condition and providing optimal decision model

Publication Date: 2023.12.14 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method for a predictive insulation condition-based recommendation policy includes determining a present insulation level for an insulation layer on a cable providing power to an electronic device based on a current leakage test. The method also includes determining a current leakage and a confidence score for the insulation layer on the cable providing power to the electronic device utilizing a supervised machine learning model. The method also includes generating a decision framework for the electronic device based on the current leakage and the confidence score in response to determining to perform an action based on the decision framework for the electronic device, the method also includes performing the action based on the decision framework to address the current leakage for the insulation layer on the cable providing power to an electronic device.