Resistive Coating Life Prediction From Resistance Drift
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Solution Overview
Problem
Existing resistive coatings in windshields and windows deteriorate over time due to prolonged use or damage, leading to potential cracking, breaking, and emergency situations without effective prediction or monitoring systems.
Innovation Solution
A system utilizing a measurement sensor, computer-readable memory, and processor to determine a resistive coating's present resistance value, age, and apply a linear model to predict its remaining usable life, providing alerts and updates for timely replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If a resistive coating is used in a window heating system, then the window can be heated to remove moisture and improve visibility, but the coating deteriorates over time due to prolonged use or damage
Solution Approach 1:
The system performs preliminary action by continuously monitoring the resistance of the resistive coating and predicting its remaining usable life before actual failure occurs. The processor applies a linear model to resistance measurements taken at different times to determine when the coating will deteriorate beyond acceptable limits, allowing proactive replacement scheduling and preventing emergency situations from cracking or breaking.
2Measurement precision
If the resistive coating is monitored continuously, then the remaining usable life can be predicted accurately, but the system complexity increases
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with an electrical measurement approach. Instead of using sophisticated sensors or mechanical inspection methods, the system simply measures the electrical resistance of the coating at different times and uses a linear model in the processor to predict remaining life. This substitution of electrical measurement for mechanical monitoring achieves accurate prediction while keeping the system relatively simple.
3Ease of manufacture
If the coating is replaced only after failure, then maintenance costs are reduced, but safety risks increase due to cracking and breaking
Solution Approach 1:
The system implements feedback by continuously measuring the resistance of the resistive coating and using this information to predict when the coating will deteriorate beyond acceptable limits. The processor provides feedback about the remaining usable life, allowing maintenance to be scheduled proactively before failure occurs. This feedback mechanism balances maintenance costs and safety risks by enabling replacement at the optimal time - not too early (wasting resources) and not too late (risking failure).
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables proactive maintenance by predicting the coating's failure before it occurs, preventing cracking and arcing events, ensuring safety and reducing unscheduled maintenance.
Implementation Method 1
determine a present resistance value for the coating; receive information about a present age of the article and/or coating
Implementation Method 2
configured to produce heat when electrical current passes through the coating or film
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
A system for predicting remaining usable life of an article including a resistive coating includes computer-readable memory with a predictive model predicting changes in resistance of the resistive coating over time determined based on a plurality of past resistance measurements for the resistive coating and a processor. The processor is configured to: receive and process data representative of a sensed electrical property of the resistive coating from a measurement sensor electrically connected to the resistive coating to determine a present resistance value for the coating; receive information about a present age of the article and/or coating; and determine an estimated remaining usable life of the article by applying the received information about the present age of the coating and/or article, the determined present resistance value for the coating, and a predetermined end-of-use resistance value for the coating to the predictive model of the computer-readable memory.