Predicting Tintable Window Failures Using AI

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Solution Overview

Problem

Electrochromic tintable windows face challenges such as malfunction identification, maintenance, and replacement, which are costly, time-consuming, and labor-intensive, especially in large facilities with multiple windows.

Innovation Solution

A system utilizing data from tintable window controller systems in conjunction with a learning module, including artificial intelligence and machine learning, to predict and identify malfunctions by analyzing current and voltage data associated with tint transitions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual identification and maintenance of malfunctioning tintable windows is performed, then maintenance can be carried out, but it becomes expensive, time-consuming, and labor-intensive especially in large facilities

Engineering Contradiction:

Improvemaintenance efficiencyVSAvoidtime required for maintenance and replacement

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automatic self-diagnosis of tintable windows by having them report their own operational status, voltage levels, and current draw data to a central server, eliminating the need for manual inspection and enabling automated failure prediction

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where window controllers report operational data to a server, which analyzes the data and sends alerts back to facility managers when anomalies or potential failures are detected, enabling proactive maintenance

Inventive Principle:

Principle #23Feedback

2Loss of time

If replacement windows are manufactured in advance, then replacement time is reduced, but inventory management becomes complex and capital-intensive

Engineering Contradiction:

Improvereplacement timeVSAvoidinventory requirements

Core Design Contradiction:

Loss of timeVSQuantity of substance

Solution Approach 1:

The system performs preliminary detection and prediction of window failures before they occur, allowing facility managers to plan and schedule replacements during convenient times without disrupting operations, eliminating the need for large inventories of pre-manufactured replacement windows

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If continuous monitoring of all tintable windows is implemented, then failure prediction accuracy is improved, but data processing complexity and computational resources increase

Engineering Contradiction:

Improvefailure prediction accuracyVSAvoiddata processing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts only the most critical diagnostic parameters from window controllers, such as voltage levels, current draw, and operational status, filtering out unnecessary data to reduce processing complexity while maintaining high prediction accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The monitoring system is segmented into distributed window controllers that collect local data, a central server that aggregates and analyzes data from multiple windows, and a user interface layer, allowing scalable deployment without overwhelming computational complexity

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12271868B2Failure prediction of at least one tintable window

Publication Date: 2025.04.08 VIEW OPERATING CORP

AI summary

Data from measurements is used in conjunction with a learning module to identify and predict tintable window malfunctions. The measurements can be based at least in part on data accumulated during regular operation of a tintable window.