Tintable Window Failure Prediction Using Controller Data
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Solution Overview
Problem
Tintable windows, such as electrochromic windows, suffer from malfunction issues that are difficult to detect and address, leading to inefficiencies and high maintenance costs, especially in large facilities.
Innovation Solution
A system utilizing data from tintable window controllers, combined with a learning module, analyzes measurements to predict and identify potential malfunctions by recognizing incomplete or uncharacteristic tint transitions, employing machine learning and sensors to provide early alerts and schedule maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance methods are used for tintable windows, then maintenance can be performed when windows are visibly malfunctioning, but maintenance costs and downtime increase due to reactive rather than predictive approach
Solution Approach 1:
The system performs preliminary actions by continuously monitoring window performance parameters (voltage, current, tint transition characteristics) and predicting failures before they occur. The machine learning model analyzes historical data to identify early signs of degradation, enabling maintenance scheduling in advance rather than waiting for visible malfunction.
Solution Approach 2:
The system implements feedback by continuously collecting operational data from window controllers, comparing actual performance against predicted patterns, and adjusting maintenance predictions accordingly. The feedback loop includes monitoring tint transition completeness, voltage-current relationships, and detecting anomalies that indicate impending failure.
2Measurement precision
If manual inspection and maintenance of tintable windows is performed, then maintenance can be conducted based on visible symptoms, but detection precision and early warning capability are insufficient
Solution Approach 1:
The system uses intermediary elements including machine learning models that act as mediators between raw sensor data and maintenance decisions. The ML models process complex multi-parameter data (voltage, current, tint transitions) and translate them into actionable predictions, bridging the gap between operational data and maintenance requirements.
Solution Approach 2:
The patent replaces manual mechanical inspection with automated electronic monitoring and data-driven prediction systems. Instead of physically examining windows for visible defects, the system uses sensors and algorithms to detect subtle electrical and optical parameter changes that indicate impending failure.
3Productivity
If reactive maintenance is performed after window malfunction becomes visible, then maintenance can be scheduled based on actual needs, but facility productivity and energy efficiency are reduced due to window failures
Solution Approach 1:
The system takes preliminary action by predicting window failures before they impact facility productivity. By analyzing degradation trends in electrical and optical parameters, the system schedules maintenance proactively, preventing window failures that would reduce facility energy efficiency and productivity.
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 identification and scheduling of maintenance for tintable windows, reducing downtime and maintenance costs by predicting failures before they become visible, thus ensuring timely replacement and minimizing operational disruptions.
Implementation Method 1
Electrochromism is a phenomenon in which a material exhibits a reversible electrochemically-mediated change in an optical property when the material is placed in a different electronic state, e.g., by being subjected to a voltage change.
Data Source
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.