Dryer Time Estimation Using Exhaust and Ambient Humidity Sensors
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Solution Overview
Problem
Current laundry appliances inaccurately estimate the remaining drying time due to variations in load size, relying solely on moisture sensors, which often result in longer than displayed times for both standalone and combination machines.
Innovation Solution
A system utilizing exhaust temperature, exhaust humidity, and ambient humidity sensors, combined with a trained recurrent neural network and post-processing block, to accurately predict the estimated time remaining by calculating the difference between exhaust and ambient humidity ratios and elapsed time, while ignoring outlying predictions for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a moisture sensor is used to calculate estimated time remaining, then the system is simple to operate, but the measurement precision is insufficient leading to inaccurate time estimation
Solution Approach 1:
The patent segments the humidity measurement function into two separate sensors: an exhaust humidity sensor for measuring humidity in the exhaust air and an ambient humidity sensor for measuring environmental humidity. This segmentation allows each sensor to be optimized for its specific measurement environment, improving overall measurement precision while maintaining system simplicity through dedicated functional separation.
Solution Approach 2:
The patent introduces the ambient humidity sensor as an intermediary that measures environmental humidity conditions. This intermediary measurement is then used by the controller to compensate for environmental variations in the estimated time remaining calculation, thereby improving accuracy without requiring direct measurement of all affecting factors.
2Measurement precision
If multiple sensors are used to improve estimation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The controller performs multiple functions using the sensor data: it calculates estimated time remaining, displays the information to the user, and potentially adjusts drying parameters. By making the controller multi-functional, the patent avoids adding separate dedicated devices for each function, thereby improving measurement precision through multiple sensors while limiting the increase in overall device complexity.
Solution Approach 2:
The patent merges the exhaust humidity sensor, ambient humidity sensor, and controller into an integrated system where the controller processes data from both sensors to generate the estimated time remaining. This merging consolidates multiple components into a unified system, improving measurement precision through combined sensor input while managing complexity through integration rather than separate standalone units.
3Measurement precision
If a recurrent neural network with post-processing is used, then estimation accuracy improves, but device complexity and processing requirements increase
Solution Approach 1:
The recurrent neural network is trained in advance with historical drying data to learn the complex relationships between humidity, temperature, load size, and drying time. This preliminary training action allows the network to make accurate predictions during operation without requiring complex real-time calculations, thereby improving prediction reliability while managing processing complexity through pre-computed knowledge.
Solution Approach 2:
The post-processing block implements a feedback mechanism where the estimated time remaining is continuously monitored and adjusted based on actual drying progress. This feedback loop allows the system to learn from actual performance and refine its predictions over time, improving accuracy while using relatively simple comparison logic rather than complex real-time optimization algorithms.
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
The system provides a more accurate estimation of the remaining drying time, accounting for load size variations and improving prediction reliability by using machine learning to analyze humidity and temperature data, ensuring precise timing and user convenience.
Implementation Method 1
an exhaust temperature sensor disposed in the exhaust vent generating an exhaust temperature signal
Implementation Method 2
an exhaust humidity sensor disposed in the exhaust vent generating an exhaust humidity signal
Implementation Method 3
an ambient humidity sensor generating an ambient humidity signal
Data Source
AI summary
A laundry appliance includes a cabinet having a laundry compartment located inside said cabinet, an exhaust vent extending from the laundry compartment, an exhaust temperature sensor disposed in the exhaust vent generating an exhaust temperature signal, an exhaust humidity sensor disposed in the exhaust vent generating an exhaust humidity signal and an ambient humidity sensor generating an ambient humidity signal. The appliance further includes a display and a controller coupled to the exhaust temperature sensor, the exhaust humidity sensor and the ambient humidity sensor. The controller determines an estimated time remaining for a drying cycle based on the exhaust temperature signal, the exhaust humidity signal and the ambient humidity signal and causes the display to display the estimated time remaining.