Laundry Dryer Motor Data Analysis for Hard-to-Dry Item Detection
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Solution Overview
Problem
Existing laundry treating appliances struggle with accurately determining load size and identifying heavy or hard-to-dry items, leading to inefficient drying cycles and potential damage to fabrics due to subjective user input and delayed detection.
Innovation Solution
A method using motor torque and speed data analysis, combined with logistic regression, to predict the presence of heavy and/or hard-to-dry items, adjusting cycle parameters and detecting airflow blockages for improved drying efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input is used for laundry load size, then the user interface is simple, but the determination accuracy is low due to subjective estimation
Solution Approach 1:
The system enables the dryer to automatically determine laundry load size and detect hard-to-dry items using motor data without requiring user input or additional sensors. The motor itself serves as the detection device, and the system self-calibrates using nominal logistic regression models trained on historical data, eliminating the need for external measurement devices.
Solution Approach 2:
The patent replaces manual user input (mechanical interaction) with automated electronic detection using motor current and speed data. The mechanical estimation process is substituted with computational analysis of electrical parameters, achieving objective and accurate load characterization without physical intervention.
2Measurement precision
If traditional motor control is used, then the control system is simple, but the detection of hard-to-dry items is delayed or inaccurate
Solution Approach 1:
The system continuously monitors motor current and speed data throughout the drying cycle, comparing real-time measurements against predicted values from the nominal logistic regression model. When deviations indicate the presence of hard-to-dry items, the system provides feedback to adjust cycle parameters, enabling dynamic adaptation and accurate detection without additional hardware.
Solution Approach 2:
The patent transforms the approach by changing from fixed cycle parameters to dynamic parameters adjusted based on real-time motor data analysis. The system monitors changes in current and speed patterns that correlate with hard-to-dry item presence, using statistical models to interpret these parameter variations and trigger appropriate responses.
3Productivity
If fixed cycle parameters are used, then the operation cycle is simple to control, but the drying efficiency is reduced due to inability to adapt to load variations
Solution Approach 1:
The system transitions from static fixed cycle parameters to dynamic adjustable parameters that adapt in real-time based on detected load characteristics. When hard-to-dry items are identified through motor data analysis, the controller automatically modifies cycle length, temperature, and agitation patterns, enabling the drying process to optimize for different load compositions without user intervention.
Solution Approach 2:
The patent introduces a computational intermediary layer (the nominal logistic regression model and control algorithm) that sits between the motor and the cycle control system. This intermediary processes motor data, detects load characteristics, and translates findings into appropriate cycle parameter adjustments, bridging the gap between simple motor operation and complex adaptive control.
Data Source
AI summary
Hard-to-dry items are detected in a laundry load. Drum motor data is captured from a motor powering a rotating drum of a laundry appliance. Moving range (MR) and standard deviation (STD) are determined from the drum motor data. A hard-to-dry item model is used to predict presence or absence of heavy and/or hard-to-dry items in the laundry load, the hard-to-dry item model being determined using a nominal logistic regression of at least the MR and STD of the drum motor data in situations having presence or absence of heavy and/or hard-to-dry items. Cycle parameters for a cycle of operation of the laundry appliance are updated based on the prediction by the hard-to-dry item model of presence or absence of heavy and/or hard-to-dry items.