Dishwasher Filter Clogging Detection Using Drainage Sound Analysis
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Solution Overview
Problem
Existing dishwasher technologies fail to accurately and efficiently notify users about filter clogging in real-time, leading to reduced cleaning performance due to the need for separate pressure measurement devices and limited user notification when clogging occurs.
Innovation Solution
A method and system that analyze the degree of filter clogging by learning the drainage sound of washing water and generating an alarm when the closure exceeds a predetermined level, using a microphone to extract the drainage sound signal and a pre-trained neural network model to determine the filter's operational state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate pressure measurement device is installed to detect filter clogging, then the detection capability is improved, but the device complexity increases
Solution Approach 1:
The patent replaces the mechanical pressure measurement device with an acoustic detection system using a microphone and neural network. The system detects filter clogging by analyzing drainage sound characteristics (frequency, amplitude, time-domain features) instead of measuring pressure directly, thereby eliminating the need for complex pressure sensors while maintaining detection capability
Solution Approach 2:
The patent introduces sound waves as an intermediary medium to detect filter clogging. Instead of directly measuring pressure, the system uses drainage sound as a mediator that carries information about filter blockage state, which is then processed by a neural network to determine clogging level
2Device complexity
If the filter clogging detection is performed only when pressure reaches a predetermined level, then the device complexity is reduced, but the user notification timing is delayed
Solution Approach 1:
The patent performs preliminary detection of filter clogging by analyzing drainage sound characteristics during the drainage process, before the filter reaches critical blockage levels. The neural network continuously monitors sound features and can alert users in advance, enabling proactive filter maintenance before cleaning performance deteriorates
Solution Approach 2:
The patent implements continuous feedback through real-time analysis of drainage sound during the drainage process. The system provides ongoing information about filter status rather than a single threshold-based notification, allowing users to understand the progressive clogging state and take timely action
3Productivity
If the drainage sound analysis is performed during active washing operations, then the real-time monitoring capability is improved, but the measurement precision deteriorates due to noise interference
Solution Approach 1:
The patent employs periodic action by analyzing drainage sound at specific intervals during the drainage process rather than continuously during all washing operations. The system captures sound data at designated drainage phases, processes it through the neural network, and provides updates at appropriate moments, balancing real-time monitoring with noise rejection
Solution Approach 2:
The patent extracts the drainage sound signal from the complex acoustic environment by focusing specifically on the drainage phase. The system isolates and analyzes only the relevant drainage sound portion, separating it from other washing operation noises, thereby improving measurement precision through selective signal extraction
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 real-time monitoring and notification of filter clogging, allowing users to replace or clean the filter promptly, thereby maintaining optimal cleaning performance and preventing noise interference during analysis.
Implementation Method 1
obtaining a drainage sound signal generated by draining washing water through the filter when the dishwasher operates
Implementation Method 2
analyzing the degree of closure of the filter based on a signal corresponding to the drainage sound by matching the drainage sound generated when the dishwasher operates and the learned drainage sound
Data Source
AI summary
A method and a dishwasher capable of analyzing the closure state of the filter of the dishwasher by using a deep neural network model trained through machine learning of artificial intelligence is provided. The dishwasher may include a microphone configured to obtain a drainage sound signal generated by draining washing water through the filter during operation of the dishwasher, a processor configured to analyze the degree of closure of the filter based on the drainage sound signal, and an alarm generator configured to generate an alarm if the degree of closure is a greater than or equal to a predetermined level. The operational state of the dishwasher may be analyzed based on a drainage sound of the washing water drained to the filter by the degree of closure of the filter.