Server communicating with dishwasher
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Solution Overview
Problem
Dishwashers experience frequent defects due to complex operations like water supply, spraying, heating, and draining, making it difficult for customers to identify the cause of issues, leading to prolonged repair times and incorrect diagnoses by untrained repair personnel.
Innovation Solution
A server communicates with the dishwasher to train a neural network using operation information, determining the cause of defects and providing guidance to users or service providers, reducing the need for on-site visits and enabling pre-emptive repair preparations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a dishwasher performs various complicated operations such as water supply, spraying, heating, draining, and drying cycles, then the cleaning function is improved, but the reliability deteriorates because defects frequently occur
Solution Approach 1:
The patent introduces a server as an intermediary between the dishwasher and the user/service center. The server receives operation information from the dishwasher, trains a neural network model to analyze defect patterns, and provides diagnostic results. This intermediary system handles the complexity of defect analysis externally, allowing the dishwasher itself to remain relatively simple while improving reliability through intelligent monitoring and prediction capabilities.
2Loss of time
If the customer detects the defect and requests repair from a service center, then the repair process is initiated, but the time until repair is performed increases due to detection delay and dispatch time
Solution Approach 1:
The system performs preliminary defect diagnosis by training a neural network model with operation information before the actual repair occurs. The server analyzes operation data, identifies potential defects, and determines their causes in advance. This preliminary analysis provides defect cause information to the service center before the repair worker arrives, enabling faster and more accurate repair execution without requiring extended on-site diagnosis time.
3Ease of repair
If the repairer operates the dishwasher directly on the spot or disassembles it to determine the cause of defects, then the defect cause can be identified, but the repair efficiency decreases and untrained repairers may have difficulty
Solution Approach 1:
The system implements a feedback mechanism where operation information from the dishwasher is continuously collected and fed into a neural network model trained on historical defect data. The model processes this feedback information, compares it against known defect patterns, and provides accurate defect cause identification. This feedback loop enables even untrained repairers to obtain precise diagnostic results without needing extensive technical knowledge or time-consuming manual testing procedures.
Data Source
AI summary
A server communicating with a dishwasher is disclosed. The server communicating with a dishwasher, according to an embodiment of the present invention, comprises: a communication unit for communicating with one or more dishwashers; a memory for storing learning results obtained using a plurality of failure causes and operation information corresponding to each of the plurality of failure causes; and a processor for receiving operation information of a particular dishwasher among the one or more dishwashers from the particular dishwasher, and acquiring the cause of a failure occurring in the particular dishwasher by using the operation information of the particular dishwasher and the learning results, wherein the learning results include relational parameters corresponding to the relationships between the plurality of failure causes and the operation information.