Method and apparatus for inspecting defects in washer based on deep learning
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Solution Overview
Problem
Conventional methods for inspecting washer conditions are inadequate as they cannot continuously monitor the operation or provide reliable inspection results, especially in varying use environments.
Innovation Solution
A deep learning-based method and apparatus that gather data during washer operation, train artificial neural networks to generate a condition inspection model, and use this model to assess the washer's condition, including features like RPM, gyro, and acceleration data, to determine if the washer is defective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional inspection methods are used to check significant changes in the washer, then the inspection process is simple, but the ability to continuously monitor and detect operation conditions is insufficient
Solution Approach 1:
The patent replaces conventional mechanical inspection methods with a deep learning-based neural network system. Sensors collect operational data (vibration, sound, current) which is processed by trained neural networks to detect washer conditions, substituting physical inspection mechanisms with intelligent data analysis systems.
Solution Approach 2:
The patent introduces neural networks as intermediary components between raw sensor data and inspection conclusions. The neural networks act as mediators that process and interpret operational data, enabling reliable condition assessment without direct mechanical inspection of the washer components.
2Measurement precision
If deep learning models are trained with comprehensive operational data, then inspection accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks with comprehensive operational data before actual inspection. The models are trained offline with various washer conditions (normal, abnormal, defective states) so that during operation, they can quickly process sensor data and provide immediate inspection results without requiring real-time extensive computation.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw sensor data into meaningful features through the neural network processing. The system changes the parameters from raw operational measurements to interpreted condition indicators that the trained models can efficiently evaluate for accurate defect detection.
3Adaptability or versatility
If the washer inspection system adapts to various use environments, then the applicability is improved, but the system complexity and calibration requirements increase
Solution Approach 1:
The patent achieves universality by training neural networks to handle multiple washer conditions and environmental variations with a single integrated system. The deep learning models are designed to process various operational scenarios (different loads, speeds, conditions) using the same architecture, eliminating the need for separate inspection systems for different environments.
Solution Approach 2:
The patent adapts to environmental variations through parameter changes in the training data. The neural networks are trained with operational data collected under diverse conditions (different laundry types, washer speeds, operational modes), enabling the models to automatically adjust and maintain accuracy across varying use environments without manual recalibration.
Data Source
AI summary
Disclosed is a method and apparatus for inspecting defects in a washer based on deep learning. According to an embodiment of the present disclosure, a method for inspecting defects in a washer based on deep learning gathers learning data while the washer operates and trains a first ANN model for diagnosing the condition of the washer and a second ANN model for securing the reliability of the result of inspection of the condition of the washer. Thereafter, the washer may make a diagnosis of whether the washer is defective based on the two pre-trained ANN models and are thereby able to continuously monitor whether the washer has an abnormal condition. According to an embodiment, the artificial intelligence (AI) module may be related to unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.