Infrared Camera Component Defect Detection and Cause Analysis
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Solution Overview
Problem
Conventional methods for detecting defective components in production processes primarily focus on identifying defects rather than determining their cause, and they do not utilize infrared cameras effectively for real-time heat measurement in fully developed components.
Innovation Solution
A method and apparatus that use an infrared camera to measure the temperature of components during the production process, compare the measured temperature with reference values, and generate distribution data to identify defective components and determine the cause of defects by consulting a cause-of-defect database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional test signal methods are used to detect defective components, then defect identification is achieved, but the cause of defect cannot be determined
Solution Approach 1:
The patent introduces thermal imaging technology as an intermediary measurement method. Instead of directly testing electrical signals, the system uses thermal cameras to capture heat distribution patterns as an intermediate indicator that reflects both component functionality and potential defects. This intermediary measurement enables indirect observation of internal component states without direct electrical contact, thereby preserving diagnostic information.
Solution Approach 2:
The patent changes the measurement parameter from electrical test signals to thermal parameters. By measuring temperature distribution and thermal characteristics instead of electrical responses, the system gains access to different physical manifestations of component behavior. This parameter transformation allows detection of defects that may not be apparent through conventional electrical testing while providing thermal signatures that can indicate specific failure modes.
2Reliability
If infrared camera is used for heat measurement in production process, then defect detection capability is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent implements self-service through automated image processing and analysis algorithms. The system automatically captures thermal images, processes the thermal data, compares measurements against reference values, and generates defect identification results without requiring manual interpretation. This automation reduces the skill level required for operation while maintaining high detection reliability, effectively resolving the contradiction between improved capability and increased measurement difficulty.
Solution Approach 2:
The patent incorporates feedback mechanisms where measurement results are automatically compared against predefined reference values and thresholds. The system provides real-time feedback on component status, enabling operators to quickly identify and respond to defects. This structured feedback approach simplifies the measurement process by providing clear decision criteria, reducing the complexity of interpreting infrared data while maintaining high detection reliability.
3Ease of operation
If only normal/defective classification is performed, then simple identification is achieved, but cause analysis function is missing
Solution Approach 1:
The patent segments the defect analysis process into multiple hierarchical levels. First, components are classified as normal or defective based on thermal characteristics. Then, for defective components, the system further segments the analysis by identifying specific thermal patterns and comparing them against a database of known defect signatures. This segmentation enables both simple initial classification and detailed cause analysis, resolving the contradiction between operational simplicity and information completeness.
Solution Approach 2:
The patent adds another dimension to the analysis by incorporating spatial thermal distribution patterns alongside basic temperature measurements. Instead of solely classifying components as normal or defective, the system analyzes the spatial characteristics of heat distribution, allowing it to differentiate between various types of defects based on their unique thermal signatures. This dimensional expansion enables cause analysis while maintaining ease of operation through automated pattern recognition.
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
Effectively identifies defective components and determines their causes, improving defect detection accuracy and enabling targeted retesting and production line optimization.
Implementation Method 1
measuring a temperature of the test-target component, to which the driving power is applied, with an infrared camera
Data Source
AI summary
Disclosed is an apparatus for detecting a defective component includes an infrared camera configured to capture an image of a test-target component in a production process, and a processor configured to measure a temperature of the component based on the image captured by the infrared camera and identify that the component of which temperature is out of a range of a reference value is defective. In addition, a database of various causes of defect is established, and an expected cause of defect for a defective component or a retest component is reasoned out from the database based on the measured temperature.