Thermal Analysis Signal Evaluation with Sliding-Window Effect Detection
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Solution Overview
Problem
Existing thermal analysis methods require manual evaluation of energetic effects, which is time-consuming and lacks automation, and existing automated solutions rely on neural networks, which are computationally intensive and require extensive data sets.
Innovation Solution
A method using an artificial intelligence module with support vector machines or random forest methods to classify thermal effects by analyzing sliding windows of a measurement curve, allowing for automated detection and classification of thermal effects without the need for neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used for thermal analysis, then measurement precision can be maintained through expert judgment, but productivity is reduced due to time-consuming manual analysis
Solution Approach 1:
The system performs automated self-evaluation of thermal analysis data using machine learning models. The artificial intelligence module automatically identifies thermal effects, determines their characteristics, and generates evaluation results without requiring manual expert intervention, thereby maintaining precision while significantly improving productivity
Solution Approach 2:
The patent replaces the manual mechanical evaluation process with an automated computational system. Machine learning models and algorithms substitute human expert analysis, using pattern recognition and data processing to identify thermal effects and their parameters automatically, achieving both speed and accuracy
2Extent of automation
If neural networks are used for automated thermal effect detection, then extent of automation is improved, but use of energy and device complexity increase due to computational requirements
Solution Approach 1:
The system performs preliminary processing of thermal analysis data by dividing measurement curves into segments and extracting relevant features before classification. This preprocessing step reduces the complexity and computational energy required for the subsequent machine learning classification, while maintaining high automation level
Solution Approach 2:
The patent segments the thermal analysis measurement curve into multiple sections or windows, allowing the machine learning model to process smaller, more manageable data portions independently. This segmentation reduces the overall computational energy requirement compared to processing the entire dataset at once, while maintaining comprehensive automation
3Extent of automation
If neural networks are used for automated thermal effect detection, then extent of automation is improved, but device complexity increases due to data set requirements
Solution Approach 1:
The system extracts and uses only the essential features and characteristics from thermal analysis data that are relevant for identifying thermal effects. By selecting and processing only the most important data elements rather than requiring extensive comprehensive datasets, the system achieves high automation with reduced model complexity and fewer data requirements
Data Source
AI summary
A method and a device for evaluating a measuring signal of a thermal analysis. The device has a data interface, which is configured to receive the measuring signal of the thermal analysis, wherein the measuring signal specifies a measurement curve, which is based on a temperature series, and a processing logic. The processing logic is configured to determine a number of sliding windows based on the measuring signal, wherein each sliding window is assigned to a corresponding section of the measurement curve with a number of measuring points, and to determine by means of an artificial intelligence module, which is carried out by the processing logic and which is configured for the classification, whether a thermal effect of a sample material, on which the thermal analysis is based, is present for the respective one of the number of sliding windows, wherein the artificial intelligence module is configured to determine a contiguous section of the thermal effect based on the measurement curve.