Neural Network Rating System for 3D Object State Assessment
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Solution Overview
Problem
Current methods for assessing the state of mechanical or technical systems are manual, time-consuming, subjective, and prone to errors, with automated approaches being either highly problem-specific or lacking the accuracy of human expert assessments.
Innovation Solution
A neural network-based rating system that uses a training data record comprising multiple state data points from three-dimensional training objects to adapt and calculate state ratings, eliminating the need for application-specific feature definition and enabling fully data-driven assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment by experts is used, then assessment accuracy is maintained, but time consumption increases and subjectivity remains
Solution Approach 1:
The system performs preliminary extraction of application-specific features from tensor field data before assessment, enabling automated rule-based evaluation. This preprocessing step allows the assessment system to operate automatically without requiring expert manual intervention, thereby reducing time consumption while maintaining assessment capability through structured feature analysis
Solution Approach 2:
The patent introduces an intermediary automated assessment system that bridges the gap between raw tensor field data and expert-level assessment conclusions. This intermediary system uses rule-based automation with extracted features to replicate expert decision-making processes, reducing dependency on manual expert assessment while preserving assessment accuracy through systematic rule application
2Productivity
If rule-based automation is implemented, then time consumption is reduced, but accuracy lags behind human expert assessment
Solution Approach 1:
The system applies local quality by focusing rule-based automation on specific extracted features and regions of interest within the tensor field data. Rather than attempting to automate the entire assessment process uniformly, the system identifies and applies rules to locally extracted features that are most critical for accurate assessment, thereby improving overall accuracy while maintaining efficiency
Solution Approach 2:
The patent changes parameters by dynamically adjusting which features are extracted and which rules are applied based on the specific assessment context. This adaptive parameter selection allows the rule-based system to optimize its accuracy for different assessment scenarios, closing the gap between automated and expert assessment quality
3Extent of automation
If application-specific features are extracted for machine learning, then automation is achieved, but feature engineering complexity increases and accuracy decreases
Solution Approach 1:
The system extracts only the essential application-specific features from the tensor field data that are necessary for assessment, rather than attempting to process the complete raw data set. This selective extraction reduces the complexity of feature engineering by focusing on the most relevant characteristics while maintaining sufficient automation capability for accurate assessment
Data Source
AI summary
A method for rating a state of a three-dimensional test object by taking into consideration a prescribed assessment task and by using a rating system comprising a neural network, wherein a training data record comprising multiple state data points from one or more three-dimensional training objects is provided and/or used, wherein the training data record comprises a known state rating in regard to the prescribed assessment task for each of the state data points, wherein the neural network of the rating system is parameterized in a training process by using the training data record in order to adapt the rating system to the prescribed assessment task, and wherein a state rating for a prescribed state data point of the test object is calculated with the adapted rating system in an execution process. In addition, a corresponding rating system and a computer program product are disclosed.