Automated Product Error Identification Through Test-Value Clustering
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Solution Overview
Problem
Existing methods fail to provide a fully automated identification of product defects and their causes in complex products due to the large number of parts, diverse suppliers, and numerous manufacturing steps, making it difficult to efficiently identify and address defects in products like vehicle transmissions.
Innovation Solution
A method involving dimension reduction of n-dimensional test values using statistical processes like t-SNE, comparing these values with learned reference values, and assigning them to groups to automatically identify product defects and their causes, enabling comprehensive and automated defect identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical processing of large numbers of measurement data is performed to identify defective products, then measurement precision is improved, but loss of time increases due to complex data processing requirements
Solution Approach 1:
The patent pre-processes and stores measurement data from multiple sources (vibration, temperature, pressure, flow, electrical signals) before actual defect detection occurs. By accumulating and organizing baseline data during normal operation, the system prepares reference information in advance that enables rapid defect identification without requiring complex real-time statistical processing when defects occur.
Solution Approach 2:
The patent creates simplified representations of complex measurement data by generating feature vectors that capture essential characteristics of product behavior. These feature vectors serve as compressed copies of the full measurement datasets, enabling rapid comparison and defect identification without processing the complete original data, thus reducing computational time while maintaining detection accuracy.
2Reliability
If comprehensive test information is collected from multiple manufacturing steps and suppliers, then reliability of defect identification is improved, but device complexity increases due to integrating diverse data sources
Solution Approach 1:
The patent implements a universal data processing platform that handles multiple types of measurement data (vibration, temperature, pressure, flow, electrical signals) from various manufacturing steps and suppliers through a single integrated system. This multi-functional approach consolidates diverse data sources into one cohesive defect detection system, reducing overall complexity while maintaining comprehensive coverage of all product elements throughout the manufacturing process.
Solution Approach 2:
The patent transforms heterogeneous measurement data from different sources and formats into a unified parameter representation through feature extraction and normalization. By converting diverse physical measurements into standardized feature vectors with consistent dimensions and scales, the system simplifies data integration without losing important defect-related information from any source.
3Measurement precision
If manual failure analysis is performed to identify specific product defects and causes, then measurement precision is improved, but productivity decreases due to time-consuming analysis processes
Solution Approach 1:
The patent replaces manual failure analysis with an automated computer-based system that uses machine learning algorithms and pattern recognition to identify defect causes. The system automatically compares measured feature vectors against stored reference data and previously identified defect patterns, eliminating the need for manual inspection and analysis while maintaining or improving diagnostic accuracy through consistent, repeatable automated decision-making processes.
Solution Approach 2:
The patent enables the defect detection system to automatically identify and diagnose defects without requiring manual intervention. The system self-calibrates by learning from historical data, automatically updates defect detection thresholds, and provides real-time defect identification and cause analysis, freeing operators from time-consuming manual failure analysis tasks while maintaining high diagnostic precision.
Data Source
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AI summary
The invention relates to a method for automatically identifying a product error in a product (1, 2, 3, 40, 41, 42, 43, 44, 45) and/or for automatically identifying a product error cause of the product error, comprising the steps: - producing the product (1, 2, 3, 40, 41, 42, 43, 44, 45, 100) from a multiplicity of product elements (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18) by way of a multiplicity of manufacturing steps, and - detecting a number n of items of test information by means of at least one product test (101), wherein the n items of test information form an n-dimensional test value. The method according to the invention is characterized by the steps: - carrying out dimension reduction of the n-dimensional test value (102) by means of at least one statistics process to obtain a dimension-reduced test value, - comparing the dimension-reduced test value (103) to a multiplicity of learned reference values (46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60), - assigning the dimension-reduced test value to at least one group of reference values (46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 104) that are similar to each other, and - automatically identifying the product error (105) and/or the product error cause (106) on the basis of the assignment. The invention further relates to a corresponding device.