Adaptive Quality Monitoring Using Value-Based Sample Selection
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Solution Overview
Problem
The cold-start problem in industrial manufacturing hinders the efficient development of data-driven applications for new manufacturing scenarios due to the need for extensive data collection and annotation, leading to time- and cost-intensive processes, with existing adaptive learning methods often resulting in moderate performance and limited adaptability to changing conditions.
Innovation Solution
A monitoring apparatus and method that utilizes a context embedding unit and value estimation unit to pre-select valuable unlabeled data samples, which are then labeled and used to continuously improve a prediction model, enabling faster customization and higher performance in new manufacturing processes by leveraging labeled data from previous scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive data collection and annotation is performed to develop data-driven applications for new manufacturing scenarios, then the model performance and accuracy are improved, but the time and cost required for development increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing embedding indicators for all historical data samples before they are needed. When a new manufacturing scenario is encountered, these pre-computed embedding indicators can be immediately queried and used to select relevant training data, eliminating the need for time-consuming data collection and annotation processes.
Solution Approach 2:
The system creates copies of historical data representations in the form of embedding indicators that capture the essential characteristics of manufacturing processes. These embedding indicators serve as compressed representations that can be quickly compared and matched against new scenarios, enabling fast retrieval of relevant training data without copying the actual raw data.
2Ease of manufacture
If random sampling of data samples is used for model training in cold-start situations, then the development process is simplified, but the model performance deteriorates to moderate levels
Solution Approach 1:
The embedding indicator serves as an intermediary that bridges the gap between new manufacturing scenarios and historical data. By computing embedding indicators for both and comparing them, the system can identify and select historically relevant data samples that are most suitable for training the model in new scenarios, replacing random sampling with intelligent selection.
3Loss of information
If all unlabeled data samples are labeled by domain experts, then complete data annotation is achieved, but the labeling effort and costs increase unnecessarily
Solution Approach 1:
The system extracts only the most relevant data samples for labeling by comparing embedding indicators between new and historical manufacturing scenarios. By identifying and selecting only those samples that have high relevance scores, the system extracts a minimal subset of data that needs to be labeled, leaving the rest unlabeled and thus reducing labeling effort significantly.
Solution Approach 2:
Instead of uniformly labeling all data samples, the system applies selective labeling only to locally relevant samples identified through embedding indicator comparison. This local quality approach ensures that labeling resources are concentrated on the most valuable samples while leaving less critical samples unlabeled.
Data Source
AI summary
A monitoring apparatus for an industrial manufacturing scenario is provided, including a) providing an initial version of a prediction unit, b) obtaining a set of unlabeled input data samples, c) aggregating an embedding indicator, d) aggregating a value indicator for each data sample of the set of unlabeled input data samples, e) selecting a subset of data samples of the set of unlabeled input data samples depending on the value indicator and outputting a labelling request to a labelling unit, f) receiving labels for the subset of data samples in the labelling unit, g) training the current version of the prediction unit resulting in a trained version of the prediction unit, and h) outputting a monitoring result for the set of unlabeled input data samples (Ds) by the trained version of the prediction unit indicating the quality of the supplemented manufacturing to control the manufacturing process.