Inference Workflow for Sensor-Aware Manufacturing Defect Analysis
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Solution Overview
Problem
Existing inference systems face inefficiencies due to the need to manually prioritize sensor data and user input, leading to time-consuming processes and increased costs in making inferences about manufacturing defects and countermeasures.
Innovation Solution
An inference apparatus that classifies questions as qualitative or quantitative, determines the use of sensor data or user input based on acquirability, and uses a determiner to select the appropriate data for inference, thereby streamlining the inference process without increasing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data and user input are manually prioritized for each monitoring target device, then the inference accuracy is improved, but the preparation time and complexity increase
Solution Approach 1:
The system performs preliminary classification of questions into qualitative and quantitative types, and pre-establishes the correspondence between question types and data sources (sensor data for quantitative, user input for qualitative). This preliminary preparation eliminates the need for manual prioritization during actual inference operations, resolving the contradiction between inference accuracy and preparation time.
Solution Approach 2:
The inference apparatus automatically determines which data source to use based on the question type classification, without requiring manual configuration or prioritization settings. The system serves itself by autonomously selecting appropriate data sources, thereby eliminating time-consuming manual setup while maintaining accurate inference results.
2Reliability
If sensor data is always prioritized for quantitative questions, then the objectivity of inference is improved, but the system becomes complex when sensors are unavailable
Solution Approach 1:
The system dynamically adjusts data source selection based on sensor availability. When sensors are available, quantitative questions use sensor data for objective inference; when sensors are unavailable, the system automatically switches to user input. This dynamic adaptability maintains inference objectivity while avoiding system complexity through straightforward conditional logic.
Solution Approach 2:
The system changes the data source parameter based on sensor availability status. For quantitative questions, the data source parameter switches between sensor data (when available) and user input (when unavailable), maintaining inference objectivity without requiring complex system architecture.
3Measurement precision
If manual prioritization of data sources is performed for each device, then the inference precision is improved, but the implementation cost increases
Solution Approach 1:
The inference apparatus implements a universal question classification mechanism that works across all monitoring target devices regardless of sensor availability. The same classification logic and data source selection rules apply universally, eliminating the need for device-specific manual prioritization configurations and reducing implementation costs while maintaining inference precision.
Solution Approach 2:
The system automatically determines appropriate data sources through question classification without requiring manual configuration for each device. This self-service approach maintains high inference precision through consistent data source selection while significantly reducing implementation costs by eliminating time-consuming manual setup work.
Data Source
AI summary
An inference apparatus makes an inference with respect to a phenomenon, the inference apparatus includes: a question acquirer configured to acquire a question related to the phenomenon; a question classifier configured to classify whether the question is a qualitative question or a quantitative question; a sensor classifier configured to classify whether sensor data is acquirable or not when the question is the quantitative question; a determiner configured to determine the sensor data as data to be used for the inference when the sensor data is acquirable and configured to determine input data by a user as the data to be used for the inference when the sensor data is unacquirable; and an inferrer configured to make the inference corresponding to the phenomenon using the data determined by the determiner.