Industrial Machine Data Collection Planning for Uniform Parameter Coverage
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Solution Overview
Problem
Generating a data collection plan that effectively covers the specification ranges of industrial machines to support machine learning and operation tests is challenging, requiring extensive man-hours due to the need for data collection under various operating conditions, especially when data collection is time-consuming.
Innovation Solution
A data collection plan generating device and method that sets machine specifications, extracts constraint conditions, generates operating conditions, and creates a data collection plan to uniformly distribute parameter values within specified ranges, reducing the number of required data points and man-hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection is conducted under various operating conditions to cover specification ranges, then the quality and applicability of machine learning data is improved, but the number of required data points and development time increases significantly
Solution Approach 1:
The system performs preliminary analysis of machine specifications and constraint conditions to pre-determine the necessary operating conditions for data collection. By calculating the minimum required data points in advance based on specification ranges and constraints, the system creates a data collection plan that covers all necessary specification ranges without requiring excessive data points, thus reducing development time while maintaining data quality.
2Manufacturing precision
If the number of data points is increased to evenly cover specification ranges, then the coverage and representativeness of collected data is improved, but the man-hours required for data collection increases enormously
Solution Approach 1:
The system changes the parameters of data collection by dynamically determining the number of data points to be collected for each operating condition based on the specification ranges and constraint conditions. Instead of collecting a fixed large number of data points, the system calculates the appropriate number of data points needed to achieve uniform coverage, thereby improving data coverage quality while reducing the total number of data collection operations required.
3Measurement precision
If comprehensive data collection is performed to support machine learning functions, then the performance and accuracy of machine learning models is improved, but the complexity and cost of development increases
Solution Approach 1:
The system incorporates feedback mechanisms by evaluating the relationship between machine specifications, constraint conditions, and required data points. The data collection plan generating device uses this feedback to automatically adjust and optimize the data collection plan, ensuring that only the necessary operating conditions and data points are collected. This reduces development complexity while maintaining sufficient data quality for accurate machine learning model training.
Data Source
AI summary
This data collection plan generating device generates a data collection plan including a combination of operating conditions for use when data are being acquired from an industrial machine that is operating. The data collection plan generating device: sets the specification of the industrial machine; extracts a constraining condition relating to an operation of the industrial machine; on the basis of the specification and the constraining condition, generates a plurality of operating conditions including a set of parameter values relating to the set specification; and generates and outputs a data collection plan from the generated operating conditions.