Axis-Dependent Error Detection in Linear Combination Encoding
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Solution Overview
Problem
Conventional error compensation techniques for industrial machines face limitations in improving accuracy beyond the inputtable data size, and axis-dependent data with a white noise-like property cannot be effectively compressed using entropy encoding, leading to undetected approximation errors that affect machining precision.
Innovation Solution
An approximation error detection device and program that utilize a linear combination model to approximate and encode axis-dependent data, detecting approximation errors exceeding a predetermined threshold, thereby enhancing error compensation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of input points of error amount is increased to improve error compensation accuracy, then the error compensation accuracy is improved, but the inputtable data size reaches an upper limit that prevents further accuracy improvement
Solution Approach 1:
The patent applies asymmetry by treating different types of error data differently - axis-independent data is compressed using entropy encoding while axis-dependent data uses linear combination modeling. This asymmetric approach allows optimal compression for each data type, enabling higher accuracy within the same data size limit.
Solution Approach 2:
The patent changes the representation parameters of error data by converting axis-dependent error data from direct coordinate values to linear combination coefficients. This parameter transformation reduces the effective data size while preserving the essential error information needed for high-accuracy compensation.
2Quantity of substance
If entropy encoding is used to compress axis-dependent data, then data compression is achieved, but the compression effectiveness is poor due to the white noise-like property of the data
Solution Approach 1:
The patent segments error data into two distinct categories: axis-independent data and axis-dependent data. This segmentation allows each type to be processed with the most appropriate compression method, avoiding the ineffective application of entropy encoding to axis-dependent data while still achieving overall compression goals.
Solution Approach 2:
The patent introduces linear combination modeling as an intermediary approach between raw axis-dependent data and compressed representation. By expressing axis-dependent data as a linear combination of basis vectors, the system achieves effective compression while preserving the structural relationships in the data that entropy encoding would fail to capture.
3Quantity of substance
If model approximation encoding is used to compress axis-dependent data, then data compression is achieved, but approximation errors may remain that affect measurement and error compensation accuracy
Solution Approach 1:
The patent implements feedback by detecting approximation errors in the compressed representation and using this information to adjust the compression threshold or model parameters. This feedback mechanism ensures that approximation errors remain within acceptable limits while maintaining effective data compression.
Solution Approach 2:
The patent applies partial action by selectively applying linear combination modeling only to axis-dependent data that benefits from this approach, while leaving axis-independent data to be processed by entropy encoding. This partial application optimizes the balance between compression effectiveness and approximation accuracy.
4Quantity of substance
If axis-dependent data is approximated and encoded to reduce data size, then the data size is reduced, but the user cannot notice the decrease in accuracy of error compensation
Solution Approach 1:
The patent enables the compression system to self-monitor its performance by automatically detecting approximation errors in the compressed representation. This self-service capability allows the system to identify when compression accuracy deteriorates, providing transparency without requiring external verification.
Solution Approach 2:
The patent introduces an approximation error detection mechanism as an intermediary between data compression and error compensation operations. This intermediary layer monitors the quality of compressed data and alerts users when approximation errors exceed acceptable thresholds, bridging the gap between compression efficiency and accuracy reliability.
Data Source
AI summary
The present invention makes it possible to detect an approximation error amount in approximating and encoding axis-dependent data that depends on the coordinate value of each axis of an industrial machine. An approximation error detection device 1 comprises an approximation error amount detection unit 11 that detects an approximation error amount with an absolute value greater than or equal to a predetermined threshold among approximation error amounts in performing model approximation encoding of axis-dependent data on the basis of a part of the axis-dependent data that depends on the coordinate value of each axis of an industrial machine, and on a linear combination model that approximates the axis-dependent data as a linear combination of data on each axis of the industrial machine.