CNC Sensor Data Modeling for Multi-Channel Cross-Correlation
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Solution Overview
Problem
There is a lack of models for researching the cross-correlation of multi-channel data collected by sensors on numerical control machine tools, which hinders effective monitoring and analysis of machine tool operating states.
Innovation Solution
A modeling system and method that utilize a controller connected to multiple sensors, a multi-channel sensor interface circuit, and a flash memory with various modules to convert and process multi-channel data into tensor-data, allowing for the extraction of cross-correlation information using sparse coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are installed to collect multi-channel data for monitoring CNC machine tool operating status, then the monitoring capability and data completeness are improved, but the device complexity and difficulty of data processing increase
Solution Approach 1:
The patent segments the complex multi-channel data processing task into distinct functional modules: a construction module that organizes raw sensor data into tensor structures, an overlay module that performs specific processing operations, and an export module that outputs results. This modular segmentation reduces the overall system complexity by breaking down the monolithic data processing function into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces tensor data structures as an intermediary representation layer between the raw multi-channel sensor data and the analysis algorithms. This tensor intermediary standardizes the heterogeneous data from multiple sensors into a unified mathematical format, simplifying subsequent processing operations and reducing the complexity of data manipulation across different sensor types and channels.
2Reliability
If multiple sensors are installed to collect multi-channel data for monitoring CNC machine tool operating status, then the monitoring capability is improved, but the difficulty of detecting and measuring cross-correlation increases
Solution Approach 1:
The patent transforms the multi-channel sensor data from its original heterogeneous format into standardized tensor data structures with specific mathematical properties. This parameter transformation changes the data representation from disparate sensor readings to structured tensors that facilitate cross-correlation analysis through established mathematical operations, thereby reducing the difficulty of detecting relationships between different sensor channels.
Solution Approach 2:
The patent elevates the data representation from traditional multi-dimensional arrays to tensor structures that inherently capture multi-way relationships. This dimensional transformation allows cross-correlation analysis to be performed more effectively by utilizing the inherent multi-linear algebraic properties of tensors, making it easier to detect and measure correlations across multiple sensor channels simultaneously.
Data Source
AI summary
A modeling system for collected data of sensors on a numerical control machine tool and a method therefor. The modeling system comprises a plurality of sensors for collecting numerical control machine tool operation state data serving as multi-channel data, wherein an output end of a sensor is connected to an input end of a multi-channel sensor interface circuit, and an output end of the multi-channel sensor interface circuit is connected to a controller. The plurality of sensors are multi-path temperature sensors. Data that is collected by each sensor and is transmitted to the controller serves as one piece of channel data. The method effectively prevents the defect in the prior art of there being no model for researching a cross correlation of multi-channel data, collected by a plurality of sensors, of an operation state of a numerical control machine tool.