Industrial IoT Abnormal Identification for Machine Tool Machining
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Solution Overview
Problem
Existing technologies face challenges in accurately evaluating the machining process of intelligent machine tools due to the complexity of factors affecting cutting parameters, leading to poor accuracy in identifying abnormalities and their causes.
Innovation Solution
An industrial Internet of Things (IoT) system based on abnormal identification, which includes a service platform, a management platform, and a sensor network platform, uses force data from spindle sensors and real-time image data from cameras to identify abnormal causes by analyzing keyframes and processing surface data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only cutting parameters are used to evaluate the machining process, then the evaluation system is simple, but the accuracy of abnormal identification is poor
Solution Approach 1:
The patent combines multiple data sources including cutting parameters, spindle vibration data, and acoustic emission data into a unified evaluation system. This merging of different types of data enables more accurate abnormal identification while maintaining system manageability through integrated processing.
Solution Approach 2:
The patent transitions from single-dimensional cutting parameter evaluation to multi-dimensional evaluation by incorporating vibration frequency analysis and acoustic emission characteristics. This dimensional expansion allows the system to identify abnormalities that cannot be detected by cutting parameters alone.
2Measurement precision
If multiple parameters are used to evaluate the machining process, then the evaluation accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the evaluation system into independent modules: cutting parameter detection, vibration detection, acoustic emission detection, and integrated analysis. Each module handles specific parameters independently, reducing the complexity of data collection while enabling comprehensive multi-parameter evaluation through modular architecture.
Solution Approach 2:
The patent introduces data processing and integration modules as intermediaries between various sensors and the analysis system. These intermediaries standardize data formats, filter noise, and coordinate multiple data sources, thereby managing system complexity while preserving the benefits of multi-parameter evaluation.
3Reliability
If real-time monitoring of multiple parameters is implemented, then the abnormal detection capability improves, but the data processing complexity increases
Solution Approach 1:
The patent implements preliminary data processing steps including noise filtering, feature extraction, and threshold pre-setting for each parameter type. By preparing and pre-processing data before comprehensive analysis, the system reduces the computational burden during real-time operation while maintaining high abnormal detection capability.
Solution Approach 2:
The patent establishes feedback mechanisms where the system continuously monitors processed data, compares it against predetermined thresholds and historical patterns, and adjusts processing parameters dynamically. This feedback loop enables the system to adapt to changing conditions without increasing fundamental processing complexity.
Data Source
AI summary
The present disclosure discloses a control method of industrial Internet of Things (IoT) based on abnormal identification. The IoT includes: an obtaining unit, which is configured to obtain a first machining parameter; a detection unit, which is configured to obtain real-time image data when the first machining parameter is abnormal; an extraction unit, which is configured to obtain a keyframe and obtain a second machining parameter; a judgment unit, which is configured to determine an abnormal cause based on the first machining parameter and the second machining parameter; and a communication unit, which is configured to transmit the abnormal cause to a user terminal through a service platform.