Edge Computing Real-Time Data Processing for Vibration Monitoring
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Solution Overview
Problem
Existing methods struggle with real-time dynamic fast processing of online acceleration data, leading to difficulties in ensuring data consistency and accuracy due to noise and drift, especially in cantilever beam-like structures where vibration causes elastic deformation, limiting the applicability of laser displacement sensing equipment.
Innovation Solution
A real-time dynamic signal fast processing method based on edge computing is proposed, which includes a dynamic integration algorithm and a high-pass filter to de-noise speed and displacement data, ensuring accuracy and reducing drift, and is embedded into an edge device near the data source to improve timeliness and reduce storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser displacement sensing equipment is used to monitor cantilever beam-like structures, then displacement measurement precision is improved, but device complexity and installation difficulty increase due to higher requirements for installation position and space
Solution Approach 1:
The patent uses acceleration sensors to measure acceleration data, which is then integrated to obtain speed and displacement information. This copying approach replaces direct displacement measurement with acceleration measurement followed by mathematical integration, allowing the use of simpler acceleration sensors instead of complex laser displacement sensors while maintaining the ability to obtain displacement information
Solution Approach 2:
The patent replaces optical measurement systems (laser displacement sensors) with mechanical/electrical sensors (acceleration sensors) combined with signal processing. The acceleration sensors convert mechanical vibration into electrical signals that are then processed through integration to obtain displacement, substituting a simpler mechanical sensing approach for complex optical measurement
2Measurement precision
If existing acceleration data processing methods are used, then data de-noising is improved, but real-time processing capability deteriorates because these methods require higher data integrity and are designed for complete acceleration information
Solution Approach 1:
The patent applies de-noising operations selectively based on the amount of accumulated data. When the number of monitoring points reaches a certain threshold (1/4 of sampling frequency), de-noising is performed; otherwise, direct integration is used. This partial application of de-noising maintains real-time processing capability while improving accuracy when sufficient data is available
Solution Approach 2:
The patent implements dynamic processing where the de-noising operation is conditionally applied based on real-time data accumulation status. The system dynamically adjusts its processing strategy - using simple integration for rapid response when data is scarce, and switching to de-noised integration when sufficient data accumulates, optimizing both speed and accuracy adaptively
3Productivity
If online acceleration data is processed without de-noising, then processing speed is improved, but measurement precision deteriorates due to noise and drift in integrated speed and displacement data
Solution Approach 1:
The patent applies de-noising operations selectively based on the amount of accumulated data. When the number of monitoring points reaches a certain threshold (1/4 of sampling frequency), de-noising is performed; otherwise, direct integration is used. This partial application of de-noising maintains real-time processing capability while improving accuracy when sufficient data is available
Solution Approach 2:
The patent skips de-noising operations when data accumulation is insufficient, rushing through the integration process directly to maintain processing speed. This selective skipping prevents excessive processing delays while still applying de-noising when it becomes beneficial, balancing speed and accuracy based on data availability
Data Source
AI summary
An online fast processing method for real-time data based on edge computing. In the method, a dynamic online de-noising method is adopted to remove noise contained in speeds to ensure the effectiveness and accuracy of de-noising results; for the displacement integrated online, an efficient method is adopted for dynamic online de-noising to further reduce the effectiveness of drift in the displacement value on final integration results; and under the condition of ensuring the accuracy of an integration method, an integration algorithm is embedded into an edge device to realize fast calculation and analysis of data near a data source and realize dynamic fast integration of online signals based on the edge device, which provides effective references for efficient processing and calculation of data.