Signal Filtering by Interval Frequency Analysis Under Memory Limits
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Solution Overview
Problem
High sampling frequencies required to determine noise frequencies lead to increased data amounts and memory capacity issues, which existing technologies fail to address effectively.
Innovation Solution
A signal processing device that filters signals from sensors by determining filter parameters using time series signals within specified intervals, allowing for high sampling frequencies without increasing memory capacity, by employing a data acquiring element, filter element, transfer element, and filter parameter determination element, and includes a sensor data sending element and specified interval acquiring element to set intervals for proper noise exclusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sampling frequency is increased to determine noise frequencies accurately, then the measurement precision is improved, but the data amount increases and memory capacity becomes insufficient
Solution Approach 1:
The patent divides the time series signal into multiple predetermined intervals and performs frequency analysis separately on each interval. This segmentation allows the system to process smaller data portions individually, reducing the memory burden while maintaining accurate noise frequency detection through localized spectral analysis of each segment.
Solution Approach 2:
The system performs frequency analysis on predetermined intervals of the time series signal before complete data collection is necessary. By preliminarily analyzing specific time segments to identify noise frequencies, the system determines filter parameters early without requiring storage of the entire high-volume dataset, thus resolving the memory capacity issue.
2Measurement precision
If the sampling frequency is increased to capture high frequency vibrations, then the measurement precision is improved, but the processing load increases
Solution Approach 1:
The patent segments the high-frequency vibration signal into predetermined intervals and performs frequency analysis on each segment separately. This approach reduces the processing load by avoiding computation on the entire high-volume dataset at once, while still capturing high-frequency vibration characteristics accurately through localized spectral analysis.
Solution Approach 2:
The system performs frequency analysis on only predetermined intervals of the time series signal rather than processing the complete dataset. This partial action approach suffices for determining noise frequencies and filter parameters, reducing computational complexity while maintaining adequate measurement precision for vibration detection.
Data Source
AI summary
The invention relates to a signal processing device, a control method thereof, a control procedure and a recording medium, which is unnecessary to increase a memory capacity for implementing sampling to determine a filter parameter. The signal processing device includes a data acquiring element (10), acquiring signals of a time series, i.e. time series signals from a sensor; a filtering element (21), performing a filtering operation according to frequencies; a transfer element (30), transferring the time series signals; and a filter parameter determination element (50), performing frequency analysis to the time series signals within a pre-specified interval, i.e. a specified interval to determine a filter parameter.