Adaptive Sampling for Line Noise Error Reduction
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Solution Overview
Problem
Industrial process measurement systems face challenges with line noise interference, leading to measurement errors, particularly due to unpredictable noise sources and the need for manual filtering selection, which can increase setup time and power consumption.
Innovation Solution
An automated system that detects line noise in sensor signals using noise detector circuitry, adjusts the sample rate of the A/D converter, and modifies the decimation period of a sigma delta filter to reduce noise impact, allowing for adaptive noise management without manual intervention and minimizing power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual filtering selection is used to reduce line noise, then measurement accuracy is improved, but setup time and operator error risk increase
Solution Approach 1:
The system automatically detects line noise frequency and configures filtering parameters without operator intervention. The microcontroller monitors the sensor signal, identifies power line frequency components, and autonomously adjusts the decimation filter settings, eliminating manual setup requirements and reducing operator error risk.
Solution Approach 2:
The system continuously monitors the sensor signal for line noise characteristics and dynamically adjusts filtering parameters based on detected noise levels. This closed-loop feedback mechanism ensures optimal filtering is applied adaptively, maintaining measurement accuracy without requiring manual reconfiguration.
2Measurement precision
If constant filtering is applied to reduce line noise, then measurement accuracy is improved, but update rate decreases and power consumption increases
Solution Approach 1:
The system dynamically adjusts the decimation period based on detected line noise frequency and amplitude. When line noise is detected, the system applies stronger filtering with longer decimation periods; when line noise is absent or low, the system reduces filtering intensity and uses shorter decimation periods, thereby maintaining fast update rates during normal operation while providing enhanced filtering only when needed.
Solution Approach 2:
The system changes the decimation parameter (filtering strength) based on line noise conditions. By varying this parameter dynamically rather than using constant filtering, the system optimizes the balance between measurement accuracy and update rate, applying strong filtering only when line noise interference is detected.
3Measurement precision
If constant filtering is applied to reduce line noise, then measurement accuracy is improved, but power consumption increases
Solution Approach 1:
The system performs line noise detection periodically rather than continuously, and applies enhanced filtering only during periods when line noise is detected. During normal operation with low or no line noise, the system uses minimal filtering, thereby reducing average power consumption while maintaining measurement accuracy when needed.
Data Source
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AI summary
A method includes sensing a process parameter to generate a sensor signal that includes a process signal and line noise components (102), digitizing the sensor signal at a sample rate (102), detecting line noise zero crossings in the sensor signal (104), determining a line noise frequency as a function of the detected line noise zero crossings (106), and adjusting the sample rate as a function of the line noise frequency to reduce an impact of line noise on the digitized sensor signal (110).