Data acquisition and signal processing are crucial in vibration measurement. These techniques transform analog sensor signals into digital data for analysis. Understanding sampling rates, , and conversion processes is key to accurately capturing vibration information.
Signal processing enhances vibration data quality and extracts meaningful insights. Filtering removes unwanted frequencies, while advanced techniques like FFT and enable detailed analysis of vibration signals in both time and frequency domains.
Data acquisition and conversion
Analog-to-Digital Conversion Process
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Data acquisition in vibration measurement captures analog signals from sensors and converts them into digital form for analysis and storage
(ADC) transforms continuous analog signals into discrete digital values at specific time intervals
Nyquist-Shannon sampling theorem requires to be at least twice the highest frequency component in the signal to avoid
Resolution in ADC represents the number of discrete digital values used to represent the continuous (12-bit, 16-bit)
Key components of a data acquisition system
Sensors
Signal conditioning circuits
Anti-aliasing filters
Sample-and-hold circuits
Analog-to-digital converters
ADC Limitations and Specifications
results from finite resolution of digital representation of continuous analog signals
of an ADC system measures the ratio between largest and smallest signals it can accurately measure (expressed in dB)
(SQNR) measures quality of digitized signal
Improves by approximately 6 dB for each additional bit of resolution
Quantization noise decreases with higher resolution ADCs
Signal processing techniques
Filtering and Windowing
Filtering removes unwanted frequency components from vibration signals
Common types: low-pass, high-pass, band-pass, notch filters
Digital filters implemented as (FIR) or (IIR)
FIR filters provide linear phase response, stable operation