A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

In the fault diagnosis field, the fault feature signal is weak and contaminated by the noise. The lock-in amplifier is a useful tool for weak signal detection. Aiming to the amplitude error of the lock-in amplifier caused by frequency deviation between the measured signal and the reference signal, a...

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Bibliographic Details
Main Authors: Chen, K. (Author), Cheng, Y. (Author), Wang, H. (Author), Wang, L. (Author), Wang, Y. (Author)
Format: Article
Language:English
Published: MDPI 2022
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Online Access:View Fulltext in Publisher
Description
Summary:In the fault diagnosis field, the fault feature signal is weak and contaminated by the noise. The lock-in amplifier is a useful tool for weak signal detection. Aiming to the amplitude error of the lock-in amplifier caused by frequency deviation between the measured signal and the reference signal, a DFT-based automatic signal frequency estimation method is studied to improve the frequency accuracy of the reference signal. Based on this frequency estimation method, a software digital lock-in amplifier method is proposed to detect the multiple frequencies signals. This proposed method can automatically measure the frequency value of the measured signal without prior frequency information. Then, the reference signals are generated through this frequency value to make the digital lock-in amplifier estimate the amplitude of the measured signal. Moreover, an iterative structure is used to implement the multiple frequencies signal measurement. The frequencies and amplitudes measurement accuracies are tested. Under different SNR conditions, the frequency relative error is less than 0.1%. In addition, the amplitude relative error with different signal frequencies is less than 1.7% when the SNR is −1 dB. This proposed software digital lock-in amplifier method has a higher signal frequency tracking ability and amplitude measurement accuracy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ISBN:20763417 (ISSN)
DOI:10.3390/app12136431