In dynamic machine testing (such as material fatigue testing, structural vibration analysis), sensor signals are easily affected by circuit thermal noise, environmental electromagnetic interference, mechanical vibration, etc., resulting in random fluctuations in collected data, masking real characteristics such as material deformation and stress changes. Data denoising needs to balance between "removing noise" and "preserving effective signals" - excessive denoising will lose key details, while insufficient denoising will affect analysis accuracy.Dynamic machine testing softwareThrough algorithm optimization and parameter adjustment, noise can be accurately suppressed and the essence of data can be restored. The following is a practical guide.

1、 Noise recognition: precise identification of interference sources

Before noise reduction, it is necessary to use software analysis functions to locate the type of noise and avoid blind processing

Time domain feature judgment: When viewing the original data curve in the software, if there are irregular sharp spikes (such as a sudden jump of ± 5MPa in the stress curve and rapid recovery), it is mostly electromagnetic pulse interference (such as surges caused by relay on-off); If the curve shows a stable fluctuation as a whole (such as displacement data continuously fluctuating up and down by 0.01mm), it may be due to thermal noise in the sensor circuit (positively correlated with temperature and bandwidth).

Frequency domain analysis confirmation: Use the software's "spectrum analysis" function to convert time-domain data into frequency domain spectra. If there is a significant peak at 50Hz/60Hz, it is power frequency electromagnetic interference; If the noise energy in the high frequency range (>1kHz) is significant, it is mostly high-frequency interference caused by mechanical vibration; If there is sustained noise in the low frequency range (<100Hz), it may be resistance contact noise or transistor flicker noise.

2、 Algorithm selection: noise reduction scheme adapted to the scene

Based on the type of noise and testing requirements, select the corresponding algorithm. The core solution is as follows:

Basic filtering: Suppressing regular noise

Low pass filtering: suitable for high-frequency noise (such as mechanical vibration), set the cutoff frequency in the software (such as 500Hz for structural vibration testing), filter out noise above this frequency, and avoid filtering out effective signals (such as material resonance peaks);

Notch filtering: Targeted elimination of power frequency interference, input 50Hz/60Hz center frequency, bandwidth set to 5-10Hz, can accurately suppress power grid interference without affecting signals in other frequency bands.

Advanced processing: Dealing with complex noise

Sliding average filtering: It has a significant effect on random thermal noise. Set the window size in the software (such as 10-50 data points), and smooth the curve by averaging adjacent data. If the window is too large, it will cause signal lag (such as the delay of deformation peak in fatigue testing);

Wavelet threshold denoising: Suitable for scenarios with sudden changes in signals (such as stress drops at the moment of material fracture), the software automatically decomposes the data into wavelet coefficients of different scales, sets threshold values to zero for coefficients dominated by noise, and preserves the reconstructed data of signal coefficients, which can avoid signal distortion caused by traditional filtering.

3、 Parameter optimization: Balancing noise reduction and signal fidelity

After the algorithm is determined, it is necessary to fine tune the parameters and verify the effect through the software preview function:

Filter parameter adaptation: The cutoff frequency of low-pass filtering should be 20% -30% higher than the highest frequency of the effective signal (such as in material impact testing, the highest frequency of the effective signal is 300Hz, and the cutoff frequency is set to 360Hz); The sliding average window size is adjusted according to the data sampling rate (when the sampling rate is 1000Hz, the window is set to 20 points, corresponding to a smoothing time of 0.02 seconds).

Threshold and iterative control: In wavelet denoising, a higher threshold is set for high-frequency noise (such as 0.1 times the maximum coefficient), and a lower threshold is set for low-frequency noise (such as 0.05 times); Enable the software's "Iterative Noise Reduction" function (2-3 iterations are sufficient), and compare the original data after each iteration to prevent excessive smoothing and loss of details (such as small stress fluctuations at the material yield point).

4、 Effect verification: Quantitative evaluation of noise reduction quality

After noise reduction, software indicators need to be verified to ensure data reliability:

Quantitative indicator judgment: Calculate the "signal-to-noise ratio (SNR)" before and after noise reduction, and generate it automatically through the software's "data statistics" function. An SNR improvement of ≥ 10dB is considered effective noise reduction; Check the root mean square error (RMSE) and compare it with standard signals (such as calibration block test data). RMSE ≤ 5% is considered qualified.

Consistency check of features: Compare key parameters before and after noise reduction (such as fracture load in fatigue testing and resonance frequency in vibration testing). If the deviation is ≤ 2%, it indicates that effective signals have not been lost in noise reduction; If there is a significant deviation (such as a resonance frequency shift of 10Hz), the filtering parameters need to be readjusted.

　　Dynamic machine testing softwareNoise reduction needs to follow the process of "identification selection optimization verification". By accurately locating noise, adapting algorithms, and adjusting parameters, it can effectively suppress interference while preserving core data features, providing precise data support for material performance evaluation and structural reliability analysis.