To accurately predict the remaining useful life (RUL) of rolling bearings under strong noise interference, this paper proposes an RUL prediction method based on adaptive Variational Mode Decomposition (VMD) and a hybrid Temporal Convolutional Network-Gated Recurrent Unit-Self-Attention (TCN-GRU-SA) framework. First, a parameter-optimized VMD algorithm is developed by integrating the Grey Wolf Optimizer (GWO) with VMD to extract effective intrinsic mode components (IMFs) and reconstruct denoised signals, thereby mitigating the impact of strong background noise. Subsequently, time-domain degradation features are extracted from the reconstructed signals to generate more representative feature datasets. These degradation features are then fed into a parallel TCN-GRU-SA prediction model. To enhance the model’s generalization capability and RUL prediction performance, the proposed hybrid architecture combines a Temporal Convolutional Network (TCN), which captures local temporal patterns, a Gated Recurrent Unit (GRU) for modeling long-term dependencies, and a Self-Attention (SA) mechanism to prioritize critical degradation-related features. Experimental validation on the PHM2012 rolling bearing accelerated lifetime dataset demonstrates that the proposed method achieves superior noise robustness and prediction accuracy compared to existing approaches. Specifically, it reduces the root mean square error (RMSE) by 18.7 % and improves the coefficient of determination (R2) by 12.3 % under high-noise conditions, confirming its effectiveness in industrial predictive maintenance applications.

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved.

Accurate real-time pneumatic flow estimation offers a cost-effective alternative to expensive, bulky flow meters, yet persistent challenges stem from complex valve environments, high nonlinearity, and stringent precision requirements. This paper introduces a novel control framework integrating fuzzy PID dynamic tuning with adaptive wavelet threshold denoising, synergistically optimizing fuzzy PID and improved wavelet transform (IWT) to simultaneously enhance control accuracy and signal quality. Experimental validation demonstrates a 35% reduction in spool displacement overshoot versus conventional PID control. IWT integration improves flow estimation signal-to-noise ratio (SNR) by 65% relative to hard/soft thresholding methods while reducing root mean square error (RMSE) by 49%. The approach significantly outperforms mainstream techniques in dynamic response and noise immunity, enabling precise proportional valve flow measurement. This algorithm-driven strategy replaces high-cost sensors, reducing industrial maintenance requirements. Especially applicable to electromagnetic pneumatic systems in harsh environments, it establishes a reliable framework for proportional valve flow control.

As one of the key components of transmission and support in rotating machinery, rolling bearings will directly affect the operating status of the equipment. Therefore, scholars have proposed various methods to process its fault signals. Among them, spectral amplitude modulation (SAM) is a nonlinear filtering method that can effectively extract bearing fault characteristics. However, due to the high-intensity noise interference in the working environment of the equipment, the interference component dominates the monitoring signal, and the fault characteristics are no longer obvious or even undetectable. To solve the above problems, this paper proposes a local W transform spectral amplitude (LWTSAM) modulation method. First, the method divides the original signal into multiple narrowband signals in different frequency bands and selects the narrowband signal with the most abundant fault information among them; then, the selected narrowband signal uses a windowed Fourier transform (WFT) to obtain the amplitude in the time-frequency domain, and uses different weight indices (MO) to correct the amplitude; Finally, the modified amplitude is combined with its original phase to perform inverse window Fourier transform to obtain the modified signal and its square envelope, and the square envelope under the optimal weight is calculated using unbiased autocorrelation and information entropy to complete the local W transform spectrum amplitude modulation. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. Compared with SAM method, Autogram method and fast spectral kurtosis diagram method, the superiority of this method is proved.