Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for rolling bearing acoustic signals based on Secretary Bird Optimization Algorithm (SBOA)-optimized Feature Mode Decomposition (FMD). Initially, a microphone is utilized to collect sound data while the bearing operates, followed by the application of S-FMD (Secretary Bird Optimization Algorithm-optimized Feature Mode Decomposition) to decompose the sound signal and extract components that may contain fault information related to the bearing. The SBOA is employed to adaptively optimize four influencing parameters of FMD: mode number n, filter length L, frequency band cutting number K, and cycle period m. By minimizing envelope entropy as the objective function, we achieve FMD of the bearing sound signal with the assistance of the SBOA. Additionally, this paper introduces an Integrated Signal Evaluation Index (ISEI) to extract potential bearing failure characteristics from the filtered components. Simulation experiments and test results indicate that, compared to Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, fixed-parameter FMD, and adaptive variational mode decomposition methods, the proposed approach more effectively extracts weak characteristic information related to early faults in bearing sound signals.

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications.

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis.

In practical scenarios, rolling bearing vibration signals suffer from detail loss, and information loss occurs during feature dimensionality reduction and fusion, leading to inaccurate life prediction results. To address these issues, this paper first proposes a method for predicting the remaining useful life (RUL) of bearings, which combines an improved U-Net for enhancing vibration signals and a multi-dimensional hybrid gated attention mechanism (MHGAM) for dynamic feature fusion. The enhanced U-Net effectively suppresses the loss of signal details, while the MHGAM adaptively constructs health indices through multi-dimensional weighting, significantly improving prediction accuracy. Initially, the improved U-Net is utilized for signal preprocessing. By comprehensively considering both channel and spatial dimensions, the MHGAM dynamically assigns fusion weights across different dimensions to construct a health index. Subsequently, the health index is used as input for the Bi-GRU network model to obtain the remaining life prediction results. Finally, comparative analyses between the proposed method and other RUL prediction methods are conducted using the IEEE PHM 2012 bearing dataset (Condition 1: rotational speed 1800 r/min with radial load 4000 N; Condition 2: rotational speed 1650 r/min with radial load 4200 N) and engineering test data (rotational speed 1800 r/min with radial load 4000 N). Experimental results from the IEEE PHM 2012 bearing dataset indicate that this method achieves a low mean root mean square error (RMSE = 0.0504) and mean absolute error (MAE = 0.0239). The engineering test verification results demonstrate that the mean values of RMSE and MAE for this method are 7.8% lower than those of the CNN-BiGRU benchmark model and 14.6% lower than those of the TCN-BiGRU model, respectively. In terms of comprehensive prediction performance scores, the average scores improve by 7.8% and 9.3 percentage points compared with the two benchmark models, respectively. Under various test conditions, the prediction results of this method exhibit commendable comprehensive performance, significantly enhancing the prediction accuracy of bearing remaining useful life.