The opportune fault detection of the rolling element bearings can avoid serious equipment accidents or even casualties. However, the early fault features of the bearings are weak and often submerged in heavy background noise and interferences. This paper proposes a novel approach for bearing fault diagnosis based on the hard thresholding fast iterative filtering (HTFIF), IMF selection index integrating L- kurtosis, correlation coefficient and autocorrelation function impulse harmonic to noise ratio (L-KCA), and improved k-value symmetrical difference analytic energy operator (k-SDAEO). As an adaptive and fast time-frequency analysis method, HTFIF is first adopted to process the bearing vibration signal and obtain a series of IMFs. After that, an alternative fusion index L-KCA is developed to select the sensitive IMF. Finally, a novel k-SDAEO with strong noise robustness is presented to process the selected IMF. With this method, the weak bearing fault signatures can be identified from the energy spectrum. The performance of the proposed method comparing to the traditional methods are investigated by numerical simulation and experimental studies. The results show that the proposed method has better fault feature extraction capability and higher efficiency, which can be implemented in the on-line and real-time fault detection.

The incipient fault features of rolling element bearings (REBs) are easily overwhelmed by environmental noise and vibration interference. Therefore, this paper proposes a novel fault feature extraction method for REBs based on a SOSO (Strengthen-Operate denoising-Subtract-Strengthen) boosting technique. Firstly, an improved fast non-local mean filtering (IFNLM) algorithm is proposed by improving the similarity measure and kernel function while reducing the amount of weight calculation based on distance symmetry. Secondly, a SOSO_IFNLM boosting filtering structure is constructed to reduce the noise of the original vibration signal and enhance the early faint fault pulse. Finally, a k-value improved symmetric higher-order frequency-weighted energy operator (k-SHFWEO) is proposed to detect the bearing fault features from denoised signals. The effectiveness and feasibility of the proposed SOSO_IFNLM-k-SHFWEO method are numerically and experimentally investigated. The results demonstrate that the proposed method has better fault feature extraction capability for early weak faults of REBs and higher efficiency compared to other popular methods.

To address the limitations of traditional deep learning models, which rely on empirical hyperparameter tuning and suffer from limited diagnostic accuracy due to insufficient feature extraction capability in rolling bearing fault diagnosis, this paper proposes an intelligent diagnosis method. The proposed approach is based on an adaptive weighted Black-Winged Kite Algorithm (WBKA)-optimized Convolutional Neural Network with Squeeze-and-Excitation Attention-Bidirectional and Long Short-Term Memory (CNN-SE-BiLSTM) network. Firstly, raw vibration signals are preprocessed using z-score normalization to eliminate dimensional inconsistencies and enhance the stability of model training. Secondly, to mitigate the issues of premature convergence and local optima entrapment of the WBKA, an adaptive weighting strategy and dynamic mutation mechanism are designed to improve its global search capability and convergence efficiency. Finally, an enhanced WBKA-optimized hybrid diagnosis model is developed by embedding the Squeeze-and-Excitation (SE) attention module into the CNN-BiLSTM framework. The SE module dynamically recalibrates the importance of channel-wise features, the CNN captures spatial fault patterns, and the BiLSTM models bidirectional temporal dependencies, thereby establishing a collaborative spatiotemporal diagnostic mechanism. Experimental validation on the datasets from Case Western Reserve University and Jiangnan University demonstrates that the optimized CNN-SE-BiLSTM model achieves superior diagnostic accuracy (99.62%-99.98%), with an F1-score improvement of 0.9% over the CNN-BiLSTM baseline, highlighting the efficacy of the SE-guided attention mechanism. Furthermore, the improved WBKA exhibits 60% fewer iterations than PSO (46 vs 115) and stronger robustness, which enables high accuracy and efficiency in intelligent fault diagnosis.

In this study, a theoretical model was established for longitudinal braking stability (LBS) of a lightweight liquid storage mini-track vehicle to investigate its behaviors. The influence of the tank liquid filling ratio (TLR), initial velocity (IV), and initial braking deceleration (IBD) on the LBS of the tracked vehicle was studied via FLUENT simulation. The simulation results showed that when the IV and the IBD were constant, the TLR significantly affected the braking efficiency. Conversely, when the TLR was 0.7, the braking efficiency decreased by 32%, resulting in the worst LBS. The braking efficiency at different IVs decreased by ~30% when using a constant IBD and TLR, and the braking stability exhibited little difference. Further, the braking efficiency decreased noticeably with increasing IBD; meanwhile, the braking longitudinal stability worsened. When the IBD was increased to 5 m⋅s⁻², the braking efficiency decreased by 48.4%. The road experiment showed that when the tracked vehicle was accelerated and braked under limit conditions, the stability requirements were met. Finally, by combining the simulation and road experiment, the driving parameters of the tracked vehicle that could satisfy the LBS were obtained.