Precision rolling bearings serve as critical components in a range of diverse industrial applications, where their continuous health monitoring is essential for preventing costly downtime and catastrophic failures. Early-stage bearing defects present significant diagnostic challenges, as they manifest as weak, nonlinear, and non-stationary transient features embedded within high-amplitude random noise. While entropy-based methods have evolved substantially since Shannon’s pioneering work—from approximate entropy to multiscale variants—existing approaches continue to face limitations in their computational efficiency and information preservation. This paper introduces the Adaptive Composite Multiscale Slope Entropy (ACMSlE) framework, which overcomes these constraints through two innovative mechanisms: a time-window shifting strategy, generating overlapping coarse-grained sequences that preserve critical signal information traditionally lost in non-overlapping segmentation, and an adaptive scale optimization algorithm that dynamically selects discriminative scales through entropy variation coefficients. In a comparative analysis against recent innovations, our integrated fault diagnosis framework—combining Fast Ensemble Empirical Mode Decomposition (FEEMD) preprocessing with Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) classification—achieves 98.7% diagnostic accuracy across multiple bearing defect types and operating conditions. Comprehensive validation through a multidimensional stability analysis, complexity discrimination testing, and data sensitivity analysis confirms this framework’s robust fault separation capability.

With the rapid growth of global maritime trade, the efficient and safe management of maritime traffic has become increasingly critical. This study proposes a comprehensive framework for ship trajectory prediction and maritime traffic congestion identification based on Automatic Identification System (AIS) data. We integrate spatiotemporal analysis with deep learning techniques, specifically combining Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) to form a Temporal Graph Convolutional Network (T-GCN) model. This model effectively captures both spatial dependencies among ships and temporal dynamics in traffic flow. Furthermore, we introduce a congestion measurement indicator based on the Speed Performance Index (SPI) to quantify and identify congestion levels in maritime routes. The proposed method not only enhances the accuracy of ship trajectory prediction but also enables proactive congestion warnings, contributing to improved maritime safety and operational efficiency. Experimental results demonstrate the effectiveness of our approach in real-world scenarios.

Strong noise can obscure the transient and modulation signatures used for bearing fault diagnosis, while envelope-based methods remain sensitive to the frequency band selected before demodulation. This study proposes a learnable band-selective envelope fusion WDCNN (LBEF-WDCNN). A raw-waveform branch extracts temporal features, whereas an auxiliary branch applies four differentiable Gaussian masks, reconstructs the corresponding spectrally selected waveforms, and encodes their Hilbert envelope spectra. Both branches are trained jointly and combined using a fixed, sample-independent residual coefficient of $0.1$. The method was evaluated on HUST using a matched-noise protocol comprising additive white Gaussian noise (AWGN), colored noise, and impulse noise at 0, $-6$, and $-10$ dB, with additional dataset-specific evaluations on CWRU and PU. Across the nine HUST conditions, LBEF-WDCNN achieved the highest average Macro-F1 of $98.53\%$ using 74,065 trainable parameters. At $-10$ dB AWGN, it reached $95.30\%$ Macro-F1, exceeding DRSN-CW by 7.68 percentage points. Ablation results showed that learnable band selection improved the three-noise average at $-10$ dB from $94.79\%$ for fixed bands to $96.73\%$. DRSN-CW remained stronger under severe impulse noise. These results indicate that the proposed representation is particularly effective under severe broadband contamination while retaining a compact model size.

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, machinery frequently experiences massive domain shifts induced by varying rotational speeds. Concurrently, acquiring high-fidelity fault instances is limited compared to abundant healthy baseline data, often resulting in a long-tailed distribution. Under such data-starved conditions, conventional few-shot domain adaptation (FSDA) methodologies often may be affected by distributional erasure; global alignment objectives are mainly driven by the healthy majority, causing sparse fault signatures to be erroneously absorbed as noise and leading to severe diagnostic performance degradation. To address this setting, this study develops a physics-guided dual-stream fusion framework for extreme few-shot cross-domain fault diagnosis. The method does not treat the Laplace wavelet, STFT, CNNs, or AdaBN as newly introduced techniques. Instead, it integrates these components into a unified diagnostic pipeline designed for long-tailed target support sets under large speed shifts. A learnable Laplace wavelet convolution is used in the temporal branch to emphasize transient impact responses, while STFT spectrograms provide a complementary time-frequency representation for the two-dimensional branch. The two feature streams are then fused for target fault classification. For domain adaptation, a Strict AdaBN strategy is applied using only the target support set, rather than the target test data or a large unlabeled target pool. Under the evaluated 50 healthy + 12 fault support condition, the healthy samples provide target-domain operating-background statistics for BN recalibration, while the limited fault samples are used for supervised classifier adjustment. Experiments on the HUSTbearing and Torino DIRG datasets show that the proposed integrated framework achieves stable performance under the evaluated few-shot cross-speed settings. These results suggest that combining physics-guided Laplace convolution, time-frequency representations, and support-set-restricted BN recalibration can be useful for bearing fault diagnosis when target fault samples are limited.