Abnormal ship navigation behaviors in cross-sea bridge waters pose significant threats to maritime safety, creating a critical need for accurate anomaly detection methods. Ship AIS trajectory data contain complex temporal features but often lack explicit labels. Most existing anomaly detection methods heavily rely on labeled or semi-supervised data, thus limiting their applicability in scenarios involving completely unlabeled ship trajectory data. Furthermore, these methods struggle to capture long-term temporal dependencies inherent in trajectory data. To address these limitations, this study proposes an unsupervised trajectory anomaly detection model combining a transformer architecture with a variational autoencoder (transformer–VAE). By training on large volumes of unlabeled normal trajectory data, the transformer–VAE employs a multi-head self-attention mechanism to model both local and global temporal relationships within the latent feature space. This approach significantly enhances the model’s ability to learn and reconstruct normal trajectory patterns, with reconstruction errors serving as the criterion for anomaly detection. Experimental results show that the transformer–VAE outperforms conventional VAE and LSTM–VAE in reconstruction accuracy and achieves better detection balance and robustness compared to LSTM–-VAE and transformer–GAN in anomaly detection. The model effectively identifies abnormal behaviors such as sudden changes in speed, heading, and trajectory deviation under fully unsupervised conditions. Preliminary experiments using the POT method validate the feasibility of dynamic thresholding, enhancing the model’s adaptability in complex maritime environments. Overall, the proposed approach enables early identification and proactive warning of potential risks, contributing to improved maritime traffic safety.

Mechanical wear occurs during the operating cycle of all types of complex machinery. In this paper, the spectral, ferro-spectral, physical, and chemical analyses, along with onboard particle counting characteristics under laboratory conditions, are taken as small sample datasets. Wasserstein Generative Adversarial Network is used as the regeneration algorithm model for raw data, and the composite dataset with richer semantic information is used as input. A one-dimensional representation of the composite data is transformed into a two-dimensional image containing richer static information using the Markov Transfer Field transformation concept. The Mixture of Experts based meritocracy architecture selects different expert systems for various features in the dataset by categorizing the expert systems according to combinatorial principles and setting corresponding weight assignments. ConvNeXt, Bidirectional Transformer (BiTransformer), and Bidirectional Long Short-Term Memory are then employed to capture the image features and perform fault diagnosis on the composite one-dimensional mechanical wear data, respectively. An attention mechanism is added to optimize the algorithm globally, weighting the feature information across multiple dimensions to ensure the reliability and completeness of the results. The final results show that the accuracy of fault diagnosis exceeds 95%, demonstrating ideal performance.

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.