Bin Liu

This literature review offers an in-depth overview of recent advances in routing optimization for Unmanned Aerial Vehicles (UAVs), a field central to improving the performance, reliability, and flexibility of UAV systems. The review is organized into five categories: (1) multi-objective mission planning, (2) algorithmic design and optimization techniques, (3) energy efficiency and resource allocation, (4) communication protocols and network management, and (5) context-specific applications and environmental adaptability. The review highlights methodological progress and algorithmic approaches developed to balance competing demands such as mission effectiveness, energy use, and communication stability. Emphasis is placed on techniques that aim to extend UAV network longevity through effective energy strategies and on the creation of robust communication frameworks to support dependable data exchange. The study also considers how routing methods are being adapted to accommodate dynamic operational environments and varying external conditions. By drawing together insights from these areas, the review provides a comprehensive perspective on the current state of UAV routing optimization and identifies pressing challenges and directions for future research, with a focus on developing more adaptive and intelligent routing solutions.

With the rapid advancement of sensing technology, some critical components within engineering systems are equipped with sensors to collect condition monitoring (CM) signals. Such systems are referred to as partially monitored systems because only selected components are monitored. However, the method to integrate real-time component-level CM signals into reliability assessments of these systems remains unexplored. This study introduces a novel reliability assessment method designed to address the challenges of evaluating the reliability of partially monitored systems, particularly considering the highly nonstationary nature of CM signals and their dependence on the component states. A multistate degradation hidden Markov model with time-varying parameters (DHMM-TVP) is developed to better handle the nonstationary and nonlinear nature of CM signals. The expectation-maximization (EM) algorithm is adapted to estimate the unknown parameters within the DHMM-TVP framework. Furthermore, leveraging DHMM-TVP in combination with a functional kernel regression model, a generalized reliability assessment method is proposed, specifically tailored for cases where the system reliability structure is unknown or only partially known. A numerical simulation study and two case studies were conducted to validate the proposed reliability assessment approach. The component-level validation was performed using an experimental bearing accelerated degradation testing dataset, while the system-level verification employed aircraft turbofan engine datasets from the NASA prognostics data repository, collectively demonstrating the effectiveness of the proposed method.

Transmission systems are prone to developing new faults over long-term operations. Incremental learning offers a solution by continuously acquiring new knowledge from online data streams while preserving existing knowledge, making it ideal for ongoing diagnosis in industrial settings. However, the scarcity of incremental fault data and the time-intensive updating of incremental diagnosis methods reduce the effectiveness of existing models in real-time incremental diagnosis. To address this issue, a Cosine-represent Real-time Evolved Network (CREN) is proposed to cope with the challenge of incremental few-shot fault diagnosis (IFFDs) for transmission systems. First, a cosine-represented learning strategy is employed to achieve bias imputation in few-shot scenarios by redesigning the mapping between features and weights. Second, a continuous evolution classifier is constructed to achieve real-time incremental updating by embedding decoupled nonparametric class prototypes into the classification weights. The efficiency of the proposed method is verified through IFFD experiments on the two transmission systems.

In complex industrial application scenarios, abnormal samples are scarce. In the case of weak defect features, the high similarity of positive and negative samples further complicates detection and localization. In addition, anomalies are often subtle and unpredictable, which makes it particularly difficult to detect and localize anomalous subregions with unknown anomaly patterns. Many detection algorithms suffer from high computational complexity and huge memory consumption. To address these challenges, this paper proposes a multi-scale prototype fusion network for industrial product surface anomaly detection and localization (MPFnet). MPFnet uses multi-scale prototypes to construct representative normal patterns and incorporates a multi-scale fusion block to facilitate information exchange between different scales. This design enhances the model’s attention to characterize prototype and normal features. Feature adapter is constructed to generate fitness features, reducing domain bias. By adding noise to the adapted features, anomalous features are generated, and anomalies are detected using a simple and efficient discriminator. A large number of experiments were carried out on the challenging MVTec AD and MVTec LOCO AD datasets, demonstrating that MPFnet outperforms other state-of-the-art comparative methods, achieving good detection and localization results regardless of defect patterns.

Currently, various state-of-the-art Transformer variants have gained widespread attention in the field of fault diagnosis. However, these Transformers often adopt a global sequence modelling strategy to extract fault features, which is susceptible to the interference of redundant information and strong noise, due to the local and sparse nature of vibration signals. Therefore, a new feature enhancement and end-to-end fault diagnosis model named PSparseFormer is proposed in this paper. Firstly, a parallel sparse self-attention module is designed to efficiently extract the local and sparse features at different locations of complex vibration signals to reduce the over-sensitivity to irrelevant information. Secondly, the multiscale broadcast feed-forward block is developed to simultaneously facilitate global and local spatial feature information transmission and adjust the contribution of features at different levels, enhancing the robustness of local feature extraction against noise. Experimental analysis using datasets from two planetary gearboxes illustrates the effectiveness of the proposed method in addressing challenges related to feature extraction and enhancement, particularly in the presence of strong noise interference. Comparative evaluations against various state-of-the-art Transformers reveal that the proposed method exhibits superior diagnostic performance.