Unmanned aerial vehicles (UAVs) have demonstrated remarkable versatility across a spectrum of missions, and their autonomous flight and real-time control heavily rely on on-board sensors. However, UAV sensors are susceptible to external cyberattacks and internal failures in complex environments, and the scarcity of real sensor fault data poses challenges for developing accurate detection models. This paper is the first to investigate the zero-shot fault detection (ZSFD) problem for UAV sensors, which considers the most challenging scenario where there are no fault data provided for model training. Therefore, a novel Convolutional Variational Autoencoder-Generative Adversarial Network (CVAE-GAN) is proposed, which is designed with a discriminator, and a generator consisting of two encoders and one decoder. First, a fixed-length sliding window is used on normal data of multiple sensors to construct multidimensional input samples, and the generator first learns the latent distribution of fault-free data in adversarial training. Then, the CVAE-GAN generates large reconstruction errors for unseen fault data in testing, which are used as anomaly scores to be compared with adaptive thresholds to achieve fine-grained detection of different sensor faults. Finally, comprehensive experimental results based on real sensor data from an UAV demonstrate the effectiveness of our method in performing ZSFD tasks with different sensor faults, especially in detecting bias, stuck, and impact faults in magnetometer, where the GM metrics outperform the compared models by approximately 7% on average, and the test time efficiency opens up the possibility of realizing online fault detection.

In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.