Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.

This article presents an open-circuit fault diagnosis approach for the drive inverter system in a permanent magnet synchronous motor. This method can detect weak faults, and extract key features based on the current vector phase. Initially, the three-phase current signal is collected as the original input, and the current vector phase is calculated. Subsequently, fault detection employs time-domain inversion techniques. A current vector residual coding signal is constructed based on the Park’s Vector. Phase characteristics are then precisely and automatically extracted through logical operations on relational patterns. Ultimately, the fault type is located according to the different combinations of characteristic variables. Compared with the traditional methods, the proposed diagnostic technique has a simple structure and is not affected by variations in load and speed. Automatic feature extraction avoids the loss of traditional sliding window sampling information and the influence of boundary effect. The experimental results show that the method is effective and practical in the open-circuit fault diagnosis of inverters.

Power system condition prediction is to predict key parameters such as voltage and current of its main components to ensure the reliability and safety of electrical system operation. However, as satellite missions and operating conditions become more complex, the state of electrical systems is affected by satellite loads and platforms. Therefore, it is difficult for offline-trained state prediction models to dynamically adapt to changing operating conditions and changing relationships between parameters. To overcome these problems, an online deep learning method, On-line Rules-limited Temporal Convolutional Network (O-R-TCN ), is proposed to improve the adaptability of the state prediction method. Firstly, a temporal convolutional network (TCN) online weight update method based on a recursive extreme learning machine is proposed, which realizes the self-learning of dynamic parameter relations under dynamic operation conditions to obtain higher prediction accuracy. Moreover, considering that anomalies within the monitored data may lead to undesired model updating, an association rule mining approach is proposed based on the Frequent Pattern-Growth (FP-Growth) fusion with trend sign aggregation approximation (TSAX). Avoid model updates caused by system exceptions or failures. Experiments are implemented based on the actual satellite power system telemetry data. The results illustrate that the proposed method can self-learn the varying relationship between the parameters and update the state prediction model during the working state change. At the same time, abnormal data can be effectively detected by the association rule, which ensures the effectiveness of the proposed method.

Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to address this problem, their performance inevitably meets the bottleneck due to the limitation of labeling cost. To address the aforementioned issue, this paper proposes a semi-supervised classification method for hyperspectral images that improves active deep learning. Specifically, the proposed model introduces the random multi-graph algorithm and replaces the expert mark in active learning with the anchor graph algorithm, which can label a considerable amount of unlabeled data precisely and automatically. In this way, a large number of pseudo-labeling samples would be added to the training subsets such that the model could be fine-tuned and the generalization performance could be improved without extra efforts for data manual labeling. Experiments based on three standard HSIs demonstrate that the proposed model can get better performance than other conventional methods, and they also outperform other studied algorithms in the case of a small training set.