Comparison of different methods of aggregation of model ensemble outcomes in the validation and reconstruction of real power plant signals

International audienceSensors are placed at various locations in a production plant to monitor the state of the processes and components. For the plant state monitoring to be effective, the sensors themselves must be monitored for detecting anomalies in their functioning and for reconstructing the correct values of the signals measured. In this work, the task of sensor monitoring and signal reconstruction is tackled with an ensemble of Principal Component Analysis (PCA) models handling individual overlapping groups of sensor signals, randomly generated according to the Random Feature Selection Ensemble (RFSE) technique. The outcomes of these models are combined using a Local Fusion (LF) technique based on the evaluation of the models performance on set of training patterns similar to the test pattern under reconstruction. The performances obtained using the LF method are compared to those obtained using classical aggregation methods such as Simple Mean (SM) Globally weighted average (GWA), Median (MD) and Trimmed Mean (TM), on a real case study concerning 215 signals monitored at a Finnish Pressurized Water Reactor (PWR) nuclear power plant

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

To maintain the reliability, availability, and sustainability of electricity supply, electricity companies regularly perform visual inspections on their transmission and distribution networks. These inspections have been typically carried out using foot patrol and/or helicopter-assisted methods to plan for necessary repair or replacement works before any major damage, which may cause power outage. This solution is quite slow, expensive, and potentially dangerous. In recent years, numerous researches have been conducted to automate the visual inspections by using automated helicopters, flying robots, and/or climbing robots. However, due to the high accuracy requirements of the task and its unique challenges, automatic vision-based inspection has not been widely adopted. In this paper, with the aim of providing a good starting point for researchers who are interested in developing a fully automatic autonomous vision-based power line inspection system, we conduct an extensive literature review. First, we examine existing power line inspection methods with special attention paid to highlight their advantages and disadvantages. Next, we summarize well-suited tasks and review potential data sources for automatic vision-based inspection. Then, we survey existing automatic vision-based power line inspection systems. Based on that, we propose a new automatic autonomous vision-based power line inspection concept that uses Unmanned Aerial Vehicle (UAV) inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis and inspection. Then, we present an overview of possibilities and challenges of deep vision (deep learning for computer vision) approaches for both UAV navigation and UAV inspection and discuss possible solutions to the challenges. Finally, we conclude the paper with an outlook for the future of this field and propose potential next steps for implementing the concept

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

In this paper, we present a novel automatic autonomous vision-based power line inspection system that uses unmanned aerial vehicle inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of the data analysis. To facilitate the implementation of the system, we address three main challenges of deep learning in vision-based power line inspection: (i) the lack of training data; (ii) class imbalance; and (iii) the detection of small components and faults. First, we create four medium-sized datasets for training component detection and classification models. Furthermore, we apply a series of effective data augmentation techniques to balance out the imbalanced classes. Finally, we propose the multi-stage component detection and classification based on the Single Shot Multibox detector and deep Residual Networks to detect small components and faults. The results show that the proposed system is fast and accurate in detecting common faults on power line components, including missing top caps, cracks in poles and cross arms, woodpecker damage on poles, and rot damage on cross arms. The field tests suggest that our system has a promising role in the intelligent monitoring and inspection of power line components and as a valuable addition to smart grids

LS-Net: fast single-shot line-segment detector

In unmanned aerial vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in post-processing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional, and it consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the physically based rendering approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real time. This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection