2,559 research outputs found
Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges
As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to
author
Underground Diagnosis Based on GPR and Learning in the Model Space
Ground Penetrating Radar (GPR) has been widely used in pipeline detection and
underground diagnosis. In practical applications, the characteristics of the
GPR data of the detected area and the likely underground anomalous structures
could be rarely acknowledged before fully analyzing the obtained GPR data,
causing challenges to identify the underground structures or abnormals
automatically. In this paper, a GPR B-scan image diagnosis method based on
learning in the model space is proposed. The idea of learning in the model
space is to use models fitted on parts of data as more stable and parsimonious
representations of the data. For the GPR image, 2-Direction Echo State Network
(2D-ESN) is proposed to fit the image segments through the next item
prediction. By building the connections between the points on the image in both
the horizontal and vertical directions, the 2D-ESN regards the GPR image
segment as a whole and could effectively capture the dynamic characteristics of
the GPR image. And then, semi-supervised and supervised learning methods could
be further implemented on the 2D-ESN models for underground diagnosis.
Experiments on real-world datasets are conducted, and the results demonstrate
the effectiveness of the proposed model
Cell fault management using machine learning techniques
This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this
Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning
Early fault detection and fault prognosis are crucial to ensure efficient and
safe operations of complex engineering systems such as the Spallation Neutron
Source (SNS) and its power electronics (high voltage converter modulators).
Following an advanced experimental facility setup that mimics SNS operating
conditions, the authors successfully conducted 21 fault prognosis experiments,
where fault precursors are introduced in the system to a degree enough to cause
degradation in the waveform signals, but not enough to reach a real fault. Nine
different machine learning techniques based on ensemble trees, convolutional
neural networks, support vector machines, and hierarchical voting ensembles are
proposed to detect the fault precursors. Although all 9 models have shown a
perfect and identical performance during the training and testing phase, the
performance of most models has decreased in the prognosis phase once they got
exposed to real-world data from the 21 experiments. The hierarchical voting
ensemble, which features multiple layers of diverse models, maintains a
distinguished performance in early detection of the fault precursors with 95%
success rate (20/21 tests), followed by adaboost and extremely randomized trees
with 52% and 48% success rates, respectively. The support vector machine models
were the worst with only 24% success rate (5/21 tests). The study concluded
that a successful implementation of machine learning in the SNS or particle
accelerator power systems would require a major upgrade in the controller and
the data acquisition system to facilitate streaming and handling big data for
the machine learning models. In addition, this study shows that the best
performing models were diverse and based on the ensemble concept to reduce the
bias and hyperparameter sensitivity of individual models.Comment: 25 Pages, 13 Figures, 5 Table
Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization
Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management
Artificial Intelligence-based Control Techniques for HVDC Systems
The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PD
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