MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection

• URL: http://arxiv.org/abs/2310.00332v1
• Date: Sat, 30 Sep 2023 10:37:12 GMT
• Title: MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection
• Authors: Iurii Katser, Vyacheslav Kozitsin, Igor Mozolin
• Abstract summary: Application of computer vision for anomaly detection has been under attention in several industrial fields. This work focuses on the research of the Magnetic Flux Leakage data and the preprocessing techniques. In doing so, we exploited the recent convolutional neural network structures and proposed robust approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, the application of computer vision for anomaly detection has been under attention in several industrial fields. An important example is oil pipeline defect detection. Failure of one oil pipeline can interrupt the operation of the entire transportation system or cause a far-reaching failure. The automated defect detection could significantly decrease the inspection time and the related costs. However, there is a gap in the related literature when it comes to dealing with this task. The existing studies do not sufficiently cover the research of the Magnetic Flux Leakage data and the preprocessing techniques that allow overcoming the limitations set by the available data. This work focuses on alleviating these issues. Moreover, in doing so, we exploited the recent convolutional neural network structures and proposed robust approaches, aiming to acquire high performance considering the related metrics. The proposed approaches and their applicability were verified using real-world data.

Related papers

• XAI-guided Insulator Anomaly Detection for Imbalanced Datasets [11.215245485606369]
  Power grids serve as a vital component in numerous industries, seamlessly delivering electrical energy to industrial processes and technologies. Drones are increasingly deployed to inspect powerlines, resulting in a substantial stream of visual data which requires swift and accurate processing. Deep learning methods have become widely popular for this task, proving to be a valuable asset in fault detection.
  arXiv  Detail & Related papers  (2024-09-25T11:19:42Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis [53.24804865821692]
  This study introduces a taxonomy for log anomalies and explores automated data labeling to mitigate labeling challenges. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies.
  arXiv  Detail & Related papers  (2023-12-22T15:04:20Z)
• Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review [4.493547775253646]
  Machine learning algorithms can be trained to accurately classify and locate defects in semiconductor samples. Convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images.
  arXiv  Detail & Related papers  (2023-08-16T13:59:43Z)
• Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach [2.967390112155113]
  We enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality. We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD) The effectiveness of the proposed method is demonstrated through its application to collected data.
  arXiv  Detail & Related papers  (2023-06-19T09:54:34Z)
• Real-Time Outlier Detection with Dynamic Process Limits [0.609170287691728]
  This paper proposes an online anomaly detection algorithm for existing real-time infrastructures. Online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.
  arXiv  Detail & Related papers  (2023-01-31T10:23:02Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• Graph Convolutional Networks for traffic anomaly [4.172516437934823]
  Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. To fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. We formulate the problem in a novel way, as detecting anomalies in a set of directed weighted graphs representing the traffic conditions at each time interval.
  arXiv  Detail & Related papers  (2020-12-25T22:36:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.