Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach

• URL: http://arxiv.org/abs/2101.03701v1
• Date: Mon, 11 Jan 2021 05:08:19 GMT
• Title: Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach
• Authors: Zhiming Zhang, Jin Yan, Liangding Li, Hong Pan, and Chuanzhi Dong
• Abstract summary: This study proposes a data-driven method that detects cable damage from measured cable forces by recognizing biased patterns from the intact conditions. The proposed method was tested on an in-service cable-stayed bridge with damaged stay cables.
• Score: 4.648677931378919
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This study proposes a data-driven method that detects cable damage from measured cable forces by recognizing biased patterns from the intact conditions. The proposed method solves the pattern recognition problem for cable damage detection through time series classification (TSC) in deep learning, considering that the cable's behavior can be implicitly represented by the measured cable force series. A deep learning model, long short term memory fully convolutional network (LSTM-FCN), is leveraged by assigning appropriate inputs and representative class labels for the TSC problem, First, a TSC classifier is trained and validated using the data collected under intact conditions of stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. The proposed method was tested on an in-service cable-stayed bridge with damaged stay cables. Two scenarios in the proposed TSC scheme were investigated: 1) raw time series of cable forces were fed into the classifiers; and 2) cable force ratios were inputted in the classifiers considering the possible variation of force distribution between cable pairs due to cable damage. Combining the results of TSC testing in these two scenarios, the cable with rupture was correctly identified. This study proposes a data-driven methodology for cable damage detection that requires the least data preprocessing and feature engineering, which enables fast and convenient early detection in real applications.

Related papers

• Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance [12.235603878539546]
  Cable broadband networks are one of the few "last-mile" broadband technologies widely available in the U.S. Existing tools in the public domain have prohibitive high false-positive rates. We propose CableMon, the first public-domain system that applies machine learning techniques to PNM data.
  arXiv  Detail & Related papers  (2024-12-12T18:49:11Z)
• Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation [49.53202761595912]
  Continual Test-Time Adaptation involves adapting a pre-trained source model to continually changing unsupervised target domains. We analyze the challenges of this task: online environment, unsupervised nature, and the risks of error accumulation and catastrophic forgetting. We propose an uncertainty-aware buffering approach to identify and aggregate significant samples with high certainty from the unsupervised, single-pass data stream.
  arXiv  Detail & Related papers  (2024-07-12T15:48:40Z)
• A Collision-Aware Cable Grasping Method in Cluttered Environment [12.17415219032655]
  We introduce a Cable GraspConvolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. We generate an dataset that mimics the intricacies of cable pose and factoring in potential collisions between cables and robotic grippers. We achieve commendable success rates of 92.3% for cables and 88.4% for unknown cables.
  arXiv  Detail & Related papers  (2024-02-22T12:47:04Z)
• A robust deep learning-based damage identification approach for SHM considering missing data [12.46223206282221]
  Missing data significantly impacts the conduction of Structural Health Monitoring method. This paper develops a robust method for damage identification that considers the missing data occasions. Results show that the missing data imputation and damage identification can be implemented together.
  arXiv  Detail & Related papers  (2023-03-31T18:00:56Z)
• Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization [59.038366742773164]
  Linears and leaky ReLU trained by gradient flow on logistic loss have an implicit bias towards satisfying the Karush-KuTucker (KKT) conditions. In this work we establish a number of settings where the satisfaction of these conditions implies benign overfitting in linear classifiers and in two-layer leaky ReLU networks.
  arXiv  Detail & Related papers  (2023-03-02T18:24:26Z)
• Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks [63.189632935619535]
  Bayes risk CTC (BRCTC) is proposed to enforce the desired characteristics of the predicted alignment. By using BRCTC with another preference for early emissions, we obtain an improved performance-latency trade-off for online models.
  arXiv  Detail & Related papers  (2022-10-14T03:55:36Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression [21.43182752819447]
  In this paper, a noval method to locate underground cables based on Ground Penetrating Radar (GPR) and Gaussian-process regression is proposed. Experiments on real-world datasets are conducted, and the obtained results demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-01-25T08:32:14Z)
• 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)
• Frequency-based tension assessment of an inclined cable with complex boundary conditions using the PSO algorithm [5.674726559114232]
  This study aimed to propose a frequency-based method of cable tension identification considering the complex boundary conditions at the two ends of cables. The proposed approach was applied to the tension identification of the anchor span of a suspension bridge (Jindong Bridge) in China.
  arXiv  Detail & Related papers  (2021-08-11T04:07:27Z)
• Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders [0.0]
  High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. Machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. This paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection.
  arXiv  Detail & Related papers  (2021-06-24T18:58:43Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

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.