Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning

• URL: http://arxiv.org/abs/2103.05430v1
• Date: Tue, 23 Feb 2021 11:44:10 GMT
• Title: Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning
• Authors: Chun Yui Wong, Pranay Seshadri, Geoffrey T. Parks
• Abstract summary: bladed components in aero-engines operate close to material limits. In-service damage on compressor and turbine blades has a profound and immediate impact on the performance of the engine. Current methods of blade visual inspection are mainly based on borescope imaging.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To maximise fuel economy, bladed components in aero-engines operate close to material limits. The severe operating environment leads to in-service damage on compressor and turbine blades, having a profound and immediate impact on the performance of the engine. Current methods of blade visual inspection are mainly based on borescope imaging. During these inspections, the sentencing of components under inspection requires significant manual effort, with a lack of systematic approaches to avoid human biases. To perform fast and accurate sentencing, we propose an automatic workflow based on deep learning for detecting damage present on rotor blades using borescope videos. Building upon state-of-the-art methods from computer vision, we show that damage statistics can be presented for each blade in a blade row separately, and demonstrate the workflow on two borescope videos.

Related papers

• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• A Novel Approach for Defect Detection of Wind Turbine Blade Using Virtual Reality and Deep Learning [0.0]
  We develop virtual models of wind turbines to synthesize the near-reality images for four types of common defects. In the second step, a customized U-Net architecture is trained to classify and segment the defect in turbine blades. The proposed methodology provides reasonable defect detection accuracy, making it suitable for autonomous and remote inspection through aerial vehicles.
  arXiv  Detail & Related papers  (2023-12-30T13:58:50Z)
• Towards Viewpoint Robustness in Bird's Eye View Segmentation [85.99907496019972]
  We study how AV perception models are affected by changes in camera viewpoint. Small changes to pitch, yaw, depth, or height of the camera at inference time lead to large drops in performance. We introduce a technique for novel view synthesis and use it to transform collected data to the viewpoint of target rigs.
  arXiv  Detail & Related papers  (2023-09-11T02:10:07Z)
• An Automated System for Detecting Visual Damages of Wind Turbine Blades [2.8360662552057323]
  Damages on wind turbine blades are the leading cause of high operational costs. Recent works in visual identification of blade damages are still experimental. We argue that pushing models to production long before achieving the "optimal" model performance can still generate real value.
  arXiv  Detail & Related papers  (2022-05-22T23:17:49Z)
• Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery [17.639472693362926]
  BladeNet is an application-based, robust dual architecture to perform both unsupervised turbine blade detection and extraction. Our dual architecture detects surface faults of glass fibre composite material blades with high aptitude. BladeNet produces an Average Precision (AP) of 0.995 across our Orsted blade inspection dataset for offshore wind turbines and 0.223 across the Danish Technical University (DTU) NordTank turbine blade inspection dataset.
  arXiv  Detail & Related papers  (2021-12-01T15:20:12Z)
• Oil and Gas Pipeline Monitoring during COVID-19 Pandemic via Unmanned Aerial Vehicle [0.0]
  The vast network of oil and gas transmission pipelines requires periodic monitoring to avoid equipment failure and potential accidents. Among many inspection methods, the unmanned aerial vehicle system contains flexibility and stability. The current paper is based on the idea of capturing video and images of drone-based inspections, which can discover several potential hazardous problems before they become dangerous.
  arXiv  Detail & Related papers  (2021-11-15T16:44:16Z)
• Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry [55.11913183006984]
  We show how a model can be trained to control a vehicle's trajectory using camera poses estimated through visual odometry methods. We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car.
  arXiv  Detail & Related papers  (2021-03-20T16:29:01Z)
• Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines [0.8795040582681388]
  Existing state-of-the-art methods for quantifying wear require disassembly and cutting of the examined liner. A deep-learning framework is proposed that allows computation of the surface-representing bearing load curves from reflection RGB images of the liner surface. For this purpose, a convolutional neural network is trained to estimate the bearing load curve of the corresponding depth profile.
  arXiv  Detail & Related papers  (2021-03-15T16:01:17Z)
• Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks [88.46770122522697]
  In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road. Deep learning methods for automotive radar interference mitigation can succesfully estimate the amplitude of targets, but fail to recover the phase of the respective targets. We propose an efficient and effective technique that is able to estimate both amplitude and phase in the presence of interference.
  arXiv  Detail & Related papers  (2020-10-14T09:41:06Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)
• Circumventing Outliers of AutoAugment with Knowledge Distillation [102.25991455094832]
  AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part of discriminative information from the training image. To relieve the inaccuracy of supervision, we make use of knowledge distillation that refers to the output of a teacher model to guide network training.
  arXiv  Detail & Related papers  (2020-03-25T11:51:41Z)

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.