Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade

Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade

Bibliographic Details
Main Author: Wang, Kan
Language:English
Published: University of Cincinnati / OhioLINK 2017
Subjects:
Computer Science
volume CT
nondestructive examination
anomaly
inspection
deep learning
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504869009850912
id ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1504869009850912
record_format oai_dc
spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15048690098509122021-08-03T07:04:08Z Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade Wang, Kan Computer Science volume CT nondestructive examination anomaly inspection deep learning Volume CT (VCT) scan is being used in various industrial and medical applications. One example of such is the inspection of turbine blades in jet engines. Even with the complete 3D data, it can be difficult to directly inspect potential anomaly regions (e.g. drilled holes) in the internal of the blade. The aim of this thesis is to design methods to help manual visual inspection and automatic detection of anomalies in turbine blade VCT data.In the first part, an unwrapping method inspired by medical research was designed and tested on the dataset. The method applies thresholding methods to the CT scans and extracts the skeleton of the major internal cavities. The skeleton branches are then used to perform unwrapping on the VCT data to transform it into a flattened 3D representation. The result reveals the internal of the blade and allows much more convenient visual inspection.The second part then explores deep learning techniques to automate anomaly detection. This chapter begins with an introduction to fundamental ideas of deep learning and popular frameworks, followed by a discussion of dataset composition and training/validation/testing set partitioning strategies. Finally, four Convolutional Neural Networks (CNN) were designed and trained to identify a specific type of anomaly. The result shows that an optimal combination of set partitioning strategy and network design allows the system to reach high accuracy in automatic anomaly detection. In conclusion, the thesis demonstrated a novel view to inspect VCT data and investigated the application of deep learning techniques to capture 3D features in the industrial environment. Both demonstrate great practical value and may inspire and stimulate more discussions and researches in this area. 2017 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504869009850912 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504869009850912 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Science
volume CT
nondestructive examination
anomaly
inspection
deep learning
spellingShingle Computer Science
volume CT
nondestructive examination
anomaly
inspection
deep learning
Wang, Kan
Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
author Wang, Kan
author_facet Wang, Kan
author_sort Wang, Kan
title Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
title_short Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
title_full Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
title_fullStr Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
title_full_unstemmed Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade
title_sort volume ct data inspection and deep learning based anomaly detection for turbine blade
publisher University of Cincinnati / OhioLINK
publishDate 2017
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504869009850912
work_keys_str_mv AT wangkan volumectdatainspectionanddeeplearningbasedanomalydetectionforturbineblade
_version_ 1719452936488091648

Similar Items