CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition

10/14/2021
by   Vahid Yaghoubi, et al.
12

Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster-Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline Nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19 with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 10

page 16

page 18

page 19

page 23

page 24

page 26

10/13/2021

Vibration-Based Condition Monitoring By Ensemble Deep Learning

Vibration-based techniques are among the most common condition monitorin...
07/17/2020

Multi-Classifier selection-fusion framework: application to NDT of complex metallic parts

Recent advances in computational methods, material science, and manufact...
03/19/2020

Extremal Region Analysis based Deep Learning Framework for Detecting Defects

A maximally stable extreme region (MSER) analysis based convolutional ne...
02/25/2022

A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

Nowadays, hand gesture recognition has become an alternative for human-m...
12/21/2020

A Frequency And Phase Attention Based Deep Learning Framework For Partial Discharge Detection On Insulated Overhead Conductors

Partial discharges are known as indicators of degradation of insulation ...
02/07/2018

A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification

Convolutional Neural Network (CNN)-based machine learning systems have m...
12/04/2020

A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

Achieving a high prediction rate is a crucial task in fault detection. A...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.