scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

2020 ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jian-Yu Long ◽  
Yan-Yang Zi ◽  
Shao-Hui Zhang ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Novelty Detection ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Sparse Autoencoder

Abstract Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that it is able to accurately diagnose known types of defects, as well as to detect unknown defects, outperforming other state-of-the-art methods.

A new tool for supervised classification of satellite images available on web servers: Google Maps as a case study

2016 ◽  
Author(s):  
Agustín García-Flores ◽  
Abel Paz-Gallardo ◽  
Antonio Plaza ◽  
Jun Li
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Web Servers ◽  
Google Maps

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

scholarly journals BREIZHCROPS: A TIME SERIES DATASET FOR CROP TYPE MAPPING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1545-1551
Author(s):  
M. Rußwurm ◽  
C. Pelletier ◽  
M. Zollner ◽  
S. Lefèvre ◽  
M. Körner
Keyword(s):  
Time Series ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Additional Data ◽  
Type Mapping ◽  
North East ◽  
Time Series Dataset ◽  
Crop Type ◽  
Sentinel 2

Abstract. We present BreizhCrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/breizhcrops) that has been designed with applicability for practitioners in mind. We plan to maintain the repository with additional data and welcome contributions of novel methods to build a state-of-the-art benchmark on methods for crop type mapping.

scholarly journals Supervised Classification of Satellite Images to Analyze Multi-Temporal Land Use and Coverage : A Case Study for the Town of MARABA, State of PARA, Brazil

2015 ◽  
Author(s):  
Priscila Siqueira Aranha ◽  
Flavia Pessoa Monteiro ◽  
Paulo Andre Ignacio Pontes ◽  
Jorge Antonio Moraes de Souza ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
...  
Keyword(s):  
Land Use ◽  
Supervised Classification ◽  
Satellite Images ◽  
Multi Temporal ◽  
The Town

scholarly journals How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset

2020 ◽  
Vol 10 (10) ◽  
pp. 3359 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Disease Diagnosis ◽  
Fine Tuning ◽  
Medical Image Classification ◽  
Fine Tune ◽  
Entire Network

Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.

scholarly journals Localizer-to-Mapper Knowledge Transfer: Real-time Diagnosis of Deep SLAM in Everyday Navigation

2021 ◽  
Author(s):  
kanji tanaka
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Real Time ◽  
Deep Neural Networks ◽  
Black Box ◽  
Input Output ◽  
Diagnosis Algorithm ◽  
The Cost ◽  
Time Diagnosis

In this study, we address a novel "domain-shift localization (DSL)" problem, by which "user robots" of a deep SLAM system localize a domain-shifted region in the robot workspace during their daily navigation. Furthermore, we present a case study pertaining to a simple deep SLAM system comprising a visual place classifier and visual odometry as exteroceptive and proprioceptive modules, respectively. Such a DSL method enables "mapper robots" to focus on available resources (e.g., time, energy, and computation) in the domainshifted region, rather than the entire workspace, thereby significantly reducing the cost of per-domain DNN maintenance. Unlike conventional scenarios of SLAM diagnosis, the deep SLAM system comprises deep neural networks (DNNs) with black-box characteristics, which render it difficult to directly diagnose the internal signals of SLAM modules. Hence, we present a novel diagnosis algorithm that does not rely on internal signals but uses only the input/output signals of DNNs as input to DSL. Experiments demonstrate that, compared with a vanilla deep SLAM system that does not reflect fault diagnosis, the proposed deep SLAM framework can achieve a path that is more similar to the actual measured GPS path. This study is a first step towards a real-time approach to diagnose aging of deep SLAM systems.

Geomorphometric Attributes Applied to Soil-Landscapes Supervised Classification of Mountainous Tropical Areas in Brazil: A Case Study

2008 ◽  
pp. 357-365
Author(s):  
W. Carvalho Junior ◽  
E.I. Fernandes Filho ◽  
C.A.O. Vieira ◽  
C.E.G.R. Schaefer ◽  
C.S. Chagas
Keyword(s):  
Supervised Classification ◽  
Tropical Areas
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