Journal ArticleDOI
A transfer convolutional neural network for fault diagnosis based on ResNet-50
TLDR
A new TCNN with the depth of 51 convolutional layers is proposed for fault diagnosis of ResNet-50 and achieves state-of-the-art results, which demonstrates that TCNN(ResNet- 50) outperforms other DL models and traditional methods.Abstract:
With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.read more
Citations
More filters
Journal ArticleDOI
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
Mohamed Loey,Gunasekaran Manogaran,Gunasekaran Manogaran,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa +4 more
TL;DR: A hybrid model using deep and classical machine learning for face mask detection will be presented, and the SVM classifier achieved 99.64 % testing accuracy in RMFD.
Journal ArticleDOI
A comprehensive review on convolutional neural network in machine fault diagnosis
TL;DR: This work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively, and points out the characteristics of current development, facing challenges and future trends.
Journal ArticleDOI
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
TL;DR: A novel ensemble convolutional neural networks (CNNs) based architecture for effective detection of both packed and unpacked malware, named Image-based Malware Classification using Ensemble of CNNs (IMCEC).
Journal ArticleDOI
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane,Jongwon Seok +1 more
TL;DR: This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms and can be of good help for future researchers to start their work on machinery fault Detection and diagnosis using the C WRU dataset.
Journal ArticleDOI
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
TL;DR: A systematic review of state-of-the-art deep learning-based PHM frameworks emphasizes on the most recent trends within the field and presents the benefits and potentials of state of theart deep neural networks for system health management.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
How transferable are features in deep neural networks
TL;DR: This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.
Proceedings Article
How transferable are features in deep neural networks
TL;DR: In this paper, the authors quantify the transferability of features from the first layer to the last layer of a deep neural network and show that transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task and (2) optimization difficulties related to splitting networks between co-adapted neurons.