A New Two-Level Hierarchical Diagnosis Network Based on Convolutional Neural Network

Wen, Long; Li, Xinyu; Gao, Liang

doi:10.1109/tim.2019.2896370

Cited by 96 publications

(37 citation statements)

References 39 publications

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“…Even under the interference of noise, it achieved the desirable classification accuracy of 97.74% [87]. [37], and HCNN represents hierarchical convolutional neural network [90].…”

Section: A Cnn-based Fault Diagnosis For Bearingmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…Even under the interference of noise, it achieved the desirable classification accuracy of 97.74% [87]. [37], and HCNN represents hierarchical convolutional neural network [90].…”

Section: A Cnn-based Fault Diagnosis For Bearingmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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“…On account of the type and degree of failure in the meantime, a novel hierarchical CNN based on LeNet-5 was constructed for bearing fault diagnosis, with a structure of multiple shared layers and two respective classifiers. Before network training, S-transform was employed to convert fault data into time-frequency distribution images [85]. In terms of experimental design, for comparison, three different cases were adopted with reference to three relative researches under the various operation conditions and workloads [86,87,88].…”

Section: Figure 2 Time-frequency Distribution Of Different Vibrationmentioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…There are many and interesting applications and methodologies proposed by authors in an effort to try and cover the ongoing and growing challenges in this field. In [18], the authors propose a methodology based on hierarchical convolutional neural networks (HCNN) as a two level hierarchical diagnosis network with two main characteristics: the fault pattern and fault severity are modelled as one hierarchical structure and estimated at the same time. Based on that structure the proposed architecture has two classifiers.…”

Section: Predictive Algorithms In Industrial Processesmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

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The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

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A New Two-Level Hierarchical Diagnosis Network Based on Convolutional Neural Network

Cited by 96 publications

References 39 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

A Deep Learning framework for simulation and defect prediction applied in microelectronics

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