An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

Wang, Zirui; Wang, Jun; Wang, Youren

doi:10.1016/j.neucom.2018.05.024

Cited by 325 publications

(110 citation statements)

References 24 publications

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“…The training procedure is accomplished by using the back-propagation (BP) algorithm. Following greedy layer-wise training for SDAE, a deep network can be generated based on supervised fine-tuning [31].…”

Section: Related Workmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

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Section: Related Workmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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“…Liang et al designed a novel and high-accuracy fault detection approach named WT-GAN-CNN for rotating machinery, This model was based on CWT, generative adversarial nets (GANs) and CNN, the built CNN model is used to accomplish the fault detection of rotating machinery by the original training time-frequency images and the generated fake training time-frequency images, and verified its anti-noise ability through experiments [ 32 ]. Wang et al combined GANs with stacked denoising autoencoder (SDAE), using BP neural network (BPNN) as the generator to generate samples, and SDAE was used as the GAN discriminator to diagnose the planetary gearbox, The model has a certain anti-noise performance [ 33 ]. Peng et al proposed a new deep one-dimensional convolutional neural network (Der-1DCNN) based on one-dimensional residual blocks.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Shi

Chen

et al. 2020

Sensors

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Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

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“…Upon an adversarial learning of the GANs, a discriminator is employed to evaluate the probability that a sample created by the generator is real or false. Most of the applications of GANs are focused in the field of image processing such as biomedical images [26,27], but recently some papers in the industrial field have been published, mainly focused on data generation to solve the problem of the imbalanced training data [28][29][30][31][32][33]. Through continuous improvement of GAN, its performance has been increasingly enhanced and several variants have recently been developed [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Adversarial Variational Autoencoder

Zemouri

2020

MAKE

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We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE.

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An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

Cited by 325 publications

References 24 publications

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Semi-Supervised Adversarial Variational Autoencoder

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