Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
TL;DR: A novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm that is well suited to the fault-diagnosis model and superior to other existing methods is proposed.
About: This article is published in Measurement.The article was published on 2016-11-01. It has received 592 citations till now. The article focuses on the topics: Convolutional neural network & Support vector machine.
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TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
1,569 citations
TL;DR: A new CNN based on LeNet-5 is proposed for fault diagnosis which can extract the features of the converted 2-D images and eliminate the effect of handcrafted features and has achieved significant improvements.
Abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.
1,240 citations
Cites background or methods from "Hierarchical adaptive deep convolut..."
...Guo et al [30] investigated the LeNet-5-based adaptive deep CNN on the bearing fault diagnosis....
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...[30] investigated a hierarchical adaptive deep CNN to bearing fault diagnosis....
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...Guo et al. [30] investigated a hierarchical adaptive deep CNN to bearing fault diagnosis....
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...They are sparse filter [16], DBN) [12], DBN-based HDN [27], SVM [43], ANN [12], and adaptive deep convolution neural network (ADCNN) [30]....
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...D features of the converted images automatically, these results outperform other DL and traditional methods, such as ADCNN, sparse filter, DBN, SVM, ANN, and SURF-based PNN, which shows the potential of the proposed method....
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TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.
1,025 citations
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
948 citations
Cites background from "Hierarchical adaptive deep convolut..."
...It should be noted that the selection of batch size affects the network training performance [45]....
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TL;DR: A novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN), which can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
Abstract: Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
876 citations
Cites background from "Hierarchical adaptive deep convolut..."
...Many papers overlook the effects of this simple operation, and most of these work use hundreds of training samples without any overlap [23,24]....
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...In [23], a hierarchical adaptive deep convolutional neural network....
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TL;DR: The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.
1,289 citations
Proceedings Article•
07 Dec 2009
TL;DR: In this paper, the authors apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks and show that the learned features correspond to phones/phonemes.
Abstract: In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks.
1,094 citations