Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

Guo, Xiaojie; Chen, Liang; Shen, Changqing

doi:10.1016/j.measurement.2016.07.054

Cited by 692 publications

(331 citation statements)

References 20 publications

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“…Since 2015, deep learning methodologies have been applied, with success, to diagnostics or classification tasks of rolling element signals [2,[16][17][18][19][20][21][22][23][24][25][26]. Wang et al [2] proposed the use of wavelet scalogram images as an input into a CNN to detect faults within a set of vibration data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

View full text Add to dashboard Cite

show abstract

“…CNN has been applied in fault diagnosis in [14][15][16][17]. CNN structures in [15,16] show great performance in classification. However, with a small number of categories, CNN would not always have better results than traditional methods as shown in [17].…”

Section: Results Comparisonmentioning

confidence: 99%

“…In general, learning [13] 96.24% 3 Wavelet-ANN [13] 88.54% 3 CNN with 2 pipelines [14] 93.61% 8 CNN with statistical feature [15] 98.02% 12 CNN with statistical feature [15] 98.35% 8 Hierarchical ADCNN [16] 98.13% 3 SVRM [16] 94.17% 3 1D-CNN [17] 97.40% 2 WP-SVM [17] 99.20% 2 FFT-SVM [17] 84.20% 2 rate, number of kernels, number of weights in each layer, and batch size are all parameters to be optimized.…”

Section: Parameter Selection For Cnnmentioning

confidence: 99%

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Xie

Zhang

2017

Shock and Vibration

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The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

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“…Consequently, a variety of deep-models for vibration based equipment condition monitoring have appeared in literature. It includes Deep Belief Networks (DBN) [20][21][22][23], Auto-Encoder ELM [24], Stacked Denoising Auto-encoders (SDA) and CNNbased fault-models [25] [26]. These researches suggest deeparchitectures to be more effective at fault recognition than shallow ones.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines

Shaheryar¹,

Yin²,

Ramay³

2017

IJCA

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Mechanical failures in rotating machinery (e.g. wind turbines, generators, motor-derives etc.) may result in catastrophic failures. Different mechanical faults induce characteristic vibrations in the equipment structure. Online vibration monitoring helps mitigate catastrophic failures through early detection and identification of underlying mechanical faults. However, extracting characteristic vibration features that improve fault classification performance and are robust to various noises in the vibration signals is a challenging task. Various statistical and signal processing-based vibrationfeatures have been proposed in the literature. These vibrationfeatures were devised on the basis of prior knowledge about characteristics of vibration signals from different fault types. Recently, automatic feature extraction through unsupervised learning in deep neural architectures has resulted in state of art performance on image and speech recognition tasks. So, Instead of feature-engineering, we, here, hypothesized that feature learning on raw vibration signal possibly will extract vibration-features that can improve fault identification performance of subsequent classifier. To the purpose, we explored Convolutional Neural Network for unsupervised feature learning on vibration signals and Denoising AutoEncoder for extracting vibration features that are robust and invariant to the noises in vibration signals. We proposed a Hybrid deep-model consisting of a Multi-channel Convolutional Neural Network followed by a stack of Denoising Auto-Encoders (MCNN-SDAE) with a single classification layer at the top. We compared the fault identification and classification performance of the proposed model with other models employing tradition statistical and signal processing based vibration-features. We validated the performance of all models on a benchmark vibration data collected from an experimental test-rig specifically designed to study vibration characteristics of bearing related faults.

show abstract

Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

Cited by 692 publications

References 20 publications

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines

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