Convolutional Neural Network Based Fault Detection for Rotating Machinery

Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Walle, Rik Van de; Hoecke, Sofie Van

doi:10.1016/j.jsv.2016.05.027

Cited by 999 publications

(463 citation statements)

References 30 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%

See 1 more Smart Citation

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

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

291

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%

“…Abdeljaber et al [19] used a CNN for structural damage detection on a grandstand simulator. Janssens et al [21] incorporated shallow CNNs with the amplitudes of the discrete Fourier transform vector of the raw signal as an input. Pooling, or subsampling, layers were not used.…”

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

291

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.…”

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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“…In this paper, four evaluation metrics are calculated, namely accuracy, precision, recall and f1-score. Their formulas can be seen in Equations (9)-(12) [22]:…”

Section: Evaluation Methodsology and Performance Measurementioning

confidence: 99%

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

Zhou

Zhu

et al. 2018

Applied Sciences

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Abstract:With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

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Convolutional Neural Network Based Fault Detection for Rotating Machinery

Cited by 999 publications

References 30 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

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

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