Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine

By Ruonan Liu, Guotao Meng, Boyuan Yang, Chuang Sun, Xuefeng Chen. IEEE Transactions on Industrial Informatics, 2017

316 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/1edc693c1c597572d540e5e87673e1b232a614fe

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “Similar to earlier work [104]–[106], [107] implements a 4layer CNN structure with 2 convolutional and 2 pooling layers employing both the CWRU dataset & dataset generated by Qian Peng Company in China, and the accuracy outperforms the conventional SVM and the shallow Softmax regression classifier, e”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “Generative adversarial convolutional network (GAN) [40] has become an important research hotspot with promising performance on data generation.”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “It is reported in [105] that the best accuracy of 96.”
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, methodology): “Deep learning methods have also been applied to fault diagnosis and obtained state-of-the-art results [6] using techniques such as auto-encoders (AE) [12]–[15], restricted boltzmann machine (RBM) [16]–[19], convolutional neural networks (CNNs) [20]–[24], recurrent neural”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, methodology): “Unlike other approaches [26, 32, 33], we utilize an independent CNN to process each sensor data.”
Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, methodology): “D CNN (2DCNN) to implement fault diagnosis [15], [18], [20], [25]–[27].”
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, methodology): “For this reason, we inject additive Gaussian white noise [39] to the raw vibration signals to construct noisy signals with different signal-to-noise ratios (SNRs).”
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, background): “Therefore, although data-driven methods reduce the dependence on prior knowledge, the results are still highly dependent on experience [17].”
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, background): “[24] added a dislocated layer before convolutional layer by considering the properties of industrial signal, thus revealed the relationship between signals with inconsistent impact intervals.”