Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

By Rui Zhao, Dongzhe Wang, Ruqiang Yan, Kezhi Mao, Fei Shen, Jinjiang Wang. IEEE transactions on industrial electronics (1982. Print), 2018

735 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b87c0cf95208caacb025bf87d9ba451a87aacaca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ming Zhao
2. Jing Lin
3. Kaixuan Liang
4. Jinyang Jiao
5. Rui Zhao
6. Ruqiang Yan
7. Cheng-Geng Huang
8. Min Wu

In Mechanical Engineering

1. Ruqiang Yan
2. Rui Zhao
3. Cheng-Geng Huang
4. Hongzhong Huang
5. Yanfeng Li
6. Min Wu
7. Yong Qin
8. Dong Yang

Citations in the corpus

• A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, background): “, physics-model-based approaches [8], statistical-based approaches [9]–[11], artificial intelligence (AI) approaches [12]–[14], and hybrid approaches [15].”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, methodology): “[27] designed a deep learning model named Convolutional Bi-directional Long ShortTermMemory Networks (CBLSTM) by combining CNN and LSTM.”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, background): “However, the conventional RNN often suffers from the problem of gradient vanishing or exploding during the network training, which greatly degrades its performance on modeling long-term dependencies [28].”
• Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, background): “[32] proposed a local feature-based gated unit and achieved good performance in three machine health monitoring tasks.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “The popular deep learning based diagnostic models include deep auto-encoder [11], deep belief network [12], recurrent neural network [13], and convolutional neural network (CNN) [14].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “TABLE III SUMMARY OF STATISTICAL FEATURES IN TIME DOMAIN AND FREQUENCY DOMAIN ACCORDING TO [53]–[55]”
• Deep Learning Based Vulnerability Detection: Are We There Yet? (2020, positive): “Where, GRUð Þ is a Gated Recurrent Function, xv is the embedding of the current vertex v, and gð Þ is a transformation function that assimilates the embeddings of all of vertex v’s neighbors [32], [33], [34].”
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, neutral): “performance evaluation, which is commonly used in the literature [29], [36].”
• Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction (2020, methodology): “For Ex-1 and Ex-2, we adopted GRU [50], Bi-GRU [51], and RETAIN [12] as our base models.”