Search Results - "Yan, Ruqiang"

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning by Shao, Siyu, McAleer, Stephen, Yan, Ruqiang, Baldi, Pierre

“…We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of…”
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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks by Zhao, Rui, Yan, Ruqiang, Wang, Jinjiang, Mao, Kezhi

“…In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among…”
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LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification by Hou, Borui, Yang, Jianyong, Wang, Pu, Yan, Ruqiang

“…This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector…”
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Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks by Zhao, Rui, Wang, Dongzhe, Yan, Ruqiang, Mao, Kezhi, Shen, Fei, Wang, Jinjiang

“…In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures…”
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Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter by Qian, Yuning, Yan, Ruqiang

“…This paper presents an enhanced particle filter (PF) approach for predicting remaining useful life (RUL) of rolling bearings. In the presented approach,…”
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Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis by Li, Tianfu, Zhao, Zhibin, Sun, Chuang, Yan, Ruqiang, Chen, Xuefeng

“…Deep learning (DL) based methods have swept the field of mechanical fault diagnosis, because of the powerful ability of feature representation. However, many…”
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Deep learning and its applications to machine health monitoring by Zhao, Rui, Yan, Ruqiang, Chen, Zhenghua, Mao, Kezhi, Wang, Peng, Gao, Robert X.

“…•We conduct a detailed review of the applications of recent deep learning models on machine health monitoring tasks and provide our own insights into these…”
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Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis by Du, Zhaohui, Chen, Xuefeng, Zhang, Han, Yan, Ruqiang

“…A primary challenge in fault diagnosis is to extract multiple components entangled within a noisy observation. Therefore, this paper describes and analyzes a…”
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Hilbert-Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring by Ruqiang Yan, Gao, R.X.

“…This paper presents a signal analysis technique for machine health monitoring based on the Hilbert-Huang Transform (HHT). The HHT represents a time-dependent…”
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Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis by Sun, Wenjun, Zhao, Rui, Yan, Ruqiang, Shao, Siyu, Chen, Xuefeng

“…A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes back-propagation…”
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Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines by Yan, Ruqiang, Liu, Yongbin, Gao, Robert X.

“…This paper investigates the usage of permutation entropy for working status characterization of rotary machines. As a statistical measure, the permutation…”
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A deep learning-based approach to material removal rate prediction in polishing by Wang, Peng, Gao, Robert X., Yan, Ruqiang

“…Prediction of material removal rate (MRR) during chemical mechanical polishing is critical for product quality control. Complexity involved in polishing makes…”
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Virtualization and deep recognition for system fault classification by Wang, Peng, Ananya, Yan, Ruqiang, Gao, Robert X.

“…Efficient gearbox health monitoring and effective representation of diagnostic results of dynamical systems have remained challenging. In this paper, a new…”
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Long short-term memory for machine remaining life prediction by Zhang, Jianjing, Wang, Peng, Yan, Ruqiang, Gao, Robert X.

“…•Variation pattern of system state estimated through variation pattern of sensing data.•Two system degradation stages revealed by mapping from sensing data to…”
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Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter by Qian, Yuning, Yan, Ruqiang, Hu, Shijie

“…This paper presents an integrated approach, which combines recurrence quantification analysis (RQA) with the Kalman filter, for bearing degradation evaluation…”
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Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review by Zhao, Zhibin, Wu, Jingyao, Li, Tianfu, Sun, Chuang, Yan, Ruqiang, Chen, Xuefeng

“…Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in…”
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Energy-Based Feature Extraction for Defect Diagnosis in Rotary Machines by Ruqiang Yan, Gao, R.X.

“…This paper presents an energy-based approach to defect diagnosis in rotary machines and machine components, which enhances the ability of the continuous…”
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Denoising Fault-Aware Wavelet Network: A Signal Processing Informed Neural Network for Fault Diagnosis by Shang, Zuogang, Zhao, Zhibin, Yan, Ruqiang

“…Deep learning (DL) is progressively popular as a viable alternative to traditional signal processing (SP) based methods for fault diagnosis. However, the lack…”
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Approximate Entropy as a diagnostic tool for machine health monitoring by Yan, Ruqiang, Gao, Robert X.

“…This paper presents a new approach to machine health monitoring based on the Approximate Entropy ( ApEn), which is a statistical measure that quantifies the…”
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Prognosis of Defect Propagation Based on Recurrent Neural Networks by Malhi, A, Ruqiang Yan, Gao, R X

“…Incremental training is commonly applied to training recurrent neural networks (RNNs) for applications involving prognosis. As the number of prognostic…”
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