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| 1 | Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review显示文摘Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry.With the development of artificial intelligence(AI),especially deep learning(DL)approaches,the application of AI-enabled methods to monitor,diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry.However,there is still a gap to cover monitoring,diagnosis,and prognosis based on AI-enabled methods,simultaneously,and the importance of an open source community,including open source datasets and codes,has not been fully emphasized.To fill this gap,this paper provides a systematic overview of the current development,common technologies,open source datasets,codes,and challenges of AI-enabled PHM methods from three aspects of monitoring,diagnosis,and prognosis. | Zhibin Zhao Jingyao Wu Tianfu Li Chuang Sun Ruqiang Yan Xuefeng Chen | 2021 | Chinese Journal of Mechanical Engineering2021，34，3： | 8 |
| 2 | Highly efficient K-Fe/C catalysts derived from metal-organic frameworks towards ammonia synthesis显示文摘Fe-based catalysts have been discovered as the best elementary metal-based heterogeneous catalysts for the ammonia synthesis in industrial application during the last century.Herein,a novel and scalable strategy is developed to prepare the K-promoted Fe/C catalyst with extremely high Fe loading (> 50 wt.%) through pyrolysis of the Fe-based metal-organic framework (MOF) xerogel.The obtained K-Fe/C catalysts exhibited superior activity and stability towards ammonia synthesis.The weight-specific reaction rate of Fe/C with K2O as promoter can achieve 12.4 mmol·g-1·h-1 at 350 ℃ and 30.4 mmol·g-1·h-1 at 400 ℃,approximately four and two times higher than that of the commercial fused-iron catalyst (3.4 mmol·g-1·h-1 at 350 ℃ and 16.7 mmol·g-1·h-1 at 400 ℃) under the same condition,respectively.The excellent performance of K-Fe/C can be ascribed to the inherited structure derived from the metal-organic frame precursors and the promotion of potassium,which can modify the binding energy of reactant molecules on the Fe surface,transfer electrons to iron for effective activation of nitrogen,prevent agglomeration of Fe nanoparticle (NPs) and restrain side reaction of carbon matrix to methane. | Pengqi Yan Wenhan Guo Zibin Liang Wei Meng Zhen Yin Siwei Li Mengzhu Li Mengtao Zhang Jie Yan Dequan Xiao Ruqiang Zou Ding Ma | 2019 | Nano Research2019，12，9： | 4 |
| 3 | Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis显示文摘This paper presents a transfer learning-based approach for induction motor fault diagnosis,where the Transfer principal component analysis(TPCA)is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution difference between training and testing data by mapping cross-domain data into a shared latent space in which domain difference can be reduced. The trained model can achieve a good performance in testing data by using the learned features consisting of common latent principal components. Experimental results show that the proposed approach outperforms traditional machine learning techniques and can diagnose induction motor fault under various working conditions effectively. | YAN Ruqiang SHEN Fei ZHOU Mengjie | 2021 | Chinese Journal of Electronics2021，30，1： | 3 |
| 4 | Performance enhancement of ensemble empirical mode decomposition显示文摘 | Jian Zhang Ruqiang Yan Robert X. Gao Zhihua Feng | 2010 | Mechanical Systems and Signal Processing2010，，7： | 2 |
| 5 | Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis显示文摘Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et. | Zuogang Shang Zhibin Zhao Ruqiang Yan | 2023 | Chinese Journal of Mechanical Engineering2023，36，1： | 2 |
| 6 | Subspace- based gearbox condition monitoring by kernel principal component analysis 显示文摘 | He Qingbo Kong Fanrang Yan Ruqiang | 2007 | Mechanical Systems and Sig- nal Processing2007，21，4： | 1 |
| 7 | Performance enhancement of ensemble empirical mode decomposition 显示文摘 | JianZhang Ruqiang Yan Robert X Gao | 2010 | Mechanical Systems and Signal Processing2010，24，： | 1 |
| 8 | Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data显示文摘Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively. | Hui Wang Jiawen Xu Ruqiang Yan | 2023 | Journal of Dynamics, Monitoring and Diagnostics2023，2，1： | 1 |
| 9 | An efficient approach to machine health diagnosis based on harmonic wavelet packet transform显示文摘 | Yan Ruqiang Robert X Gao | 2005 | Robotics and Computer-integrated Manufacturing2005，21，45： | 1 |
| 10 | Energy-based feature extraction for defect diagnosis in rotary machines显示文摘 | YAN Ruqiang GAO R | 2009 | IEEE Transactions on Instrumentation and Measurement2009，58，9： | 1 |
| 11 | A tour of the Hilbert-Huang transform: an empirical tool for signal analysis显示文摘 | RUQIANG YAN GAO R X | 2007 | IEEE Instrumentation & Measurement Magazine2007，10，5： | 1 |
| 12 | Complexity as a measure for machine health evaluation显示文摘 | YAN Ruqiang GAO Robert X | 2004 | IEEE Transactions on Instrumentation and Measurement2004，53，4： | 1 |
| 13 | Machine health diagnosis based on approximate entropy 显示文摘 | YAN Ruqiang GAO Robert X | 2004 | IEEE Transactions on Instrumentation and Measurement2004，53，5： | 1 |
| 14 | Base Wavelet Selection for Bearing Vibration Signal Analysis 显示文摘 | Yan Ruqiang Gao R X | 2009 | International Journal of Wavelets Multiresolution & Information Pro- cessing2009，7，4： | 1 |
| 15 | Hilbert-Huang TransformBased Vibration Signal Analysis for Machine HealthMonitoring显示文摘 | Yan Ruqiang Gao R X | 2006 | IEEE Transactions on Instrumenta-tion and Measurement2006，55，6： | 1 |
| 16 | Approximate entropy as a diag- nostic tool {or machine health monitoring显示文摘 | YAN Ruqiang GAO R X | 2007 | Mechaneial Systems and Signal Processing2007，21，2： | 1 |
| 17 | Approximate Entropy as a diagnostic tool for machine health monitoring显示文摘 | Yan Ruqiang Gao Robert X | 2007 | Mechanical Systems and Signal Process- ing2007，21，2： | 1 |
| 18 | Wavelets for fault di- agnosis of rotary machines 显示文摘 | Yan Ruqiang Robert X Gao Chen Xuefeng | 2013 | Signal Processing2013，，4： | 1 |
| 19 | Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing显示文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models. | Weixin Xu Huihui Miao Zhibin Zhao Jinxin Liu Chuang Sun Ruqiang Yan | 2021 | Chinese Journal of Mechanical Engineering2021，34，3： | 1 |
| 20 | Approximate entropy as a diagnostic tool for machine health monitoring显示文摘 | Ruqiang Yan Robert X Gao | 2007 | Mechanical Systems and Signal Processing2007，21，2： | 1 |
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