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Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2023年第1期36卷 1-18页

作者： Zuogang Shang Zhibin Zhao ruqiang yan The State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an710049China

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.

关键词： Signal processing Deep learning Explainable Denoising Fault diagnosis

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

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Chinese Journal of Mechanical Engineering 2021年第1期34卷 168-180页

作者： Chao Chen Fei Shen Jiawen Xu ruqiang yan School of Instrument Science and Engineering Southeast UniversityNanjing 210096China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an 710049China

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering ***,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more *** this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working *** on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are *** NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD *** experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working *** proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

关键词： Gear fault diagnosis Model parameter transfer Varying working conditions Least square support vector machine

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

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Chinese Journal of Mechanical Engineering 2020年第1期33卷 182-196页

作者： Fei Shen Chao Chen Jiawen Xu ruqiang yan School of Instrument Science and Engineering Southeast UniversityNanjing 210096China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same *** this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking *** short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration ***,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity ***,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column ***,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic *** Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.

关键词： Gear fault diagnosis Non-negative matrix factorization Co-clustering Varying working conditions

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 130-145页

作者： Weixin Xu Huihui Miao Zhibin Zhao Jinxin Liu Chuang Sun ruqiang yan School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health *** of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and *** this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear *** 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 *** 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 *** the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear *** 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.

关键词： Tool wear prediction Multi-scale Convolutional neural networks Gated recurrent unit

Nonlinear dynamic behavior of rotating blade with breathing crack

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Frontiers of Mechanical Engineering 2021年第1期16卷 196-220页

作者： Laihao yanG Zhu MAO Shuming WU Xuefeng CHEN ruqiang yan The State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China The Structural Dynamics and Acoustic Systems Laboratory University of Massachusetts LowellLowellMA 01854USA

This study aims at investigating the nonlinear dynamic behavior of rotating blade with transverse crack.A novel nonlinear rotating cracked blade model(NRCBM),which contains the spinning softening,centrifugal stiffening,Coriolis force,and crack closing effects,is developed based on continuous beam theory and strain energy release rate *** rotating blade is considered as a cantilever beam fixed on the rigid hub with high rotating speed,and the crack is deemed to be open and close continuously in a trigonometric function way with the blade *** is verified by the comparison with a finite element-based contact crack model and bilinear model that the proposed NRCBM can well capture the dynamic characteristics of the rotating blade with breathing *** dynamic behavior of rotating cracked blade is then investigated with NRCBM,and the nonlinear damage indicator(NDI)is introduced to characterize the nonlinearity caused by blade *** results show that NDI is a distinguishable indicator for the severity level estimation of the crack in rotating *** is found that severe crack(i.e.,a closer crack position to blade root as well as larger crack depth)is expected to heavily reduce the stiffness of rotating blade and apparently result in a lower resonant ***,the super-harmonic resonances are verified to be distinguishable indicators for diagnosing the crack existence,and the third-order super-harmonic resonances can serve as an indicator for the presence of severe crack since it only distinctly appears when the crack is severe.

关键词： rotating blade breathing crack nonlinear vibration nonlinear damage indicator

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 3-31页

作者： Zhibin Zhao Jingyao Wu Tianfu Li Chuang Sun ruqiang yan Xuefeng Chen The State Key Laboratory for Manufacturing Systems Engineering School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China

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 *** 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 ***,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 *** 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.

关键词： Monitoring Diagnosis Prognosis PHM Artificial intelligence Deep learning

AI-Enabled Monitoring, Diagnosis & Prognosis

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 1-2页

作者： ruqiang yan Xuefeng Chen Weihua Li Robert X.Gao School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China SHIEN-MING WU School of Intelligent Engineering South China University of TechnologyGuangzhou 510641China Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOhio 44106-7222USA

The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering ***,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI *** main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in *** papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.

关键词： diagnosis prognosis bringing

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1317-1330页

作者： Jiaxin Ren Jingcheng Wen Zhibin Zhao ruqiang yan Xuefeng Chen Asoke K.Nandi School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical Engineering Brunel University LondonKingston LaneUxbridgeUB83PHUK IEEE

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art ***,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models *** this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called *** SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural *** on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault ***,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability ***,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

关键词： Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification.

Mesh relationship modeling and dynamic characteristic analysis of external spur gears with gear wear

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Frontiers of Mechanical Engineering 2022年第1期17卷 160-177页

作者： Zhixian SHEN Laihao yanG Baijie QIAO Wei LUO Xuefeng CHEN ruqiang yan School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710061China

Gear wear is one of the most common gear failures,which changes the mesh relationship of normal gear.A new mesh relationship caused by gear wear affects meshing excitations,such as mesh stiffness and transmission error,and further increases vibration and noise *** paper aims to establish the model of mesh relationship and reveal the vibration characteristics of external spur gears with gear wear.A geometric model for a new mesh relationship with gear wear is proposed,which is utilized to evaluate the influence of gear wear on mesh stiffness and unloaded static transmission error(USTE).Based on the mesh stiffness and USTE considering gear wear,a gear dynamic model is established,and the vibration characteristics of gear wear are numerically *** with the experimental results verifies the proposed dynamic model based on the new mesh *** numerical and experimental results indicate that gear wear does not change the structure of the spectrum,but it alters the amplitude of the meshing frequencies and their *** condition indicators,such as root-mean-square,kurtosis,and first-order meshing frequency amplitude,can be regarded as important bases for judging gear wear state.

关键词： gear wear mesh relationship mesh stiffness transmission error vibration characteristics