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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

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 level.This 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 studied.Comparison with the experimental results verifies the proposed dynamic model based on the new mesh relationship.The 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 sidebands.Several 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

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 applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based 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 unavailable.For 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 task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The 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 time.In 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 solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The 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

Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data

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Journal of Dynamics, Monitoring and Diagnostics 2023年第1期2卷 30-41页

作者： Hui Wang Jiawen Xu ruqiang yan School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Instrument Science and Engineering Southeast UniversityNanjing 210096China

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.

关键词： convolutional neural network deep reinforcement learning gearbox fault diagnosis transfer learning

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 method.The 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 vibration.It 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 crack.The 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 crack.The results show that NDI is a distinguishable indicator for the severity level estimation of the crack in rotating blade.It 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 frequency.Meanwhile,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

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 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.

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

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 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.

关键词： Monitoring Diagnosis Prognosis PHM Artificial intelligence Deep learning

基于CBAM-CNN和压电悬臂梁的温度解耦质量感知方法

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仪器仪表学报 2024年第4期45卷 113-126页

作者：闫宇楠刘智康徐佳文严如强东南大学仪器科学与工程学院南京210096 机器人感知与控制技术重点实验室南京210096 西安交通大学高端装备研究院西安710049

悬臂梁结构广泛用于微小质量测量,而温度变化会引起测量结果漂移。传统测量方法需要在温度稳定的环境中进行,但实际应用中通常难以满足此要求,且温度漂移对测量的影响难以直接解耦。本文提出了一种基于数据驱动,CBAM-CNN和压电悬臂梁的... 详细信息

悬臂梁结构广泛用于微小质量测量,而温度变化会引起测量结果漂移。传统测量方法需要在温度稳定的环境中进行,但实际应用中通常难以满足此要求,且温度漂移对测量的影响难以直接解耦。本文提出了一种基于数据驱动,CBAM-CNN和压电悬臂梁的自适应温度解耦质量感知方法。首先,搭建谐振式压电悬臂梁温控测量平台采集不同质量负载下的阻抗响应信号,设计自适应加权预处理方法以增强结构特征并突出有限样本中的关键信息;其次,设计基于混合领域注意力机制的CBAM-CNN网络来评估信号中多个谐振峰的相对关系,实现温度解耦和质量感知。实验结果表明,该方法在25℃至55℃的温度范围内的对0.1~1 g的质量感知准确率高达99.70%,无需进行温度补偿即可实现大跨度温度下的精确质量感知。

关键词：压电悬臂梁深度学习 CNN CBAM 质量感知温度解耦