资源类型

期刊论文 278

年份

2023 32

2022 30

2021 25

2020 16

2019 18

2018 20

2017 22

2016 7

2015 12

2014 7

2013 2

2012 9

2011 10

2010 9

2009 8

2008 6

2007 10

2006 8

2005 5

2004 3

展开 ︾

关键词

故障诊断 6

卫星 3

故障 3

强震发生断层 2

深部裂缝带 2

肥胖 2

诊断 2

2-羟基丁酸 1

3D层位 1

AR模型 1

Beclin-1 1

COVID-19 1

CP）；符号间干扰（inter symbol interference， ISI）；载波间干扰（inter carrier interference 1

Cu(Inx 1

Ga1–x)Se2 1

Grade 91钢 1

ICI）；最大似然估计（maximum likelihood estimation 1

MLE） 1

OFDM 1

展开 ︾

检索范围：

排序： 展示方式：

Transient performance comparison of grid-forming converters with different FRT control strategies

《能源前沿（英文）》 2023年 第17卷 第2期   页码 239-250 doi: 10.1007/s11708-022-0856-2

摘要： Grid-forming converters (GFMs) are faced with the threat of transient inrush current and synchronization instability issues when subjected to grid faults. Instead of disconnecting from the grid unintentionally, GFMs are required to have fault ride through (FRT) capability to maintain safe and stable operation in grid-connected mode during grid fault periods. In recent studies, different FRT control strategies with distinguishing features and that are feasible for different operation conditions have been proposed for GFMs. To determine their application scope, an intuitive comparison of the transient performance of different FRT control strategies is presented in this paper. First, three typical FRT control strategies (virtual impedance, current limiters, and mode-switching control) are introduced and transient mathematical models are established. A detailed comparison analysis on transient inrush current and transient synchronization stability is then presented. The results will be useful for guiding the selection and design of FRT control strategies. Finally, simulation results based on PSCAD/EMTDC are considered to verify the correctness of the theoretical analysis.

关键词： grid-forming converters (GFMs)     fault ride through (FRT)     transient stability     transient inrush current     transient modeling    

HTML PDF 收藏

Understanding network travel time reliability with on-demand ride service data

Xiqun (Michael) CHEN, Xiaowei CHEN, Hongyu ZHENG, Chuqiao CHEN

《工程管理前沿（英文）》 2017年 第4卷 第4期   页码 388-398 doi: 10.15302/J-FEM-2017046

摘要： Travel time reliability is of increasing importance for travelers, shippers, and transportation managers because traffic congestion has become worse in major urban areas in recent years. To better evaluate the urban network-wide travel time reliability, five indices based on the emerging on-demand ride service data are proposed: network free flow time rate (NFFTR), network travel time rate (NTTR), network planning time rate (NPTR), network buffer time rate (NBTR), and network buffer time rate index (NBTRI). These indices take into account the probability distribution of the travel time rate (i.e., travel time spent for the unit distance, in min/km) of each origin-destination (OD) pair in the road network. We use real-world data extracted from DiDi-Chuxing, which is the largest on-demand ride service platform in China. For demonstrative purposes, the network-wide travel time reliability of Beijing is analyzed in detail from two dimensions of time and space. The results show that the road network is more unreliable in AM/PM peaks than other time periods, and the most reliable time period is the early morning. Additionally, we can find that the central region is more unreliable than other regions of the city based on the spatial analysis results. The proposed network travel time reliability indices provide insights for the comprehensive evaluation of the road network traffic dynamics and day-to-day travel time variations.

关键词： network travel time reliability     on-demand ride services     travel time rate     OD    

HTML PDF 收藏

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

《机械工程前沿（英文）》 2014年 第9卷 第2期   页码 130-141 doi: 10.1007/s11465-014-0298-6

摘要：

Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.

关键词： Fault detection     spline wavelet     continuous wavelet transform     fast Fourier transform    

HTML PDF 收藏

宇航员首次乘坐私人火箭到达空间站

Chris Palmer

《工程（英文）》 2020年 第6卷 第11期   页码 1207-1209 doi: 10.1016/j.eng.2020.08.007

HTML PDF 收藏

Three-dimensional finite difference analysis of shallow sprayed concrete tunnels crossing a reverse faultor a normal fault: A parametric study

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 998-1011 doi: 10.1007/s11709-020-0621-8

摘要： Urban tunnels crossing faults are always at the risk of severe damages. In this paper, the effects of a reverse and a normal fault movement on a transversely crossing shallow shotcreted tunnel are investigated by 3D finite difference analysis. After verifying the accuracy of the numerical simulation predictions with the centrifuge physical model results, a parametric study is then conducted. That is, the effects of various parameters such as the sprayed concrete thickness, the geo-mechanical properties of soil, the tunnel depth, and the fault plane dip angle are studied on the displacements of the ground surface and the tunnel structure, and on the plastic strains of the soil mass around tunnel. The results of each case of reverse and normal faulting are independently discussed and then compared with each other. It is obtained that deeper tunnels show greater displacements for both types of faulting.

关键词： urban tunnel     sprayed concrete     reverse fault     normal fault     finite difference analysis    

HTML PDF 收藏

基于矿物异常分析的隧道内不良地质识别方法及案例分析 Article

许振浩, 余腾飞, 林鹏, 李术才

《工程（英文）》 2023年 第27卷 第8期   页码 150-160 doi: 10.1016/j.eng.2022.09.013

摘要： 本研究提出了一种基于矿物异常分析的隧道不良地质识别方法（adverse geology identification through mineral anomaly analysis, AGIMAA），

关键词： 矿物异常     不良地质     断层     蚀变     异常阈值    

HTML PDF 收藏

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

《机械工程前沿（英文）》 2018年 第13卷 第2期   页码 264-291 doi: 10.1007/s11465-018-0472-3

摘要：

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

关键词： fault diagnosis     fault mechanism     feature extraction     signal processing     intelligent diagnostics    

HTML PDF 收藏

Machine learning for fault diagnosis of high-speed train traction systems: A review

《工程管理前沿（英文）》 doi: 10.1007/s42524-023-0256-2

摘要： High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

关键词： high-speed train     traction systems     machine learning     fault diagnosis    

HTML PDF 收藏

Acoustic fault signal extraction via the line-defect phononic crystals

《机械工程前沿（英文）》 2022年 第17卷 第1期   页码 10-10 doi: 10.1007/s11465-021-0666-y

摘要： Rotating machine fault signal extraction becomes increasingly important in practical engineering applications. However, fault signals with low signal-to-noise ratios (SNRs) are difficult to extract, especially at the early stage of fault diagnosis. In this paper, 2D line-defect phononic crystals (PCs) consisting of periodic acrylic tubes with slit are proposed for weak signal detection. The defect band, namely, the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity. The noise can be filtered by the band gap. As a result, fault signals with high SNRs can be obtained for fault feature extraction. The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies. All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals. This work will provide potential for extracting weak signals in many practical engineering applications.

关键词： phononic crystals     line-defect     fault signal extraction     acoustic enhancement    

HTML PDF 收藏

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

《机械工程前沿（英文）》 2023年 第18卷 第1期 doi: 10.1007/s11465-022-0725-z

摘要： As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

关键词： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information fusion    

HTML PDF 收藏

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

HTML PDF 收藏

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

HTML PDF 收藏

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

《机械工程前沿（英文）》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0713-3

摘要： Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time. For highly integrated complicated mechanical systems, the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission. Therefore, using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths, but accompanied by huge scientific challenges. This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration. The gear meshing stiffness (GMS) identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description. Specifically, the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces. Then the dynamic differential equations are inverted to identify the GMS. Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads. Some interesting properties are discovered in the identified GMS spectra, and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process. The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.

关键词： gearbox fault diagnosis     meshing stiffness     identification     transfer path     signal processing    

HTML PDF 收藏

Heavy vehicle dynamics with balanced suspension based on enveloping tire model

Yongjie LU, Shaopu YANG, Shaohua LI

《机械工程前沿（英文）》 2010年 第5卷 第4期   页码 476-482 doi: 10.1007/s11465-010-0120-z

摘要： The tire-road contact mechanics is the key problem in vehicle ride comfort and road-friendliness research. A flexible roller contact (FRC) tire model with the enveloping property is introduced to reflect the contact history between the tire and the road. Based on D’Alembert principle, an integral balanced suspension (IBS) model is established, considering mass and moment of? inertia of? the stabilizer rod. ?The sprung mass accelera- tion and tire dynamic force for balanced suspension and the traditional quarter-vehicle model are compared respectively for frequency and time domain responses. It is concluded that the quarter-vehicle model can be used to evaluate the ride comfort of vehicles; however, it has some limitations in evaluating the vehicle road-friendliness. Then, the dynamics performances for IBS model are analyzed with the single point contact (SPC) model and FRC model, respectively. These works are expected to propose a new idea for the vehicle-road interaction research.

关键词： heavy vehicle     integral balanced suspension     enveloping properties     ride comfort     road-friendliness    

HTML PDF 收藏

Finite element simulation for elastic dislocation of the North-Tehran fault: The effects of geologic

Pooya ZAKIAN; Hossein ASADI HAYEH

《结构与土木工程前沿（英文）》 2022年 第16卷 第4期   页码 533-549 doi: 10.1007/s11709-022-0802-8

摘要： The present study uses the finite element method for simulating the crustal deformation due to the dislocation of a segment of the North-Tehran fault located in the Karaj metropolis region. In this regard, a geological map of Karaj that includes the fault segment is utilized in order to create the geometry of finite element model. First, finite element analysis of homogeneous counterpart of the fault’s domain with two different sections was performed, and the results were compared to those of Okada’s analytical solutions. The fault was modeled with the existing heterogeneity of the domain having been considered. The influences of both uniform and non-uniform slip distributions were investigated. Furthermore, three levels of simplification for geometric creation of geological layers’ boundaries were defined in order to evaluate the effects of the geometric complexity of the geological layering on the displacement responses obtained with the finite element simulations. In addition to the assessment of slip distribution, layering complexity and heterogeneity, the results demonstrate both the capability and usefulness of the proposed models in the dislocation analysis for the Karaj segment of North-Tehran fault.

关键词： finite element method     fault dislocation     slip distribution     the North-Tehran fault     heterogeneity     geological layering    

HTML PDF 收藏

标题 作者 时间 类型 操作

Transient performance comparison of grid-forming converters with different FRT control strategies

期刊论文

Understanding network travel time reliability with on-demand ride service data

Xiqun (Michael) CHEN, Xiaowei CHEN, Hongyu ZHENG, Chuqiao CHEN

期刊论文

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

期刊论文

宇航员首次乘坐私人火箭到达空间站

Chris Palmer

期刊论文

Three-dimensional finite difference analysis of shallow sprayed concrete tunnels crossing a reverse faultor a normal fault: A parametric study

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

期刊论文

基于矿物异常分析的隧道内不良地质识别方法及案例分析

许振浩, 余腾飞, 林鹏, 李术才

期刊论文

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

期刊论文

Machine learning for fault diagnosis of high-speed train traction systems: A review

期刊论文

Acoustic fault signal extraction via the line-defect phononic crystals

期刊论文

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

期刊论文

Heavy vehicle dynamics with balanced suspension based on enveloping tire model

Yongjie LU, Shaopu YANG, Shaohua LI

期刊论文

Finite element simulation for elastic dislocation of the North-Tehran fault: The effects of geologic

Pooya ZAKIAN; Hossein ASADI HAYEH

期刊论文