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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    

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An end-to-end 3d seismic simulation of underground structures due to point dislocation source by using

Zhenning BA; Jisai FU; Zhihui ZHU; Hao ZHONG

《结构与土木工程前沿（英文）》 2022年 第16卷 第12期   页码 1515-1529 doi: 10.1007/s11709-022-0887-0

摘要： Based on the domain reduction idea and artificial boundary substructure method, this paper proposes an FK-FEM hybrid approach by integrating the advantages of FK and FEM (i.e., FK can efficiently generate high-frequency three translational motion, while FEM has rich elements types and constitutive models). An advantage of this approach is that it realizes the entire process simulation from point dislocation source to underground structure. Compared with the plane wave field input method, the FK-FEM hybrid approach can reflect the spatial variability of seismic motion and the influence of source and propagation path. This approach can provide an effective solution for seismic analysis of underground structures under scenario of earthquake in regions where strong earthquakes may occur but are not recorded, especially when active faults, crustal, and soil parameters are available. Taking Daikai subway station as an example, the seismic response of the underground structure is simulated after verifying the correctness of the approach and the effects of crustal velocity structure and source parameters on the seismic response of Daikai station are discussed. In this example, the influence of velocity structure on the maximum interlayer displacement angle of underground structure is 96.5% and the change of source parameters can lead to the change of structural failure direction.

关键词： source-to-structure simulation     FK-FEM hybrid approach     underground structures     point dislocation source    

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Adaptive simulation of wave propagation problems including dislocation sources and random media

Hassan YOUSEFI, Jamshid FARJOODI, Iradj MAHMOUDZADEH KANI

《结构与土木工程前沿（英文）》 2019年 第13卷 第5期   页码 1054-1081 doi: 10.1007/s11709-019-0536-4

摘要： An adaptive Tikhonov regularization is integrated with an h-adaptive grid-based scheme for simulation of elastodynamic problems, involving seismic sources with discontinuous solutions and random media. The Tikhonov method is adapted by a newly-proposed detector based on the MINMOD limiters and the grids are adapted by the multiresolution analysis (MRA) via interpolation wavelets. Hence, both small and large magnitude physical waves are preserved by the adaptive estimations on non-uniform grids. Due to developing of non-dissipative spurious oscillations, numerical stability is guaranteed by the Tikhonov regularization acting as a post-processor on irregular grids. To preserve waves of small magnitudes, an adaptive regularization is utilized: using of smaller amount of smoothing for small magnitude waves. This adaptive smoothing guarantees also solution stability without over smoothing phenomenon in stochastic media. Proper distinguishing between noise and small physical waves are challenging due to existence of spurious oscillations in numerical simulations. This identification is performed in this study by the MINMOD limiter based algorithm. Finally, efficiency of the proposed concept is verified by: 1) three benchmarks of one-dimensional (1-D) wave propagation problems; 2) P-SV point sources and rupturing line-source including a bounded fault zone with stochastic material properties.

关键词： adaptive wavelet     adaptive smoothing     discontinuous solutions     stochastic media     spurious oscillations     Tikhonov regularization     minmod limiter    

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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    

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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    

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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    

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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    

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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    

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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    

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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    

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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    

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Centrifuge experiments for shallow tunnels at active reverse fault intersection

Mehdi SABAGH, Abbas GHALANDARZADEH

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 731-745 doi: 10.1007/s11709-020-0614-7

摘要： Tunnels extend in large stretches with continuous lengths of up to hundreds of kilometers which are vulnerable to faulting in earthquake-prone areas. Assessing the interaction of soil and tunnel at an intersection with an active fault during an earthquake can be a beneficial guideline for tunnel design engineers. Here, a series of 4 centrifuge tests are planned and tested on continuous tunnels. Dip-slip surface faulting in reverse mechanism of 60-degree is modeled by a fault simulator box in a quasi-static manner. Failure mechanism, progression and locations of damages to the tunnels are assessed through a gradual increase in Permanent Ground Displacement (PGD). The ground surface deformations and strains, fault surface trace, fault scarp and the sinkhole caused by fault movement are observed here. These ground surface deformations are major threats to stability, safety and serviceability of the structures. According to the observations, the modeled tunnels are vulnerable to reverse fault rupture and but the functionality loss is not abrupt, and the tunnel will be able to tolerate some fault displacements. By monitoring the progress of damage states by increasing PGD, the fragility curves corresponding to each damage state were plotted and interpreted in related figures.

关键词： reverse fault rupture     continuous tunnel     geotechnical centrifuge     ground surface deformations     fragility curves    

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Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills

Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE

《机械工程前沿（英文）》 2010年 第5卷 第4期   页码 483-490 doi: 10.1007/s11465-010-0118-6

摘要： This paper presents an intelligent system that is necessary for diagnostic accuracy and efficiency in the iron and steel industry. A rule-based reseaning (RBR) intelligent diagnostic system has been developed based on many successful diagnostic applications. It can solve the difficulty in knowledge acquisition and has more precision. Its application results prove that the usability of the system is good and it will increasingly attain perfection.

关键词： rule-based reasoning     fault diagnosis     intelligent system     gear box    

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New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

《机械工程前沿（英文）》 2011年 第6卷 第2期   页码 249-253 doi: 10.1007/s11465-011-0124-3

摘要：

This paper introduces the basic conception of information fusion and some fusion diagnosis methods commonly used nowadays in rotating machinery. From the thought of the information fusion, a new quantitative feature index monitoring and diagnosing the vibration fault of rotating machinery, which is called distance of information entropy, is put forward on the basis of the singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet energy spectrum entropy, and wavelet space feature entropy in time-frequency domain. The mathematic deduction suggests that the conception of distance of information entropy is accordant with the maximum subordination principle in the fuzzy theory. Through calculation it has been proved that this method can effectively distinguish different fault types. Then, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of information entropy at multi-speed.

关键词： rotating machinery     information fusion     fault diagnosis     Information entropy     distance of the information entropy    

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Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

《机械工程前沿（英文）》 2017年 第12卷 第3期   页码 357-366 doi: 10.1007/s11465-017-0423-4

摘要：

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

关键词： wind turbine generator gearbox     µ-singular value decomposition     local mean decomposition     weak characteristic information extraction     early fault warning    

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标题 作者 时间 类型 操作

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

Pooya ZAKIAN; Hossein ASADI HAYEH

期刊论文

An end-to-end 3d seismic simulation of underground structures due to point dislocation source by using

Zhenning BA; Jisai FU; Zhihui ZHU; Hao ZHONG

期刊论文

Adaptive simulation of wave propagation problems including dislocation sources and random media

Hassan YOUSEFI, Jamshid FARJOODI, Iradj MAHMOUDZADEH KANI

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

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

Centrifuge experiments for shallow tunnels at active reverse fault intersection

Mehdi SABAGH, Abbas GHALANDARZADEH

期刊论文

Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills

Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE

期刊论文

New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

期刊论文

Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

期刊论文