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Experimental investigation and comparative study of inter-turn short-circuits and unbalanced voltage supply in induction machines

Fatima BABAA, Abdelmalek KHEZZAR, Mohamed el kamel OUMAAMAR

《能源前沿（英文）》 2013年第7卷第3期页码 271-278 doi: 10.1007/s11708-013-0258-6

摘要： A transient model for an induction machine with stator winding turn faults on a single phase is derived using reference frame transformation theory. The negative sequence component and the 3rd harmonic are often considered as accurate indicators. However, small unbalance in the supply voltage and/or in the machine structure that exists in any real system engenders the same harmonics components. In this case, it is too difficult to distinguish between the current harmonics due to the supply voltage and those originated by inter-turn short-circuit faults. For that, to have the correct diagnosis and to increase the sensitivity and the reliability of the diagnostic system, it is crucial to provide the relationship between the inter-turn short-circuits in the stator winding and the supply voltage imbalance through an accurate mathematical model and via a series of experimental essays.

关键词： induction machines fault indicator inter-turn short-circuit fault unbalance supply voltage

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Midline2 is overexpressed and a prognostic indicator in human breast cancer and promotes breast cancer

《医学前沿（英文）》 2021年第15卷第6期页码 942-942 doi: 10.1007/s11684-021-0876-z

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Investigating the role of exiting vehicles and turn indicator usage in gap acceptance at single-lane

Nathan P. BELZ

《结构与土木工程前沿（英文）》 2017年第11卷第1期页码 8-16 doi: 10.1007/s11709-016-0365-7

摘要： Roundabouts are becoming a preferred form of intersection control type due to their safety and capacity benefits. Yield control at roundabout entries inherently reduces fuel consumption and emissions when compared to other conventional intersection types. However, these benefits can be limited by poor driver behavior and judgment when entering the roundabout. This research addresses the nature of gap distributions and use of turn indicators by exiting vehicles at three single-lane roundabouts in Vermont, New York, and Alaska. Presented here is a comparison of vehicle headways measured at two different locations in each roundabout. Rejected headways are analyzed in the context of priority abstaining events when entering drivers yield to vehicles exiting on the same leg of the roundabout suggesting that “true” critical gaps are being overestimated. Results indicate that exiting vehicles, particularly those that do not use their turn indicators when departing from the major-stream of traffic have an influence on the entry decision of drivers on the same approach. This behavior is of particular concern for intersection efficiency (i.e., delay) and sustainability (i.e., excess fuel consumption and emissions). Results prompt the consideration of more consistent guidance on and enforcement of turn indicator use during roundabout negotiations.

关键词： roundabouts exiting vehicles turn indicator use headways

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Modeling effects of a tidal barrage on water quality indicator distribution in the Severn Estuary

Guanghai GAO, Roger A. FALCONER, Binliang LIN

《环境科学与工程前沿（英文）》 2013年第7卷第2期页码 211-218 doi: 10.1007/s11783-012-0464-1

摘要： In this study, emphasis has focused on assessing the potential hydro-environmental impacts of a barrage across the Severn Estuary, with a numerical model being developed and applied to the estuary to assess the impacts of proposed Severn Barrage on the hydrodynamic, sediment transport and faecal indicator organism distribution within the estuary. The results show that the Severn Barrage has the potential to reduce the tidal currents in a highly dynamic estuary. This leads to the reduction of suspended sediment concentrations, which in turn affects the bacterial transport processes which is highly related to the sediment transport processes.

关键词： water quality indicator Severn Estuary numerical model tidal energy

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Water environment security indicator system for urban water management

Tian HAO, Pengfei DU, Yun GAO

《环境科学与工程前沿（英文）》 2012年第6卷第5期页码 678-691 doi: 10.1007/s11783-012-0450-7

摘要： Water environment security (WES) is defined in terms of three aspects: first, that it meets basic demands due to industrial and domestic usage; second, that it protects public health from acute and chronic threats; and third, that measures are adopted to ensure sustainable ecological functioning of freshwater resources. Limitations associated with current water environment security indicators in China — including inefficiency in terms of environmental monitoring, inappropriate indicators and parameters that do not take sufficient account of local water characteristics, and a lack of management targets — leads to a failure of effective water management. To achieve better water environment management, a systematic approach, encompassing several steps including establish ideal indicator system and narrow down the scope, screen priority pollutants, identify local characteristics and organize discussion workshop, should be followed to establish a comprehensive water environment indicator system. A case study in Suzhou is included to demonstrate the detailed operational procedures used to assess the risks associated with poor management practices relating to water environment security as well as design an appropriate water environment security indicator system.

关键词： water environment security supervision and management monitoring parameter indicator system Suzhou

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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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China’s Agriculture Green Development: from Concept to Actions

《农业科学与工程前沿（英文）》 doi: 10.15302/J-FASE-2023512

摘要：

● A target-threshold indicator evaluation system is proposed to measure China’s agriculture transformation.

关键词： agricultural transformation Agriculture Green Development historical trend indicator system theoretical conception

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Midline2 is overexpressed and a prognostic indicator in human breast cancer and promotes breast cancer

null

《医学前沿（英文）》 2016年第10卷第1期页码 41-51 doi: 10.1007/s11684-016-0429-z

摘要：

Midline2 (MID2) is an ubiquitin-conjugating E2 enzyme linked to tumor progression and a novel interacting partner of breast cancer 1, early-onset (BRCA1). However, the role of MID2 in breast cancer remains unknown. This study investigated the expression, prognostic value, and role of MID2 in breast cancer. The expression of MID2 mRNA and protein was significantly upregulated in breast cancer tissue and established cell lines compared with that in normal breast epithelial cells and paired adjacent non-tumor tissue (P<0.001). Immunohistochemical analysis demonstrated that MID2 was overexpressed in 272 of 284 (95.8%) paraffin-embedded, archived breast cancer tissue. Moreover, MID2 expression increased with advanced clinical stage (P<0.001). High MID2 expression was significantly associated with advanced clinical stages and T, N, and M staging (all P<0.05). Univariate and multivariate analyses indicated that high MID2 expression was an independent prognostic factor for poor overall survival in the entire cohort (93.73 vs. 172.1 months; P<0.001, log-rank test) and in subgroups with stages Tis+ I+ II and III+ IV. Furthermore, 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide colony formation, and anchorage-independent growth ability assays were conducted. Results showed that siRNA silencing of MID2 expression significantly reduced MCF-7 and MDA-MB-231 cell proliferation in vitro and blocked the growth of MDA-MB-231 cell xenograft tumors in vivo (P<0.05). This study indicated that MID2 may be a novel prognostic marker and interventional target in breast cancer.

关键词： breast cancer MID2 proliferation overall survival xenograft

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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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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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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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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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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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标题作者时间类型操作

Experimental investigation and comparative study of inter-turn short-circuits and unbalanced voltage supply in induction machines

Fatima BABAA, Abdelmalek KHEZZAR, Mohamed el kamel OUMAAMAR

期刊论文

Midline2 is overexpressed and a prognostic indicator in human breast cancer and promotes breast cancer

期刊论文

Investigating the role of exiting vehicles and turn indicator usage in gap acceptance at single-lane

Nathan P. BELZ

期刊论文

Modeling effects of a tidal barrage on water quality indicator distribution in the Severn Estuary

Guanghai GAO, Roger A. FALCONER, Binliang LIN

期刊论文

Water environment security indicator system for urban water management

Tian HAO, Pengfei DU, Yun GAO

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

China’s Agriculture Green Development: from Concept to Actions

期刊论文

Midline2 is overexpressed and a prognostic indicator in human breast cancer and promotes breast cancer

null

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

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

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

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

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

期刊论文

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

期刊论文

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