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Laboratory diagnosis for malaria in the elimination phase in China: efforts and challenges

《医学前沿（英文）》 2022年第16卷第1期页码 10-16 doi: 10.1007/s11684-021-0889-7

摘要： Malaria remains a global health challenge, although an increasing number of countries will enter pre-elimination and elimination stages. The prompt and precise diagnosis of symptomatic and asymptomatic carriers of Plasmodium parasites is the key aspect of malaria elimination. Since the launch of the China Malaria Elimination Action Plan in 2010, China has formulated clear goals for malaria diagnosis and has established a network of malaria diagnostic laboratories within medical and health institutions at all levels. Various external quality assessments were implemented, and a national malaria diagnosis reference laboratory network was established to strengthen the quality assurance in malaria diagnosis. Notably, no indigenous malaria cases have been reported since 2017, but the risk of re-establishment of malaria transmission cannot be ignored. This review summarizes the lessons about malaria diagnosis in the elimination phase, primarily including the establishments of laboratory networks and quality control in China, to better improve malaria diagnosis and maintain a malaria-free status. A reference is also provided for countries experiencing malaria elimination.

关键词： malaria laboratory diagnosis quality control malaria elimination China

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Biosensor-based assay of exosome biomarker for early diagnosis of cancer

《医学前沿（英文）》 2022年第16卷第2期页码 157-175 doi: 10.1007/s11684-021-0884-z

摘要： Cancer imposes a severe threat to people’s health and lives, thus pressing a huge medical and economic burden on individuals and communities. Therefore, early diagnosis of cancer is indispensable in the timely prevention and effective treatment for patients. Exosome has recently become an attractive cancer biomarker in noninvasive early diagnosis because of the unique physiology and pathology functions, which reflects remarkable information regarding the cancer microenvironment, and plays an important role in the occurrence and evolution of cancer. Meanwhile, biosensors have gained great attention for the detection of exosomes due to their superior properties, such as convenient operation, real-time readout, high sensitivity, and remarkable specificity, suggesting promising biomedical applications in the early diagnosis of cancer. In this review, the latest advances of biosensors regarding the assay of exosomes were summarized, and the superiorities of exosomes as markers for the early diagnosis of cancer were evaluated. Moreover, the recent challenges and further opportunities of developing effective biosensors for the early diagnosis of cancer were discussed.

关键词： biosensor exosome cancer diagnosis

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Tomographic diagnosis of defects in hydraulic concrete structure

ZHAO Mingjie, XU Xibin

《结构与土木工程前沿（英文）》 2008年第2卷第3期页码 226-232 doi: 10.1007/s11709-008-0027-5

摘要： The ultrasonic tomographic technology is applied to diagnose the defects in hydraulic concrete structure. In order to improve the precision of diagnoses, the wavelet transformation is used in the processing of ultrasonic signals. The influences of water, scale and orientation of defect, processing methods and theoretical model on image resolution are investigated. The experimental results indicate that the result of the tomographic diagnosis of a single defect is sensitive and the boundary can be clearly determined. However, the image resolution of multiple defects is not satisfactory. The water content and scale of a defect may significantly affect the imaging resolution. Defects with the orientation perpendicular to the direction of the diagnosis may have higher precision in diagnosing. The wavelet transformation technology can elevate the imaging resolution. The applied calculation model plays a very important role in improving the accuracy of detection.

关键词： satisfactory processing orientation tomographic diagnosis orientation perpendicular

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

《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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Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

《机械工程前沿（英文）》 2022年第17卷第3期 doi: 10.1007/s11465-022-0689-z

摘要： Physical models carry quantitative and explainable expert knowledge. However, they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model. The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations. The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model. The method is validated on simulated and experimental cases. Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations. The presented work provides a quantitative, explainable, and feasible approach for identifying the cause of gas face seal contact. It is also applicable to mechanical devices that face similar difficulties.

关键词： surrogate model gas face seal fault diagnosis nonlinear dynamics tribology

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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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Pathogenesis, diagnosis, and treatment of recurrent spontaneous abortion with immune type

Qi-De LIN, Li-Hua QIU

《医学前沿（英文）》 2010年第4卷第3期页码 275-279 doi: 10.1007/s11684-010-0101-y

摘要： Recurrent spontaneous abortion (RSA), defined as three or more consecutive pregnancy losses before 20 weeks of gestation, is difficult to treat in the clinical setting. It affects 1%–5% of women of reproductive age. In the investigations of immunopathogenesis, diagnosis, and treatment of RSA since the late 1980s, it was found that RSA was associated with abnormal maternal local or systemic immune response. The pathogenesis of autoimmune RSA was mainly associated with antiphospholipid antibody (APA), while that of alloimmune RSA was due to the disturbance of maternofetal immunological tolerance. Systemic etiological screening process and diagnosis systems of RSA with immune type were developed, and anticardiolipin (ACL or ACA) + anti-β2-GP1 antibody combining multiple assays for effective diagnosis of RSA with autoimmune type was first established. According to the dynamic monitoring of clinical parameters before and during gestation, low-dose, short-course, and individual immunosuppressive therapy and lymphocyte immunotherapy for RSA with immune type were carried out. The outcomes of the offsprings of patients with RSA were followed up, and the safety and validity of the therapies were confirmed. The research achievement leads to great progress in the diagnosis and treatment of RSA in China.

关键词： spontaneous abortion recurrent autoimmune alloimmune pathogenesis diagnosis immunotherapy

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Intelligent diagnosis methods for plant machinery

Huaqing WANG, Peng CHEN, Shuming WANG,

《机械工程前沿（英文）》 2010年第5卷第1期页码 118-124 doi: 10.1007/s11465-009-0084-z

摘要： This paper reports several intelligent diagnostic approaches based on artificial neural network and fuzzy algorithm for plant machinery, such as the diagnosis method using the wavelet transform, rough sets, and fuzzy neural network; the diagnosis method based on the sequential inference and fuzzy neural network; the diagnosis approach by the possibility theory and certainty factor model; and the diagnosis method on the basis of the adaptive filtering technique and fuzzy neural network. These intelligent diagnostic methods have been successfully applied to condition diagnosis in different types of practical plant machinery.

关键词： intelligent diagnosis neural network fuzzy algorithm adaptive filtering plant machinery

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Current situation and development of prenatal diagnosis in China

Xu-Ming BIAN, Qi GUO, Qing-Wei QI

《医学前沿（英文）》 2010年第4卷第3期页码 271-274 doi: 10.1007/s11684-010-0100-z

摘要： Prenatal screening and diagnosis are major methods for control of birth defects, which is a very important problem in China. Here, we review current situation and development of prenatal screening and diagnosis in mainland China, including prenatal screening and prenatal diagnosis of fetal chromosome abnormalities, non-invasive prenatal diagnostic techniques and prenatal diagnosis of monogenic diseases, polygenic disease and congenital metabolic diseases. We also discuss epidemiology of birth defects and genetic diseases in China and related ethical issues of prenatal diagnosis.

关键词： prenatal diagnosis prenatal screening China

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

《机械工程前沿（英文）》 2021年第16卷第4期页码 829-839 doi: 10.1007/s11465-021-0652-4

摘要： Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词： imbalanced fault diagnosis graph feature learning rotating machinery autoencoder

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Multislice computed tomography angiography in the diagnosis of cardiovascular disease: 3D visualizations

Zhonghua Sun

《医学前沿（英文）》 2011年第5卷第3期页码 254-270 doi: 10.1007/s11684-011-0153-7

摘要： Multislice computed tomography (CT) has been widely used in clinical practice for the diagnosis of cardiovascular disease due to its reduced invasiveness and high spatial and temporal resolution. As a reliable alternative to conventional angiography, multislice CT angiography has been recognized as the method of choice for detecting and diagnosing head and neck vascular disease, abdominal aortic aneurysm, aortic dissection, and pulmonary embolism. In patients with suspected coronary artery disease, although invasive coronary angiography still remains as the gold standard technique, multislice CT angiography demonstrates high diagnostic accuracy; in selected patients, it is considered as the first-line technique. The imaging diagnosis of cardiovascular disease is based on a combination of two-dimensional (2D) and three-dimensional (3D) visualization tools to enhance the diagnostic value. This is facilitated by reconstructed visualizations which provide additional information about the extent of the disease, an accurate assessment of the spatial relationship between normal structures and pathological changes, and pre-operative planning and post-procedure follow-up. The aim of the present article is to present an overview of the diagnostic performance of various 2D and 3D CT visualizations in cardiovascular disease, including multiplanar reformation, maximum intensity projection, volume rendering, and virtual intravascular endoscopy. The recognition of the potential value of these visualizations will assist clinicians in efficiently using the multislice CT imaging modality for the diagnostic management of patients with cardiovascular disease.

关键词： cardiovascular disease multislice computed tomography three-dimensional reconstruction diagnosis visualization

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

《机械工程前沿（英文）》 2022年第17卷第3期 doi: 10.1007/s11465-022-0692-4

摘要： Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

关键词： axial piston pump fault diagnosis convolutional neural network multi-sensor data fusion

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Distributed flexible reconfigurable condition monitoring and diagnosis technology

HU You-min, YANG Shu-zi, DU Run-sheng

《机械工程前沿（英文）》 2006年第1卷第3期页码 276-281 doi: 10.1007/s11465-006-0025-z

摘要： As manufacturing becomes increasingly decentralized, flexible and reconfigurable, more research needs to be done on monitoring and diagnosis technology that accommodate these new trends. The distributed condition monitoring and diagnosis technology based on the flexible and reconfigurable concept is studied here. A condition monitoring diagnosis model based on the distributed flexible and reconfigurable idea is proposed in this paper. The component makeup and functions of this model are discussed in detail. The model can fulfill in most instances the manufacturing system requirements for changing the configuration of the monitoring diagnosis system according to different manufacturing system configurations. This model also realizes the flexibility and reconfigurability of the monitoring diagnosis system in some degree. The model has already spawned a successful prototype for monitoring a chemical plant in accomplishing monitoring and control of the production process and equipment. Finally, some future research work is pointed out.

关键词： chemical diagnosis system configuration monitoring equipment

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Distributed monitoring and diagnosis system for hydraulic system of construction machinery

Xiaohu CHEN, Wenfeng WU, Hangong WANG, Yongtao ZHOU,

《机械工程前沿（英文）》 2010年第5卷第1期页码 106-110 doi: 10.1007/s11465-009-0089-7

摘要： This paper mainly presents a distributed monitoring and diagnosis system for the hydraulic system of construction machinery based on the controller area net (CAN) field bus. The hardware of the distributed condition monitoring and fault diagnosis system is designed. Its structure including the sensors, distributed data acquisition units, central signal processing unit, and CAN field bus is introduced. The software is also programmed. The general software design and its realization are studied in detail. The experiments and applications indicate that the distributed condition monitoring and fault diagnosis system can effectively realize its function of real-time online condition monitoring and fault diagnosis for the hydraulic system of construction machinery.

关键词： construction machinery hydraulic system distributed condition monitoring controller area net (CAN) field bus fault diagnosis

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标题作者时间类型操作

Laboratory diagnosis for malaria in the elimination phase in China: efforts and challenges

期刊论文

Biosensor-based assay of exosome biomarker for early diagnosis of cancer

期刊论文

Tomographic diagnosis of defects in hydraulic concrete structure

ZHAO Mingjie, XU Xibin

期刊论文

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

期刊论文

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

期刊论文

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

期刊论文

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

期刊论文

Pathogenesis, diagnosis, and treatment of recurrent spontaneous abortion with immune type

Qi-De LIN, Li-Hua QIU

期刊论文

Intelligent diagnosis methods for plant machinery

Huaqing WANG, Peng CHEN, Shuming WANG,

期刊论文

Current situation and development of prenatal diagnosis in China

Xu-Ming BIAN, Qi GUO, Qing-Wei QI

期刊论文

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

期刊论文

Multislice computed tomography angiography in the diagnosis of cardiovascular disease: 3D visualizations

Zhonghua Sun

期刊论文

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

期刊论文

Distributed flexible reconfigurable condition monitoring and diagnosis technology

HU You-min, YANG Shu-zi, DU Run-sheng

期刊论文

Distributed monitoring and diagnosis system for hydraulic system of construction machinery

Xiaohu CHEN, Wenfeng WU, Hangong WANG, Yongtao ZHOU,

期刊论文

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