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| 1 | Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots显示文摘A robust fault diagnosis approach is developed by incorporating a set-membership identification(SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation(FDI). | Bo Zhou Kun Qian Xudong Ma Xianzhong Dai | 2017 | Journal of Systems Engineering and Electronics2017，28，5： | 7 |
| 2 | Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships显示文摘In this paper, the regular characteristic of wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typical wear particles spectrum is established according to the equipment structure, friction and wear rule and the characteristic of wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification. | CHEN Chang zheng, LI Qing, SONG Hong ying Diagnosis and Control Center, Shenyang University of Technology, Shenyang 110023, P.R.China | 2001 | International Journal of Plant Engineering and Management2001，6，3： | 5 |
| 3 | MHSS ARAIM Algorithm Combined with Gross Error Detection显示文摘Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets with the larger computational load,a method combining MHSS ARAIM with gross error detection is proposed in this paper.The gross error detection method is used to identify and eliminate the gross data in the original data first,then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection.Therefore,this makes up for the weakness of the MHSS ARAIM algorithm.With the data processing and analysis from several international GNSS service(IGS)and international GNSS monitoring and assessment system(iGMAS)stations,the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service(LPV-200)when using GPS and BDS measure data.Under the assumption of a single-faulty satellite,the effective monitoring threshold(EMT)is improved about 22.47%and 9.63%,and the vertical protection level(VPL)is improved about 32.28%and 12.98%for GPS and BDS observations,respectively.Moreover,under the assumption of double-faulty satellites,the EMT is improved about 80.85%and 29.88%,and the VPL is improved about 49.66%and 18.24%for GPS and BDS observations,respectively. | Yabin ZHANG Li WANG Lihong FAN Xuanyu QU | 2020 | Journal of Geodesy and Geoinformation Science2020，3，1： | 3 |
| 4 | Exploration Technology for Complex Sandstone Reservoirs in the Developed Area of Shengli Oilfield显示文摘Jiyang depression, which is the main oil productive area of Shengli oil field, is located at the southeast part of the Bohai Bay Basin and is a terrestrial lacustrine rift subsidence basin formed in the late Mesozoic with fully developed fault system. The main hydrocarbon productive formations of this depression are the terrestrial clastic rocks of the Tertiary, which are of strong lateral variation. The complex fault reservoirs and subtle lithological reservoirs distributed extensively and are becoming the main exploration targets in recent years. The exploration and development practice in these years has formed the exploration technologies, mainly including detailed study and description of low grade faults, delineation of microstructures, facies constrained formation description and prediction and low resistivity oil bearing formation’s identification. These exploration technologies have resulted in remarkable effectiveness on the reserve and oil production increments. | Li Yang, Zhang Zonglin (Shengli Oilfield Company Ltd., SINOPEC, Shandong, Dongying 257001) | 2003 | 工程科学(英文版)2003，1，2： | 3 |
| 5 | Variable selection-based SPC procedures for high-dimensional multistage processes显示文摘Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift. | KIM Sangahn | 2019 | Journal of Systems Engineering and Electronics2019，30，1： | 2 |
| 6 | A Novel Parsimonious Neurofuzzy Model Applied to Railway Carriage System Identification and Fault Diagnosis显示文摘ANovelParsimoniousNeurofuzzyModelAppliedtoRailwayCariageSystemIdentificationandFaultDiagnosisS.C.Zhou,O.L.Shuai+,T.T.Wong?.. | S. C. Zhou *, O. L. Shuai +, T. T. Wong *, T. P. Leung * ** This Project is Supported Partly by National Natural Science Foundation of China(69572014) * Dept.ME,Hong Kong Polytechnic University,Hong Kong + Dept.EE,South China University of T | 1997 | International Journal of Plant Engineering and Management1997，2，4： | 1 |
| 7 | Intelligent Fault-tolerant Management of Electromechanical Equipment显示文摘InteligentFaulttolerantManagementofElectromechanicalEquipmentWangZhongshengLeiYongJinWeihuaNorthwesternPolytechnicalUniversi... | Wang Zhongsheng Lei Yong Jin Weihua Northwestern Polytechnical University Xi’an 710072, P.R.China | 1997 | International Journal of Plant Engineering and Management1997，2，3： | 1 |
| 8 | Development of fault section identification technique for low voltage DC distribution systems by using capacitive discharge current显示文摘The increasing importance of energy efficiency has led to several studies related to the construction of a reliable low voltage DC(LVDC) distribution system.Specifically, studies on a protection scheme that considers the fault characteristics of an LVDC distribution system are essential to improve system reliability. When compared to a conventional distribution system, the most distinct feature of an LVDC distribution system is the existence of a capacitive discharge current from converters under a fault condition that results in the prompt operation of protection devices. Therefore, this study involves proposing a precise and rapid technique to identify the fault section in an LVDC distribution system. The technique involves two stages, namely: an analysis stage to analyze the capacitive discharge current and a decision stage to identify the fault section based on the fault type. A detailed discussion of each step is presented and its feasibility is verified based on the results of simulations with an ElectroMagnetic Transients Program and MATLAB~?. | Chul-Ho NOH Chul-Hwan KIM Gi-Hyeon GWON Yun-Sik OH | 2018 | Journal of Modern Power Systems and Clean Energy2018，6，3： | 1 |
| 9 | 基于核时序结构独立元分析的非线性过程监控方法(英文)显示文摘Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA. | 蔡连芳 田学民 张妮 | 2014 | Chinese Journal of Chemical Engineering2014，22，Z1： | 1 |
| 10 | A fault identification based on the parameter variation of apparent current显示文摘A new fault identification method, which is called the apparent current method, based on the parameter variation of apparent current is proposed after the analysis of the limitations of the fault interpretation method for the wide field electromagnetic data in the non-seismic exploration for oil and gas exploration. This method takes the study of the wide field electromagnetic theory and the mechanism of the fault generation, this method takes the wide field electromagnetic data as the research object, and establishes the connection between the geoelectric section and the virtual equivalent circuit, and then uses the virtual equivalent circuit as the carrier, and applies the theoretical equation of the apparent current, and combines the geological background of the study area to achieve scientific inference for location of fault in wide field electromagnetic exploration data. Theoretical model tests and the application of practical data proved that the location of underground fault can be accurately deduced by the trend of apparent current in underground space, reducing the multiple interpretations of electromagnetic data interpretation. At the same time, it also verified the correctness of the theory of apparent current and the feasibility of the method of apparent current. | LI Junguang LI Diquan YANG Yang | 2018 | Petroleum Exploration and Development2018，45，3： | 0 |
| 11 | External Disturbance Detection and Its Application on the Identification of Fault in CMG System显示文摘CMGs(control moment gyros)as satellite actuators have intrinsic structures generating disturbance,like bearings,motors,high-speed rotating wheels,steering gimbal,etc.Disturbances induced by faults in some of these parts shall be detected immediately and identified in real time.A continuous second order sliding mode observer can be applied for the detection of disturbances.In this paper,a nonlinear sliding mode observing algorithm based on the gyro sensor is suggested for the detection of external disturbances.The algorithm is then applied to detect a fault in a CMG,here the wheel fluctuation fault.By distinguishing the direction of disturbance torque by a diagnosis algorithm,the fault CMG can be then identified and isolated from other normal CMGs.The performance of detecting algorithm is verified on the hardware satellite simulator,in which four CMGs are installed. | Jung-Hyung Lee Hun-Jo Lee Joon-Yong Lee Hwa-Suk Oh | 2018 | Journal of Energy and Power Engineering2018，12，6： | 0 |
| 12 | Seismic Attributes for Fractures and Structural Anomalies: Application in Malaysian Basin显示文摘Seismic attributes are proved to be powerful tools for studying geological features on seismic data. In Malaysian Basin, intense efforts starting from this decade in seismic data acquisition and processing have resulted in significant improvement in data quality and hence the success of attribute application. Depending on the method used to calculate the attribute as well as the structure’s characteristics, one attribute can be more convenient than others for the desired objectives. Here, the focus is on detecting faults and anticlines in Central Luconia, Sarawak Basin. Therefore, different attributes useful for this purpose have been examined and compared to select the optimum selection of the attributes for further study of the data. Based on the results, different attributes provide different information for the same geological event. So, it is better to combine the outputs obtained from attributes that can identify the anomaly at the required resolution level. | Rosita Hamidi Bashir Yasir Deva Ghosh | 2018 | Advances in Geoscience2018，2，1： | 0 |
| 13 | FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS显示文摘Fault management is crucial to pro vi de quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in mana gement information base, the algorithm is compatible with the existing simple ne twork management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identif iers are acquired from the network simulation. A k-nearest neighbor classif ier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, which prove the accuracy of the algo rithm. The identification errors are discussed to illustrate that the novel faul t identification algorithm is adaptive in the fault scenarios with network traff ic change. | 孙钦东 张德运 孙朝晖 | 2004 | Journal of Pharmaceutical Analysis2004，16，2： | 0 |
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