This paper introduces the design of the parallel connection of DC/DC power supplies. The topology of system configuration and performed modeling were stated. The problems such as the ripple electric current sharing of...
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This paper presents an application of wavelet networks in identification and control design tor a class of non-linear dynamical systems. The technique of feedback linearization, supervisory control and H∞ control are...
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With the rapid advancements in space offensive and defensive countermeasure technologies, the scope of international confrontation has expanded beyond the traditional domains of sea, land, and air, reaching into the r...
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After a short view on the historical development of model-based fault detection some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated based on the work within the IFA...
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After a short view on the historical development of model-based fault detection some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated based on the work within the IFAC Technical Committee SAFEPROCESS. Some basic fault detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends in the application of model-based fault detection and diagnosis methods.
A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high freque...
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A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept *** a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is *** performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.
Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling ***,it needs to employ semi-supervised learning(SSL)metho...
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Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling ***,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product *** the slow time-varying characteristic of industrial processes,the model parameters should be updated *** to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as *** introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter *** evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and ***,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.
This paper deals with the electromagnetic analysis and comparison of the slotted and slotless high-speed permanent magnet (PM) brushless de (BLDC) motors adopting four different PM magnetizations (i.e., parallel magne...
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control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of fligh...
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ISBN:
(纸本)9781479928569
control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of flight and safety. Reliable diagnosis require well fitting dynamical models but with the high cost of detailed modelling and wind tunnel testing, it would be highly desirable if good diagnosis could be obtained with very generic models that are adapted to individual conditions of aircraft and of its operation. This paper presents an approach where a basic generic model is applied and necessary parameters in residual generators are identified on the fly. Initial estimates of parameters are known from off-line analysis of previous flights. The paper analyses how such self-tuning residual generators are combined with change detection to obtain timely fault diagnosis. The paper investigates the parameter convergence and detection properties for the suggested combination of identification and change detection techniques and shows design aspects and trade-offs to be made to make this scheme an effective and robust system for diagnosis or even prognosis. Results are verified using a number of test flights with different members of a population of UAVs that have inherent model uncertainty from one member to another and from one flight to another. Events with actual faults on control surfaces demonstrates the efficacy of the approach.
The stability of the microstructure and mechanical properties of the pre-hardened sheets during the pre-hardening forming(PHF)process directly determines the quality of the formed *** microstructure stability of the p...
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The stability of the microstructure and mechanical properties of the pre-hardened sheets during the pre-hardening forming(PHF)process directly determines the quality of the formed *** microstructure stability of the pre-hardened sheets was in-vestigated by differential scanning calorimetry(DSC),transmission electron microscopy(TEM),and small angle X-ray scattering(SAXS),while the mechanical properties and formability were analyzed through uniaxial tensile tests and formability *** results in-dicate that the mechanical properties of the pre-hardened alloys exhibited negligible changes after experiencing 1-month natural aging(NA).The deviations of ultimate tensile strength(UTS),yield strength(YS),and sheet formability(Erichsen value)are all less than 2%.Also,after different NA time(from 48 h to 1 month)is applied to alloys before pre-hardening treatment,the pre-hardened alloys possess stable microstructure and mechanical properties as ***,with the extension of NA time before pre-hardening treatment from 48 h to 1 month,the contribution of NA to the pre-hardening treatment is *** a yield strength increment of 20 MPa is achieved,with no loss in *** limited enhancement is mainly attributed to the fact that only a limited number of clusters are transformed into Guinier-Preston(GP)zones at the early stage of pre-hardening treatment,and the formation ofθ''phase inhibits the nucleation and growth of GP zones as the precipitated phase evolves.
The early detection and localization of faults in machines and drives is of primary interest to make further improvements of the reliability and safety. The conventional way is to monitor some important variables like...
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The early detection and localization of faults in machines and drives is of primary interest to make further improvements of the reliability and safety. The conventional way is to monitor some important variables like temperatures, pressures, vibrations and to generate alarms if certain limits are exceeded. However, developing internal process faults are only detected at a rather late stage. In recent years research has shown that many process faults can be detected earlier and localized better by using static and dynamic models together with some definite robust sensors. For machines and drives many faults appear as process and signal parameter changes. The contribution describes a general methodology for the fault diagnosis of machines and drives by using few sensors, dynamic process and signal models and parameter estimation. Changes of process and signal parameters are then symptoms, which are fed into a knowledge based fault diagnosis component. Then analytical and heuristic knowledge is treated via fault trees and plausibility measures. The results were obtained in several research projects with machine tools and robots driven by d. c., a.c. and synchroneous motors. Examples show some experimental results.
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