A method for support stiffness failure identification in a steel spring floating slab track of urban railway: a case study in China

The extensive use of steel spring floating slab tracks has effectively addressed the challenge of alleviating the environmental vibrations induced by urban rail transit systems.However,under the combined action of train dynamic loads and complex environmental factors,problems,such as the fracture of steel spring vibration isolators and suspension vibrations induced by the uneven settlement of the base,often occur.The failure of isolator support stiffness is often hidden in its early stages and is challenging to identify by conventional detection methods.At the same time,it will aggravate the wheel-rail interaction,accelerate the deterioration of track structure,and even affect the driving safety.This study first establishes a detailed coupled train-floating slab track-foundation analytical model.Then the influence of the vibration isolator support stiffness failure on the dynamic indices of the floating slab track system response is analyzed.A set of defect identification methods that can detect the number of failed steel springs,severity of damage,and their location is proposed.Finally,an intelligent monitoring system for support stiffness of floating slab track is built by combining the density-based spatial clustering of applications with noise algorithm and statistical data analysis and is applied to a rail line in southern China.During a three-year monitoring campaign,a suspension failure and a fracture of a steel spring were each successfully detected and detailed failure information was obtained.Field investigation results were consistent with the damage identification results.After repair,the track structure dynamic response returned to the average pre-damage level and further deterioration had been arrested.The proposed damage identification methods and monitoring system provide an approach for intelligent identification of track structure support stiffness failures.

Improved Identification Method and Fault Current Limiting Strategy for Commutation Failure in LCC-HVDC

Line-commutated converter based high-voltage direct-current(LCC-HVDC)transmission systems are prone to subsequent commutation failure(SCF),which consequently leads to the forced blocking of HVDC links,affecting the operation of the power system.An accurate commutation failure(CF)identification is fairly vital to the prevention of SCF.However,the existing CF identification methods cause CF misjudge or detection lag,which can limit the effect of SCF mitigation strategy.In addition,earlier approaches to suppress SCF do not clarify the key factor that determines the evolution of extinction angle during system recovery and neglect the influence.Hence,this paper firstly analyzes the normal commutation process and CF feature based on the evolution topology of converter valve conduction in detail.Secondly,the energy in the leakage inductance of converter transformer is presented to characterize the commutation state of the valves.Then a CF identification method is proposed utilizing the leakage inductance energy.Thirdly,taking the key variable which is crucial to the tendency of extinction angle during the recovery process into account,a fault current limiting strategy for SCF mitigation is put forward.Compared with the original methods,the proposed methods have a better performance in CF identification and mitigation in terms of detection accuracy and mitigation effect.Finally,case study on PSCAD/EMTDC validates the proposed methods.

Generalized reliability measures of Kalman filtering for precise point positioning

To deal with the adverse influence of model failures on Kalman filtering （KF） estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification method as well as the separability and reliability theories. Although the generalized reliability theory for the least square has been discussed for many decades, the generalized reliability theory of KF is not widely discussed. Compared with the least square, KF includes not only the measurement model, but also the dynamic model. In KF, the predicted value of the state parameters from the dynamic model is considered as pseudomeasurements and combined with the observed measurements to compose the form of the least square. According to the reliability of the least square, the generalized reliability of KF is derived. Then, the dynamic model failure of precise point positioning is simulated to demonstrate the usage of the generalized reliability theory. The results show that the adverse influence of the dynamic model failure is more severe than that of the measurement model. Moreover, it is recommended that the model failure identification should always be used even if the overall model test passes. It is shown that the derived generalized reliability measures are suitable for the generalized KF estimation.

实例推理中遗传训练算法用于机械失效模式识别的研究(英文)

The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.

When identity is not reached:two cases from Brasilia

When human remains are examined,three questions always need to be answered:who is the deceased,what was the cause of death,and when did the death occur,the former question being the most relevant.The identification of half or fully skeletonized human remains is a complex process and always requires the use of methods that allow individualization beyond any reasonable doubt.However,no matter how vigorous the search for identification,this is not always achieved.Here,the author presents two cases in which identification was exhaustively attempted but not achieved despite the existence of an osteo implanted device in one case and the presence of documents in the other.In one case,we could not find a potential identity for the deceased,while in the other we found a possible identity but not a family member to provide antemortem data to confirm it.Although the scientific literature tends to favour the publication of cases with favourable outcomes,one should also learn from failures,which is the reason why the author decided to publish his unsuccessful experiences.The reasons for the failures are discussed,as well as methodological improvements for future cases.