Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 6 Articles
The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain,\nis generally determined by defects from the casting process. These defects may reduce the fatigue\nlife and they are generally distributed randomly in components. The foundries, cutting facilities\nand test facilities can affect the verification of properties by testing. Hence, it is important to have a\ntool to identify which foundry, cutting and/or test facility produces components which, based on\nthe relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest\nreliability to be used for decision-making if additional cost considerations are added. In this paper, a\nstatistical approach is presented based on statistical hypothesis testing and analysis of covariance\n(ANCOVA) which can be applied to compare different groups (manufacturers, suppliers, test facilities,\netc.) and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are\npresented as obtained by statistical analysis of a large set of fatigue data for casted test components\ntypically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied\nstress) for fatigue assessment are estimated based on the statistical analyses and by introduction of\nphysical, model and statistical uncertainties used for the illustration of reliability assessment....
The fatigue crack growth (FCG) phenomenon generally exists in large mechanical structures. Due to the influences of varied kinds\nof randomfactors, the safety evaluation of structure in FCG is under great uncertainty. In this paper, based on the reliability theory,\nthe limit state equations of fracture failure and static strength failure were derived firstly, and the parameters in those equations were\nregarded as randomvariables that follow the normal distribution or log-normal distribution. According to the limit state equations,\nthe JC method (equivalent normalizing method) was used to calculate the reliability indexes under the different failure modes of\nstructure in every stress cycle. Based on the reliability indexes and correlation of the two failure modes, the joint failure probability\nwas obtained. In the end, a specific computation example was given, and the curve of joint failure probability in multiple failure\nmodes was used for comparison with the result of single failure mode.The results indicated that the reliability analysis based on\nmultiple failure modes was more reasonable, and the evaluation of reliability could be obtained in fatigue crack growth process....
The operation of distribution system with the components in deteriorating\ncondition makes the system reliability worsen. It is important to find the solution\nfor balancing failure cost and maintenance benefits such as downtime\nand reliability. In this paper, time to replace the components in optimum\ncondition based on constant-interval replacement mode is investigated. The\noptimal replacement time is mainly depended on component�s reliability and\nthe cost ration of preventive replacement and failure replacement. In this paper,\nequipment inspection method and Weibull Analysis is applied to obtain\nthe accurate reliability estimation. Weibull Analysis is applied with constant-\ninterval replacement model to investigate the optimum replacement\ntime for each component considering the different cost ratios. According to\nthe quantitative results, the determination of the optimal replacement time\n(OPT) can minimize the total downtime and failure cost. Consequently, the\nreliability of the system is maximized and estimation also becomes more accurate\ndue to sufficient approach....
An effective power quality prediction for regional power grid can provide\nvaluable references and contribute to the discovering and solving of power\nquality problems. So a predicting model for power quality steady state index\nbased on chaotic theory and least squares support vector machine (LSSVM) is\nproposed in this paper. At first, the phase space reconstruction of original\npower quality data is performed to form a new data space containing the attractor.\nThe new data space is used as training samples for the LSSVM. Then\nin order to predict power quality steady state index accurately, the particle\nswarm algorithm is adopted to optimize parameters of the LSSVM model.\nAccording to the simulation results based on power quality data measured in\na certain distribution network, the model applies to several indexes with\nhigher forecasting accuracy and strong practicability....
Aiming at the current limit value of six steady-state energy indexes, the current\nradar method is used for reference. A method of comprehensive evaluation\nof power quality based on improved radar method is proposed, which\nimproves the power quality index Type radar pattern to represent the\nsteady-state indicator. Each of the main indicators corresponds to a partial\nring, and the angle of the annular portion is mainly affected by the size of the\nweight. Compared with the previous radar map method to maintain the independence\nof the indicators and a single indicator of the binding data assessment.\nThe method has the advantages of good feasibility....
Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is\ndependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows\nfor the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA)\nand Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature\nset is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency\ndomain features over the bearing�s life cycle data.The kernel principal components which can accurately reflect the performance\ndegradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was\nconducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same\ntype of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and\nstability of the proposed method....
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