Academic literature on the topic 'Dynamic Error'

A measuring device for gear dynamic transmission error test is developed based on NI Labview software, and a new type eccentric bushing structure that can simulate a variety of installation errors is presented. The hardware and software design of the gear dynamic transmission error measuring device is given, and the gear dynamic transmission errors for low-speed and high-speed in different loads are gotten based on the device and the measured data. Experimental dynamic transmission error results show that the gear dynamic transmission error measuring device is a stable and friendly interface with easy operation and high accuracy, able to do real-time detection and data acquisition for gearing.

A numerical model is formulated to analyze the tooth contact dynamic load distribution and dynamic transmission error of a pair of spur gears under the influence of bearing elasticity and gearbox assembly errors. In the proposed model, the deformation of the tooth is computed by applying a combination of finite elements and contact mechanics. The elasticity of the bearings is represented with infinitesimal linear spring elements, while the shafts and gears except the teeth that are in engagement are assumed to be rigid bodies. Applying those assumptions, three sets of highly coupled governing equations representing the meshing teeth flexible behavior, gear-bearing assembly translation dynamics and gear rotation dynamics are derived. The resultant model is then used to predict the dynamical behaviors of the geared rotor system, tooth contact dynamic load, and dynamic transmission error. A set of parametric studies is also performed to analyze the gear dynamic response.

Fast tool servo (FTS) is widely utilized to fabricate optical freeform surfaces with nanometric surface roughness and with sub-micrometric form errors. FTSs dynamics character plays the major part in many factors that influence machining error. In this paper, a dynamic model for FTS is built up to describe its dynamic and to analyze the effects on machining error under different work frequencies. It was found that FTS dynamic mainly affect the Y direction machining accuracy of the workpiece surface, with the increase of the working frequency of FTS, the error caused by FTS dynamic also increases rapidly.

The precision of its processes is affected by static error and dynamic error. This paper focuses on modeling about dynamic errors and proposed an algorithm of the dynamic error for Table-tilting 5-axis machine tool, which is using Homogeneous Transformation Matrix to establish the dynamic errors formula, so as to structure a model of its dynamic error. Dynamic errors about rotary and linear axis of a 5-axis machining center with tilting rotary table type are defined. At last, we performed the operation and measurement of Table-tilting 5-axis machine, in order to compare and verify the dynamic errors, and to use as adjusting the Table-tilting 5-axis machine tool, and improve the precision of its machining. The result of a synthesis example verifies the effectiveness of the proposed modeling.

For many years, the focus of research in the area of human error was the prevention of erroneous actions and assessments through training and design. However, errors can never be eliminated completely. Therefore, the goal of more recent efforts is to minimize their negative consequences through support for error management, i.e., the detection, explanation, and recovery from erroneous actions. For the most part, these efforts have examined the first step in this sequence - error detection. In contrast, little is known about how operators explain and recover from errors. This is true especially for dynamic collaborative environments such as aviation. In this paper, we present findings from a survey and an incident report analysis that suggest the need for adapting the current model of error recovery. Specifically, we emphasize the importance of considering constraints imposed by specific domains in order to predict and explain the predominance and success of certain recovery strategies.

Abstract Variational data assimilation systems require the specification of the covariances of background and observation errors. Although the specification of the background-error covariances has been the subject of intense research, current operational data assimilation systems still rely on essentially static and thus flow-independent background-error covariances. At least theoretically, it is possible to use flow-dependent background-error covariances in four-dimensional variational data assimilation (4DVAR) through exploiting the connection between variational data assimilation and estimation theory. This paper reports on investigations concerning the impact of flow-dependent background-error covariances in an idealized 4DVAR system that, based on quasigeostrophic dynamics, assimilates artificial observations. The main emphasis is placed on quantifying the improvement in analysis quality that is achievable in 4DVAR through the use of flow-dependent background-error covariances. Flow dependence is achieved through dynamical error-covariance evolution based on singular vectors in a reduced-rank approach, referred to as reduced-rank Kalman filter (RRKF). The RRKF yields partly dynamic background-error covariances through blending static and dynamic information, where the dynamic information is obtained from error evolution in a subspace of dimension k (defined here through the singular vectors) that may be small compared to the dimension of the model’s phase space n, which is equal to 1449 in the system investigated here. The results show that the use of flow-dependent background-error covariances based on the RRKF leads to improved analyses compared to a system using static background-error statistics. That latter system uses static background-error covariances that are carefully tuned given the model dynamics and the observational information available. It is also shown that the performance of the RRKF approaches the performance of the extended Kalman filter, as k approaches n. Results therefore support the hypothesis that significant analysis improvement is possible through the use of flow-dependent background-error covariances given that a sufficiently large number (here on the order of n/10) of singular vectors is used.