In rotating machineries, misalignment is considered as the second most major cause of failure after unbalance. In this article, model-based multiple fault identification technique is presented to estimate speed-dependent coupling misalignment and bearing dynamic parameters in addition with speed independent residual unbalances. For brevity in analysis, a simple coupled rotor bearing system is considered and analytical approach is used to develop the identification algorithm. Equations of motion in generalized co-ordinates are derived with the help of Lagrange's equation and least squares fitting approach is used to estimate the speed-dependent fault parameters. Present identification algorithm requires independent sets of forced response data which are generated with the help of different sets of trial unbalances. To avoid/suppress the ill-conditioning of regression equation, independent sets of forced response data are obtained by rotating the rotor in clock-wise and counter clock-wise directions, alternatively. Robustness of algorithm is checked for different levels of measurement noise.
The formulas have been entered and approved for the calculation of porosity distribution on the thickness of layer of fine-grained mixture during its separation by the inclined flat or vertical cylinder vibro sieves. It has been attained as a result of approximation of tabular information of the obtained numerical computer integration of the specially worked out nonlinear differential equations of the second order in a dimensionless form. For approximation, the function of degree coefficients and index is used for the degrees which are certain by the Aitken's method. Coefficients of the entered analytical dependence are the vibro sieves related to the parameters obtained by mechanical descriptions of the separated material. Coefficients of the entered analytical dependence are related to the parameters of vibro sieves and mechanical descriptions of the separated material. In the case of cylinder vertical vibro sieve the action of centrifugal force is also taken into account. The method of mixture porosity calculation does not need a computer numerical integration of nonlinear differential equations conducted by other authors for solving this problem. Comparison of numerical results of the proposed analytical method of calculation with the ones described in literature, have confirmed its high accuracy results, for the differences do not exceed one percent. The expounded method is universal enough and simple in use, besides it opens the possibilities of subsequent analytical integration of differential equalizations of motion at the calculation of kinematics descriptions of grain flow. The developed method gives the opportunity to also solve the inverse task when, according to experimental measurements of porosity values of grain mixtures on the thickness of movable separated layer, it is needed to find the value of phenomenological permanent that is included in the expressions of coefficients of initial differential equalization. In this way, the adequacy of the mathematical model is improved. The use of approximation of degree considerably simplifies the method of authentication of differential equalization coefficients. In the article, the examples of grain mixture porosity calculation as well as the examples of phenomenological permanent authentication have been resulted after experimental calculations for both the variants of vibro sieves.
The main focus of this paper is to propose a method for prioritizing knowledge and technology factor of firms towards sustainable competitive advantage. The data has been gathered and analyzed from two high tech start-ups in which technology and knowledge play major role in company’s success. The analytical hierarchy model (AHP) is used to determine competitive priorities of the firms. Then knowledge and technology part of sense and respond questionnaire is used to calculate the variability coefficient i.e. the uncertainty caused by technology and knowledge factor. The proposed model is tested in terms of two start-ups. Based on the initial calculation of uncertainties, some improvement plan is proposed and the method is applied again to see if the uncertainty of knowledge and technology decreases. In both cases, the proposed model helped to have a clear and precise improvement plan and led in reduction of uncertainty.