Grey Relation between Nonlinear Characteristic and Dynamic Uncertainty of Rolling Bearing Friction Torque

The rolling bearing friction torque which is characterized by its uncertainty and nonlinearity affects heavily the dynamic performance of a system such as missiles, spacecrafts and radars, etc. It is difficult to use the classical statistical theory to evaluate the dynamic evaluation of the rolling bearing friction torque for the lack of prior information about both probability distribution and trends. For this reason, based on the information poor system theory and combined with the correlation dimension in chaos theory, the concepts about the mean of the dynamic fluctuant range （MDFR） and the grey relation are proposed to resolve the problem about evaluating the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque. Friction torque experiments are done for three types of the rolling bearings marked with HKTA, HKTB and HKTC separately; meantime, the correlation dimension and MDFR are calculated to describe the nonlinear characteristic and the dynamic uncertainty of the friction torque, respectively. And the experiments reveal that there is a certain grey relation between the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque, viz. MDFR will become the nonlinear increasing trend with the correlation dimension increasing. Under the condition of fewer characteristic data and the lack of prior information about both probability distribution and trends, the unitive evaluation for the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque is realized with the grey confidence level of 87.7%-96.3%.

Modeling of Testability Requirement Based on Generalized Stochastic Petri Nets

Testability design is an effective way to realize the fault detection and isolation.Its important step is to determine testability figures of merits(TFOM).Firstly,some influence factors for TFOMs are analyzed,such as the processes of system operation,maintenance and support,fault detection and isolation and so on.Secondly,a testability requirement analysis model is built based on generalized stochastic Petri net(GSPN).Then,the system's reachable states are analyzed based on the model,a Markov chain isomorphic with Petri net is constructed,a state transition matrix is created and the system's steady state probability is obtained.The relationship between the steady state availability and testability parameters can be revealed and reasoned.Finally,an example shows that the proposed method can determine TFOM,such as fault detection rate and fault isolation rate,effectively and reasonably.

Test Equipment Selection and Deployment of Materiel System Based on Grey Situation Decision

The situation is constructed when the tests are considered as events and the spare test equipments are considered as strategies. The model of grey situation decision for the test equipment selection and deployment （TESD） is founded. Through analyzing each decision objectives, their relative weights are calculated via analytic hierarchy process （AHP）. The grey situation effect matrix under each objective is computed so that the comprehensive effect measure matrix can be obtained when all of the objectives are traded off by their weights. Finally, the decision-maker can select the optimal situations by the value of the measures and such situations form the overall concept for TESD. The paper combines the AHP with grey situation decision to select and deploy the test equipments optimally. Our experimental results show that the proposed method is effective and efficient.

Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.