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Manufacturing systems are increasingly becoming automated and complex in nature. Highly reliable and flexible manufacturing systems (FMSs) are the necessity of manufacturing industries to fulfill the increasing customized demands. Worldwide, FMSs are used in industries to attain high productivity in production environments with rapidly and continuously changing manufactured goods structures and demands. Reliability prediction plays a very significant role in system design in the manufacturing industry, and two crucial issues in the prediction of system reliability are failures of equipment and system configuration. This novel work presents a stochastic model to analyze the performance of an FMS through its reliability characteristics, in the concern of its equipment. To improve the reliability of FMS, determine the sensitivity of the reliability measures of FMS. FMS consists of many components such as machine tools like CNC, automatic handling and material storage, controller and robot for serving load. The designed system is studied by using the Markov process, supplementary variable technique, Laplace transformation, coverage factor and Gumbel–Hougaard family copula to obtain various reliability measures. For some realistic approach, particular cases and graphical illustrations are also obtained.

We propose a systematic approach for design and validation of error detection software. Formally, the semantic of a specification is represented by a transition system. This representation is then used to generate a flowgraph or ddgraph which is used to construct an execution path tree. The information obtained from this algorithm representation is used to aid in the design of software-based fault detection techniques for hardware faults.

Flowgraph and ddgraph representations provide information to predict future program flow. During execution, the current program path is recorded, along with the expected path. Checks are placed to verify that the program path follows the predicted path.

Algorithm-based fault tolerance (ABFT) techniques are used to detect data structure corrupting faults and to improve the fault coverage. Fault coverage provided by this approach for different types of hardware faults has been estimated through experiments with the software-based fault injection tool (SOFIT) and the data is presented to demonstrate the effectiveness of the method.

The application of the method for choosing a distribution law in relation to a family of two-sided power distributions for further use in metrological applications is considered. An efficiency of the method is assessed by statistical modeling based on the Monte Carlo method. Comparison is made with the maximum likelihood method when estimating a single parameter of distribution law (power). For the considered family of distributions, the coverage factor and coverage interval are estimated.

Using the numerical methods, coverage factors were calculated for the composition of two Student distribution laws with different numbers of degrees of freedom and the ratio of standard deviations. The number of degrees of freedom corresponding to this composition was calculated. An approximating expression for the coverage coefficient of this composition is given. The results obtained by numerical methods are compared with the results obtained using the GUM method.