A Data-Driven Indirect Estimation of Machine Parameters for Smart Production Systems

Abstract

Automated measurement of the machine reliability parameters for a production system enables a continuous update of the mathematical model of the system, which can be used for various analyses towards productivity improvement. However, the continuous update may be impeded by some machines of which automated parameter measurements are out of order. Such a situation has been observed, for instance, when some of the machines in the line cannot save log files, or IoT devices that measure these machines stop functioning. In this context, this paper addresses the problem of estimating the reliability parameters of those machines while avoiding a direct manual measurement (by humans) of up- and down times. It turns out that those parameters can be computed using buffer-related data of the neighboring machines along with the system information. With this, a continuous update of the model is possible even though some machines stop recording their status in an automated manner. The method is indirect as opposed to direct manual measurement. The results are derived for synchronous serial production lines with Bernoulli and also exponential reliability characteristics. Our simulation studies verify the accuracy of proposed estimation methods. IEEE