An Indirect Estimation of Machine Parameters for Serial Production Lines with Bernoulli Reliability Model

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 analysis and 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 efficiencies of those machines while avoiding a direct manual measurement (by human) of up- and down times for them. It turns out that those efficiencies can be computed using starvation/blockage data of the neighboring machines along with the system information. With this, a continuous update of the model is possible even though some machines do not report status in automated manner. The method is indirect as opposed to a direct manual measurement by human. The results are derived for serial production lines with Bernoulli reliability characteristics. Simulation studies are carried out to verify the accuracy of proposed estimation method in both two-machine line case and multi-machine line case.