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 toward 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 Internet of Things devices that measure these machines stop functioning. In this context, this article addresses the problem of estimating the reliability parameters of those machines while avoiding a direct manual measurement (by humans) of uptime and downtime. 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 the proposed estimation methods.

中文翻译：

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智能生产系统机器参数的数据驱动间接估计

生产系统的机器可靠性参数的自动测量能够持续更新系统的数学模型，可用于各种分析以提高生产力。但是，某些机器的自动参数测量出现故障可能会阻碍持续更新。例如，当线路中的某些机器无法保存日志文件或测量这些机器停止运行的物联网设备时，已经观察到了这种情况。在这种情况下，本文解决了估计这些机器的可靠性参数的问题，同时避免了直接手动测量（人工）正常运行时间和停机时间。事实证明，这些参数可以使用相邻机器的缓冲区相关数据以及系统信息来计算。这样，即使某些机器停止以自动方式记录其状态，也可以持续更新模型。与直接手动测量相反，该方法是间接的。结果是针对具有伯努利和指数可靠性特性的同步串行生产线得出的。我们的模拟研究验证了所提出的估计方法的准确性。结果是针对具有伯努利和指数可靠性特性的同步串行生产线得出的。我们的模拟研究验证了所提出的估计方法的准确性。结果是针对具有伯努利和指数可靠性特性的同步串行生产线得出的。我们的模拟研究验证了所提出的估计方法的准确性。