In recent years, the requirement for real-time understanding of machine behavior has become an important objective in industrial sectors to reduce the cost of unscheduled downtime and to maximize production with expected quality. The vast majority of high-end machines are equipped with a number of sensors that can record event logs over time. In this paper, we consider an injection molding (IM) machine that manufactures plastic bottles for soft drink. We have analyzed the machine log data with a sequence of three type of events, ``running with alert'', ``running without alert'', and ``failure''. Failure event leads to downtime of the machine and necessitates maintenance. The sensors are capable of capturing the corresponding operational conditions of the machine as well as the defined states of events. This paper presents a new model to predict a) time to failure of the IM machine and b) identification of important sensors in the system that may explain the events which in-turn leads to failure. The proposed method is more efficient than the popular competitor and can help reduce the downtime costs by controlling operational parameters in advance to prevent failures from occurring too soon.

翻译：近年来，对机器行为进行实时理解的需求已成为工业领域的重要目标，旨在降低非计划停机成本，并在保证预期质量的前提下最大化生产。绝大多数高端机器配备有多个传感器，能够随时间记录事件日志。本文以生产软饮料塑料瓶的注塑成型（IM）机器为研究对象。我们分析了包含三类事件序列的机器日志数据，即“带警报运行”、“无警报运行”和“故障”。故障事件会导致机器停机并需要进行维护。传感器能够捕获机器相应的运行状态以及定义的事件状态。本文提出了一种新模型，用于预测：a) IM机器的故障时间；b) 系统中可能解释事件（进而导致故障）的重要传感器。所提出的方法比当前主流竞争方法更高效，可通过提前控制运行参数来防止故障过早发生，从而帮助降低停机成本。