Tool health monitoring and maintenance scheduling are crucial to empower smart manufacturing. Focusing on realistic needs, this study aims to develop a data-driven framework that integrates partial least squares and exponentially weighted moving-average for feature selection and model construction to monitor and predict tool health via analyzing status data collected from the sensors and thus derive the optimal maintenance strategy for smart production. Indeed, the proposed approach can deal with multi-collinearity of equipment and process data efficiently. An empirical study is conducted for validation in a leading thin film transistor liquid crystal displays manufacturing company. The results have shown practical viability of the proposed approach to provide an early detection of abnormal tool status, prolong the maintenance cycles for enhancing capacity utilization and productivity, and thus reduce the cost. Indeed, the developed solution is implemented in real settings as partial effort for enabling Industry 3.5.

中文翻译：

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智能制造工具健康监测和维护策略的数据驱动框架


工具健康监测和维护计划对于赋能智能制造至关重要。本研究着眼于现实需求，旨在开发一个数据驱动的框架，集成偏最小二乘法和指数加权移动平均法进行特征选择和模型构建，通过分析从传感器收集的状态数据来监测和预测工具的健康状况，从而得出工具的健康状况。智能生产的最优维护策略。事实上，所提出的方法可以有效地处理设备的多重共线性并处理数据。在一家领先的薄膜晶体管液晶显示器制造公司进行了实证研究以进行验证。结果表明，该方法具有实际可行性，可以及早检测异常工具状态，延长维护周期，从而提高产能利用率和生产率，从而降低成本。事实上，开发的解决方案已在实际环境中实施，作为实现工业 3.5 的部分努力。