The introduction of data-related information technologies in manufacturing allows to capture large volumes of data from the sensors monitoring the production processes and different alarms associated to them. An early prediction of those alarms can bring several benefits to manufacturing companies such as predictive maintenance of the equipment, or production optimization. This paper introduces a new system that allows to anticipate the activation of several alarms and thus, warns the operators in the plants about situations that could hamper the machines operation or stop the production process. The system follows a two-stage forecaster–analyzer approach on which first, a long short-term memory recurrent neural network based forecaster predicts the future sensor’s measurements and then, distinct analyzers based on residual neural networks determine whether the predicted measurements will trigger an alarm or not. The system supports some features that make it particularly suitable for smart manufacturing scenarios: on the one hand, the forecaster is able to predict the future measurements of different types of time-series data captured by various sensors in non-stationary environments with dynamically changing processes. On the other hand, the analyzers are able to detect alarms that can be modeled with simple rules based on the activation condition, and also more complex alarms on which it is unknown when the activation condition will be fulfilled. Moreover, the followed approach for building the system makes it flexible and extensible for other predictive analysis tasks. The system has shown a great performance to predict three different types of alarms.

在制造业中引入与数据相关的信息技术可以从监视生产过程的传感器和与之相关的不同警报中捕获大量数据。对这些警报的早期预测可以为制造公司带来一些好处，例如设备的预测性维护或生产优化。本文介绍了一种新系统，该系统可以预期多个警报的激活，从而警告工厂中的操作员可能会妨碍机器运行或停止生产过程的情况。该系统遵循两阶段的预测器-分析器方法，首先是基于长期短期记忆递归神经网络的预测器预测未来传感器的测量值，然后基于残差神经网络的不同分析器确定预测的测量值是否会触发警报。该系统支持一些使其特别适用于智能制造方案的功能：一方面，预报器能够预测非平稳环境中动态变化过程的各种传感器捕获的不同类型时间序列数据的未来测量结果。另一方面，分析器能够检测警报，这些警报可以根据激活条件使用简单的规则进行建模，还可以检测更复杂的警报，这些警报尚不清楚何时满足激活条件。此外，构建系统所遵循的方法使其可以灵活，可扩展地用于其他预测分析任务。该系统在预测三种不同类型的警报方面表现出了出色的性能。