A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach

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-stageforecaster-analyzerapproach on which first, a long short-term memory recurrent neural network basedforecasterpredicts the future sensor's measurements and then, distinctanalyzersbased 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, theforecasteris 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.

Automated summary

The introduction of data-related information technologies in manufacturing enables capturing large volumes of data from sensors and alarms. A new system is introduced to predict the activation of multiple alarms and alert operators of potential disruptions to machine operation or production.