Enhancement of the Overall Equipment Effectiveness of Factories – Development of Resilient Agent-Based Automation Systems for Machine and Plant Manufacturing Industry
Global markets are extremely competitive, making an effective operation of production systems, oftentimes measured using the Overall Equipment Effectiveness (OEE), crucial for profitability. Resi4MPM will develop a decentralized agent-based method to improve the resilience of production systems, thus increasing a factory’s OEE. This will be achieved by combining intelligent field-level devices with cloud-based data analysis methods from our Taiwanese research partners at NCKU, whose focus is the Intelligent Predictive Maintenance system. Resi4MPM will research three intertwined approaches to actively improve OEE. First, it will advance the action space method, which restricts the autonomous behavior of a machine or a component to ensure deterministic decision-making. This method enables the automatic replacement of field-level devices, e.g., sensors via soft sensors, based on the device's health status and the assessed soft device's quality. Uncertainties will be considered and accumulated to represent the safe action space of superordinate components precisely. We will especially address the key research issues of appropriately representing information for the field-level and efficient decision-making as a prerequisite for recovery. Secondly, on the machine level, Resi4MPM aims to support (semi-)automatic restart to minimize factory downtime. For this, methods to create state machines including conditions of transitions from available engineering data will be investigated and a retracing method will be researched that leverages these state machines. In case a machine stops due to a fault that does not allow recovery, this method allows the machine to identify how it can reach a safe state, from which normal operation can be resumed. Thirdly, a novel anomaly analysis method will be developed, which combines the benefits of deployment in the cloud (available computational power) with those of deployment at the field-level (high-frequency data from drives, pre-processing, pre-filtering). Algorithms will be investigated to identify anomalies and filter false alarms directly in the field for high calculation efficiency, also providing the ability to react quickly to initiate recovery (1st approach). Also, root-causes of alarm floods are determined in the cloud environment, updated, and sent to the field-level as resourceefficient models to enhance and evolve the plant and its intelligence. Since all these approaches are dependent on appropriate knowledge representations, Resi4MPM will research information extraction from engineering documents and machine learning methods for learning from operation data to generate and update the knowledge bases. This way, the methods for increasing resilience are enabled for industrial applicability reducing manual efforts. Consequently, the use cases to be addressed will be refined with the associated industry partners from machine and plant manufacturing, who will actively join Resi4MPM.
- National Cheng Kung University, Institute of Manufacturing Information and Systems
- Technical University of Munich, Institute of Automation and Information Systems