The use case deals with the complex equipment on the shop floor where the industry will act upon a sudden breakdown. The aim of the Z-BRE4K solution of this use case is to implement cognitive maintenance strategies for full equipment and process production availability while addressing the quality control of instruments on the shop floor and measure it with KPIs.

The solution exploits the available information by the integrated systems on the shop floor and connects with the shop floor’s MES and quality control system for data retrieval that are being sent to the Z-BRE4K Predictive Maintenance Component where the component implements its algorithms to create predictions. Also, data is simulated in the Z-BRE4K solution, creating a digital twin for the system which will be used to predict and reduce failure rates and downtimes. Finally, the Z-BRE4K solution is implemented to optimise the working conditions on the shop floor based on PdM.

Predictions on the shop floor will reduce breakdowns and downtimes of the machines while readapting the manufacturing procedures on the shop floor to accommodate the most suitable maintenance based on the Z-BRE4K system.

Shopfloor machinery requires periodic preventive maintenance to operate under the predefined industrial protocols and produce the parts within the expected limits. On the other hand, the continuous operation of the machines leads to a degradation of their internal tools. The main goal of the Z-BRE4K solution is to apply algorithms that will be able to predict the remaining useful lifetime of a certain tool between the intervals of scheduled maintenance. The other main motivation of the implementation of the Z-BRE4K solution is to simulate the processes over the complete production chain and monitor, based on sensorial data, the interaction between the tools and the machines in the assembly line and the product family.

Z-BRE4K combines all separate data and gathers it to the prediction of the remaining useful lifetime component where the component implements algorithms to compute the expected tool life and compare with the results given by the operators, based on their experience. The computed expected tool life is more accurate than the previous one and the prediction outcome is sent to the Decision Support System component which creates a suggestion for the production managers or the tool workshop operators. The suggestions are based on predefined rules by production managers and can create a new
Maintenance Plan, which will include new maintenance orders for the tool, based on the prediction of the expected tool life.

PdM helps this end-user to improve the uptime of their tools in the live production while reducing tooling costs, man-hours and unnecessary tooling parts stock.
 

New machinery is equipped with a vast range of sensors, thus making the latter generation of production equipment and advanced CPSs. However, given the high CAPEX of production equipment, the majority of the already deployed machinery in existing shop floors is some years old. This other type of machinery is equipped with a limited number of sensors associated with fixed alarms. In this regard, these systems lack a pervasive condition monitoring system that can detect deterioration trends leading to failures, which some of them cannot be detected with existing alarm configuration and the operation strategy executed by the final user. Hence, an end-to-end PdM suite can provide final users with a solution for improving efficiency and equipment effectiveness.

Sensor data is gathered by additional sensors and a condition monitoring solution in the system and it is distributed in the Predictive Components to create outputs based on dedicated algorithms able to determine the remaining useful life and machine failure.

Z-BRE4K solution will have a positive impact on plant productivity and also component’s management, including inventory costs for spare components.
 

This white paper presents a review of the lessons learned from the point of view of six EU funded H2020 research projects (PRECOM, PROPHESY, PROGRAMS, SERENA, UPTIME and Z-BREAK), funded under the topic “FOF-09-2017 - Novel design and PdM technologies for increased operating life of production systems”. These projects were active from 2017 to 2021 and together constituted the ForeSee cluster.

Research and technology partners together with industrial end-users worked collaboratively to develop and deploy solutions that advance maintenance practice in industry towards more efficient, sustainable, human-centric and resilient factories. This white paper aims to share knowledge, vision and lessons learnt by ForeSee cluster partners on the topic of PdM, as well as to provide recommendations for advancing PdM in industrial practice. The core target groups of this report are industry practitioners, people in academia and policy makers at the local, national and EU levels.