Periodic Reporting for period 3 - Z-BRE4K (Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories)
Berichtszeitraum: 2020-04-01 bis 2021-03-31
Z-BRE4K introduced a novel design for a predictive maintenance (PdM) capable of making accurate predictions for the future states of the components/machines/systems by the employment of intelligent simulators forecasting the generation of failures, estimating the remaining useful life RUL and triggering respective remedy actions.
During the project lifetime, Z-BRE4K solution passed through three stages of readiness levels (TRL5 to TRL7) at three end users’ industrial environments. In general, lessons learned can be summarized as:
Live data are gathered by sensors and other systems;
Data from individual systems are incorporated in a distributed system;
Quality and maintenance measurements are available;
Manual maintenance schedules are replaced with PdM procedures and schedules;
Maintenance experts are supported by gathered data and predictions to improve their know-how on the maintenance domain;
PdM accuracy and performance are established;
Productivity is improved;
Specifically, for SACMI-CDS, Z-BRE4K system gathered sensor data by adding additional sensors and condition monitoring solution where the system is distributed in the PdM that determines the RUL and machine failure. Main impact has been obtained on plant productivity and component’s management stating that it is of high importance to collaborate, not only with mechanical engineering and maintenance related professionals, but also with different technical background experts that together can improve multi-tasking, combining shopfloor and office-related activities.
The solution implemented in GESTAMP is an integrated system that exploits information on the shopfloor while connects MES and quality control system and sends data to the PdM to create predictions. The solution positively impacted at reducing breakdowns, machine downtimes, optimizing the working conditions on the shopfloor based on PdM that supported better understanding of GESTAMP´s reflection and readiness to apply PdM solution to its plants while new mitigation actions related to process flaws and defects identification were developed.
Finally, the solution on PHILIPS combines all separate data and gathers to the PdM of the RUL where the PdM outcome is sent to the decision support which creates a suggestion for the production managers or the tool workshop operators. It was interesting to evidence the impact where PdM helps to improve the uptime of their tools in the live production while reducing tooling costs, man-hours and unnecessary tooling parts stock. Lessons learned: “listening” and understanding the machines is the key for success as well as close contact between technology providers and experts where data integration/architecture and machine learning are both very important projects.
During the project lifetime, Z-BRE4K solution passed through three stages of readiness levels (TRL5 to TRL7) at three end users’ industrial environments. In general, lessons learned can be summarized as:
Live data are gathered by sensors and other systems;
Data from individual systems are incorporated in a distributed system;
Quality and maintenance measurements are available;
Manual maintenance schedules are replaced with PdM procedures and schedules;
Maintenance experts are supported by gathered data and predictions to improve their know-how on the maintenance domain;
PdM accuracy and performance are established;
Productivity is improved;
Specifically, for SACMI-CDS, Z-BRE4K system gathered sensor data by adding additional sensors and condition monitoring solution where the system is distributed in the PdM that determines the RUL and machine failure. Main impact has been obtained on plant productivity and component’s management stating that it is of high importance to collaborate, not only with mechanical engineering and maintenance related professionals, but also with different technical background experts that together can improve multi-tasking, combining shopfloor and office-related activities.
The solution implemented in GESTAMP is an integrated system that exploits information on the shopfloor while connects MES and quality control system and sends data to the PdM to create predictions. The solution positively impacted at reducing breakdowns, machine downtimes, optimizing the working conditions on the shopfloor based on PdM that supported better understanding of GESTAMP´s reflection and readiness to apply PdM solution to its plants while new mitigation actions related to process flaws and defects identification were developed.
Finally, the solution on PHILIPS combines all separate data and gathers to the PdM of the RUL where the PdM outcome is sent to the decision support which creates a suggestion for the production managers or the tool workshop operators. It was interesting to evidence the impact where PdM helps to improve the uptime of their tools in the live production while reducing tooling costs, man-hours and unnecessary tooling parts stock. Lessons learned: “listening” and understanding the machines is the key for success as well as close contact between technology providers and experts where data integration/architecture and machine learning are both very important projects.
WP1:
User requirements, end user scenarios and SoA prepared, Z-BRE4K architecture fully developed and adapted to every use case.
Critical risks for the implementation of the Z-BRE4K strategies and associated mitigation actions were identified and monitored periodically.
WP2:
An operating system based on a combination of open-source frameworks and reference architectures has been described.
IDS connectors have been also developed and deployed.
High-fidelity machine simulators have been developed by modelling the failure modes.
A Smart Object Network has been developed to allow data acquisition in real time from different sensors/devices/machine and data aggregation.
Development of 2 CECM components for the GESTAMP use case.
WP3:
A semantic model and its corresponding hierarchy as a common reference model for annotation and description of knowledge has been designed.
FMECA component has been developed and tested.
An event-based machine learning mechanism has been developed.
WP4:
Analysis of existing PdM strategies and the SoA on the industrial maintenance strategies and policies.
Retrofitting physical based models of assets have been developed as well to assist the data-based predictive modelling.
A higher-level decision support system (DSS) has been developed, implementing Key Risk Indicators (KRI) and a reasoning engine based on gathered requirements and user experience.
Adequate middleware between the MES and the PdM tools has been identified.
WP5:
Z-BRE4K components have been integrated following an Incremental Integration Strategy. Several integration errors have been identified and solved, preparing the Z-BRE4K system for technology validation process at TRL5.
TRL 6 and an initial demonstration phase within the three Z-BRE4K end users were performed. All preparatory activities at the end users´ shopfloors have been completed successfully, preparing the system for further steps.
WP6:
Methodology framework for the evaluation of Z-BRE4K solution within industrial relevant environments was established.
All three end users demonstrated the validated Z-BRE4K system at operational environments.
Improvements were preformed and Z-BRE4K demonstrated prototype system at TRL7 has been successfully completed.
Various data and information from pilot demonstrations have been collected for exploitation purposes and evaluation of the solutions has been completed.
WP7:
Formulation of standards and development of training methodology.
Development of roadmap for market uptake and market analysis.
Development of Z-BRE4K business model.
Development of Z-BRE4K pricing strategy.
Proper Risk management methodology was established for both business and implementation risks and what if scenarios – Plan B prepared when necessary.
WP8:
Plan for the Exploitation and Dissemination of Results has been defined.
Creation of Project Website and Material for dissemination of the project.
Dissemination activities have been also carried out by several partners in different events and congresses worldwide.
Cost Benefit Analysis estimating the cost savings enabled by Z-BRE4K.
Two main approaches to exploit project results were considered. (1) Each partner will individually exploit the results that they developed during the project. (2) A Joint Venture (JV) is considered to be established, to lead the commercial exploitation of the integrated Z-BRE4K results.
Establishment of future funding plan to advance Z-BRE4K to TRL9.
In summary, Z-BRE4K solution has been validated at operational environment (TRL6 and TRL7) and components are being used by the end users in a day-to-day basis while the project ended with no critical implementation risks that could have endangered the project outcome. Finally, the final common paper about the Z-BRE4K architecture summarizes all the work carried out in the project and it will be presented at 26th ICPR virtual conference on July 2021.
User requirements, end user scenarios and SoA prepared, Z-BRE4K architecture fully developed and adapted to every use case.
Critical risks for the implementation of the Z-BRE4K strategies and associated mitigation actions were identified and monitored periodically.
WP2:
An operating system based on a combination of open-source frameworks and reference architectures has been described.
IDS connectors have been also developed and deployed.
High-fidelity machine simulators have been developed by modelling the failure modes.
A Smart Object Network has been developed to allow data acquisition in real time from different sensors/devices/machine and data aggregation.
Development of 2 CECM components for the GESTAMP use case.
WP3:
A semantic model and its corresponding hierarchy as a common reference model for annotation and description of knowledge has been designed.
FMECA component has been developed and tested.
An event-based machine learning mechanism has been developed.
WP4:
Analysis of existing PdM strategies and the SoA on the industrial maintenance strategies and policies.
Retrofitting physical based models of assets have been developed as well to assist the data-based predictive modelling.
A higher-level decision support system (DSS) has been developed, implementing Key Risk Indicators (KRI) and a reasoning engine based on gathered requirements and user experience.
Adequate middleware between the MES and the PdM tools has been identified.
WP5:
Z-BRE4K components have been integrated following an Incremental Integration Strategy. Several integration errors have been identified and solved, preparing the Z-BRE4K system for technology validation process at TRL5.
TRL 6 and an initial demonstration phase within the three Z-BRE4K end users were performed. All preparatory activities at the end users´ shopfloors have been completed successfully, preparing the system for further steps.
WP6:
Methodology framework for the evaluation of Z-BRE4K solution within industrial relevant environments was established.
All three end users demonstrated the validated Z-BRE4K system at operational environments.
Improvements were preformed and Z-BRE4K demonstrated prototype system at TRL7 has been successfully completed.
Various data and information from pilot demonstrations have been collected for exploitation purposes and evaluation of the solutions has been completed.
WP7:
Formulation of standards and development of training methodology.
Development of roadmap for market uptake and market analysis.
Development of Z-BRE4K business model.
Development of Z-BRE4K pricing strategy.
Proper Risk management methodology was established for both business and implementation risks and what if scenarios – Plan B prepared when necessary.
WP8:
Plan for the Exploitation and Dissemination of Results has been defined.
Creation of Project Website and Material for dissemination of the project.
Dissemination activities have been also carried out by several partners in different events and congresses worldwide.
Cost Benefit Analysis estimating the cost savings enabled by Z-BRE4K.
Two main approaches to exploit project results were considered. (1) Each partner will individually exploit the results that they developed during the project. (2) A Joint Venture (JV) is considered to be established, to lead the commercial exploitation of the integrated Z-BRE4K results.
Establishment of future funding plan to advance Z-BRE4K to TRL9.
In summary, Z-BRE4K solution has been validated at operational environment (TRL6 and TRL7) and components are being used by the end users in a day-to-day basis while the project ended with no critical implementation risks that could have endangered the project outcome. Finally, the final common paper about the Z-BRE4K architecture summarizes all the work carried out in the project and it will be presented at 26th ICPR virtual conference on July 2021.
A benchmarking between Z-BRE4K solution against other similar solutions has been made. SWOT analysis revealed that the strongest point of Z-BRE4K is that it is a holistic solution, and its main added value, comparing with existing solutions for PdM, is the combination of data-driven and physical-based. It was also revealed the key role of digital innovation and operation managers in the upcoming future, to foster the adoption of the concept of Industry 4.0 in companies. In this sense, the collaboration between data scientists and domain experts will be critical for the future implementation of solutions like Z-BRE4K. Z-BRE4K impact has been also measured on every use case, highlighting the increasing overall equipment effectiveness.