Periodic Reporting for period 2 - IMPROVE (Innovative Modeling Approaches for Production Systems to raise validatable efficiency)
Reporting period: 2017-03-01 to 2018-08-31
Within the manufacturing industry, the complexity of production plants is steadily rising due to increasing product variances, product complexity, and pressures for production efficiency. Production systems must therefore now evolve rapidly and operate optimally, creating challenges for larger industries and serious problems for SMEs without the needed expertise or sufficient resources to adapt new technical possibilities.
To meet these challenges, the European research and innovation project IMPROVE has developed novel data-based solutions to enhance machine reliability and efficiency. Innovative tools in the fields of simulation & optimization, condition monitoring, alarm management, and quality prediction provide manufacturers with a human machine interface (HMI) and decision support system (DSS) to ensure best possible user support.
By adopting new innovative technologies to manufacturing enterprises, they are able to create more jobs. By now, already 30 million people are employed by manufacturing companies. Additionally, the innovative technologies integrated in the whole life cycle of factories will provide opportunities for novel business strategies. The IMPROVE results have a number of positive impacts regarding workplace attractiveness. First and foremost, the amount of monotonous tasks throughout the whole factory lifecycle is greatly reduced. This is enabled by reduced implementation efforts, as a result of the modelling and model learning approach, and novel HMI concepts, which support decision making and process adaption, as well as a reduction of recurring manufacturing tasks as a result of predictive maintenance. Furthermore, services and novel interaction methods to support the handling of increasingly complex production control require great know-how especially in the design process, and therefore provide ample incentive for engineering graduates and doctorate holders to design and work on cutting edge manufacturing systems.
The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Industrial prototypes have been developed and tested for all of the aforementioned technologies.
To meet these challenges, the European research and innovation project IMPROVE has developed novel data-based solutions to enhance machine reliability and efficiency. Innovative tools in the fields of simulation & optimization, condition monitoring, alarm management, and quality prediction provide manufacturers with a human machine interface (HMI) and decision support system (DSS) to ensure best possible user support.
By adopting new innovative technologies to manufacturing enterprises, they are able to create more jobs. By now, already 30 million people are employed by manufacturing companies. Additionally, the innovative technologies integrated in the whole life cycle of factories will provide opportunities for novel business strategies. The IMPROVE results have a number of positive impacts regarding workplace attractiveness. First and foremost, the amount of monotonous tasks throughout the whole factory lifecycle is greatly reduced. This is enabled by reduced implementation efforts, as a result of the modelling and model learning approach, and novel HMI concepts, which support decision making and process adaption, as well as a reduction of recurring manufacturing tasks as a result of predictive maintenance. Furthermore, services and novel interaction methods to support the handling of increasingly complex production control require great know-how especially in the design process, and therefore provide ample incentive for engineering graduates and doctorate holders to design and work on cutting edge manufacturing systems.
The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Industrial prototypes have been developed and tested for all of the aforementioned technologies.
The multidisciplinary experts involved in IMPROVE have developed innovative solutions to enhance manufacturing production and find answers to some of the most pressing industrial challenges of our time. In this regard, key achievements have been made in the fields of simulation & optimization, condition monitoring, alarm management, and quality prediction. Combining solutions in these fields into one holistic approach, a decision support system (DSS) assists the operator in taking the right choices in the manufacturing process.
Intelligent optimization algorithms help determine optimal plant parameters by simulating and evaluating different parameter configurations before the configuration is tried in the real plant. At present, despite big potential and disruptive possible impact on the machine manufacturer business, simulation is little used in real industrial application. The main reasons for this are: Little knowledge in industry about the modelling and simulation theory and best practices; Question of accuracy of the simulation results (availability of validated models); Resources to be committed to the modelling and simulation process. Based on a real industrial case, IMPROVE demonstrates that modelling and simulation, combined with optimization, is a resource-effective way to improve the overall machine design. Optimized production creates less waste, leads to a higher productivity and, consequently, greater profits.
Condition monitoring of the manufacturing system uses simulations of learned normal behaviour models to forecast maintenance requirements. Live data from the system is compared to the predictions of the model, allowing anomaly detection, condition monitoring, and predictive maintenance. Actions can be classified in two main groups: 1. “preventive change”, meaning that some components are substituted on the basis of a predefined timetable; 2. “change when the machine has failed”, meaning that components are replaced only after a damage or a malfunction has appeared. In the first case, machine users spend potentially more money than needed to replace parts still in good working condition, while in the second scenario, they lose money since the replacement of damaged parts stops the normal production. Human operators often struggle to diagnose faults or anomalous behaviour in the system, leading to system breakdown, unexpected downtime or degradation in product quality. A dynamic detection of a system’s real condition or degradation can support experts in better planning maintenance times and avoid the aforementioned negative effects caused by system degradation. IMPROVE’s condition monitoring software provides an all-round solution that could lead to a great change in the service procedures of automatic machines and will significantly improve the production process.
Alarm flooding is a persistent problem in industrial plant operation in which the operator may lose the overview on how to solve the situation. This can lead to critical alarms being overlooked, time-consuming search for the “root cause” of the problem resulting in significant downtime and irreversible damage. Basic statistics from alarm logs show that an alarm flood condition makes up nearly 10% of plant operating time. Solving this problem is thus very important to ensure an efficient and secure production. IMPROVE’s solution is the development of an innovative algorithm, based on data-driven similarity learning and case-based-reasoning (CBR) that integrates expert knowledge.
IMPROVE’s central mission is to support the operator in taking the right decisions. By combining our tools into one approach, we develop a holistic decision support system (DSS) and a quality prediction tool. The DSS is based on the actual machine behaviour and visualises the results to assist the operator in charge.
Intelligent optimization algorithms help determine optimal plant parameters by simulating and evaluating different parameter configurations before the configuration is tried in the real plant. At present, despite big potential and disruptive possible impact on the machine manufacturer business, simulation is little used in real industrial application. The main reasons for this are: Little knowledge in industry about the modelling and simulation theory and best practices; Question of accuracy of the simulation results (availability of validated models); Resources to be committed to the modelling and simulation process. Based on a real industrial case, IMPROVE demonstrates that modelling and simulation, combined with optimization, is a resource-effective way to improve the overall machine design. Optimized production creates less waste, leads to a higher productivity and, consequently, greater profits.
Condition monitoring of the manufacturing system uses simulations of learned normal behaviour models to forecast maintenance requirements. Live data from the system is compared to the predictions of the model, allowing anomaly detection, condition monitoring, and predictive maintenance. Actions can be classified in two main groups: 1. “preventive change”, meaning that some components are substituted on the basis of a predefined timetable; 2. “change when the machine has failed”, meaning that components are replaced only after a damage or a malfunction has appeared. In the first case, machine users spend potentially more money than needed to replace parts still in good working condition, while in the second scenario, they lose money since the replacement of damaged parts stops the normal production. Human operators often struggle to diagnose faults or anomalous behaviour in the system, leading to system breakdown, unexpected downtime or degradation in product quality. A dynamic detection of a system’s real condition or degradation can support experts in better planning maintenance times and avoid the aforementioned negative effects caused by system degradation. IMPROVE’s condition monitoring software provides an all-round solution that could lead to a great change in the service procedures of automatic machines and will significantly improve the production process.
Alarm flooding is a persistent problem in industrial plant operation in which the operator may lose the overview on how to solve the situation. This can lead to critical alarms being overlooked, time-consuming search for the “root cause” of the problem resulting in significant downtime and irreversible damage. Basic statistics from alarm logs show that an alarm flood condition makes up nearly 10% of plant operating time. Solving this problem is thus very important to ensure an efficient and secure production. IMPROVE’s solution is the development of an innovative algorithm, based on data-driven similarity learning and case-based-reasoning (CBR) that integrates expert knowledge.
IMPROVE’s central mission is to support the operator in taking the right decisions. By combining our tools into one approach, we develop a holistic decision support system (DSS) and a quality prediction tool. The DSS is based on the actual machine behaviour and visualises the results to assist the operator in charge.
IMPROVE provides one of the first combined simulation-optimization round trip solution ready for industrial application. The tool can be implemented as an upgrade to already existing production plants and allows educating operators with augmented reality.
IMPROVE provides an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation. The outcome solution is translated into different software options, ready for industrial use.
IMPROVE provides an alarm management algorithm to prevent alarm floods or to better handle them in case they occur.
IMPROVE provides a prototypical implementation of a decision support app for quality monitoring. The app includes a visualisation of cause and effect relations to support the operator in maintaining high quality. Furthermore, quality prediction models for selected material compositions are developed.
IMPROVE provides an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation. The outcome solution is translated into different software options, ready for industrial use.
IMPROVE provides an alarm management algorithm to prevent alarm floods or to better handle them in case they occur.
IMPROVE provides a prototypical implementation of a decision support app for quality monitoring. The app includes a visualisation of cause and effect relations to support the operator in maintaining high quality. Furthermore, quality prediction models for selected material compositions are developed.