IMPROVE Innovative Modeling Approaches for Production Systems to raise validatable efficiency

Summary

The rise of the system complexity, the rapid changing of consumers demand require the European industry to produce more customized products with a better use of resources.

The main objective of IMPROVE is to create a virtual Factory of the Future, which provides services for user support, especially on optimization and monitoring. By monitoring anomalous behaviour will be detected before it leads to a breakdown. Thereby, anomalous behaviour is detected automatically by comparing sensor observation with an automatically generated model, learned out of observations. Learned models will be complemented with expert knowledge because models cannot learn completely. This will ensure and establish a cheap and accurate model creation instead of manual modelling.

Optimization will be performed and results will be verified through simulations. Therefore, the operator has a broad decision basis as well as a suggestion of a DSS (Decision Support System), which will improve the manufacturing system. Operator interaction will be done by a new developed HMI (Human Machine Interface) providing the huge amount of data in a reliable manner. To reach this aim, every step of the research process is covered by a minimum of two experienced consortium partners, who conclude the results of the project using four demonstrators.

The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Main challenges are reducing ramp-up phases, optimizing production plants to increase the cost-efficiency, reducing time to production with condition monitoring techniques and optimise supply chains including holistic data. Consequently, the resource consumption, especially the energy consumption in manufacturing activities, can be reduced. The optimized plants and supply chains enhance the productivity of the manufacturing during different phases of production. Furthermore, the industrial competitiveness and sustainability in EU will be strengthened.

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Result items:

Evaluate the results of the pilot runs of three demonstrators and the numerical benefit

Result items:

Evaluate the results of the pilot runs of three demonstrators and the numerical benefit

The implemented algorithms are validated on lab demonstrators

Transfer the implementation from the lab demonstrators to three prototypes in different industries

More information & hyperlinks

Web resources:	http://improve-vfof.eu/ https://cordis.europa.eu/project/id/678867
Start date:	09-01-2015
End date:	31-08-2018
Total budget - Public funding:	4 148 554,00 Euro - 4 148 554,00 Euro

Cordis data

Original description

The rise of the system complexity, the rapid changing of consumers demand require the European industry to produce more customized products with a better use of resources.
The main objective of IMPROVE is to create a virtual Factory of the Future, which provides services for user support, especially on optimization and monitoring. By monitoring anomalous behaviour will be detected before it leads to a breakdown. Thereby, anomalous behaviour is detected automatically by comparing sensor observation with an automatically generated model, learned out of observations. Learned models will be complemented with expert knowledge because models cannot learn completely. This will ensure and establish a cheap and accurate model creation instead of manual modelling. Optimization will be performed and results will be verified through simulations. Therefore, the operator has a broad decision basis as well as a suggestion of a DSS (Decision Support System), which will improve the manufacturing system. Operator interaction will be done by a new developed HMI (Human Machine Interface) providing the huge amount of data in a reliable manner.
To reach this aim, every step of the research process is covered by a minimum of two experienced consortium partners, who conclude the results of the project using four demonstrators.
The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Main challenges are reducing ramp-up phases, optimizing production plants to increase the cost-efficiency, reducing time to production with condition monitoring techniques and optimise supply chains including holistic data. Consequently, the resource consumption, especially the energy consumption in manufacturing activities, can be reduced. The optimized plants and supply chains enhance the productivity of the manufacturing during different phases of production. Furthermore, the industrial competitiveness and sustainability in EU will be strengthened.

Status

CLOSED

Url

https://cordis.europa.eu/project/id/678867

Call topic

FoF-08-2015

Update Date

27-10-2022

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Factories of the Future Partnership - Made in Europe Partnership

Result items:

Evaluate the results of the pilot runs of three demonstrators and the numerical benefit

Result items:

Evaluate the results of the pilot runs of three demonstrators and the numerical benefit

The implemented algorithms are validated on lab demonstrators

Transfer the implementation from the lab demonstrators to three prototypes in different industries

Result items:

An algorithm detecting anomalies out of observation based on learned models

An algorithm to classify an anomaly as critical or not

An algorithm to identifying the root cause of a fault

An intelligent optimization algorithm which is able to find the efficient configuration of the factory

Behaviour model which is learned

Evaluate the forecasting quality

Evaluate the results of the pilot runs of three demonstrators and the numerical benefit

Framework for operator knowledge collection

Learned causality model which integrates expert knowledge

Open access summary report

Quality based HMI, which supports decision making in problematic production scenarios

Report on the overall architecture which ensures the collaboration of all WPs

Summary of all requirements for all parts of the project

The implemented algorithms are validated on lab demonstrators

Transfer the implementation from the lab demonstrators to three prototypes in different industries

Result items:

A Survey of Internet of Things and Big Data Integrated Solutions for Industrie 4.0

A flexible architecture for data mining from heterogeneous data sources in automated production systems

Applications of non-monotonic reasoning to automotive product configuration using answer set programming

Defining and validating similarity measures for industrial alarm flood analysis

Detection of regime switching points in non-stationary sequences using stochastic learning based weak estimation method

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency - Intelligent Methods for the Factory of the Future

LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems

Making Implicit Knowledge Explicit – Acquisition of Plant Staff’s Mental Models as a Basis for Developing a Decision Support System

Simulation and optimisation of production lines in the framework of the IMPROVE project

Unsupervised mode detection in cyber-physical systems using variable order Markov models

Using self-organizing maps to learn hybrid timed automata in absence of discrete events

Comment: Unpredicted breakdowns of a plant are often very costly as production stops. The fault has to be identified and spare parts have to be ordered and installed before the production can continue. Condition monitoring can be used to detect wear and therefore schedule maintenance before a complete failure. In case of breakdown a root cause analysis can aid the experts in finding the error cause. Both of these approaches can decrease downtimes significantly and decrease costs.

Comment:

The operator has a broad decision basis as well as a suggestion of a DSS (Decision Support System), which will improve the manufacturing system. Operator interaction will be done by a new developed HMI (Human Machine Interface) providing the huge amount of data in a reliable manner.

Comment: ICT solutions enable the work in IMPROVE. Big Data is collected from the production plants and modern machine learning techniques are used to learn models from data, drastically reducing the manual modelling efforts. Learned models can adapt to the modern, fast changing systems and are available for tasks like condition monitoring and root cause analysis. The results are then displayed to the user, hiding the systems complexity.

Result items:

An algorithm to identifying the root cause of a fault

Comment: Information can also be extracted from higher level data, like alarm lists. Causalities can be learned from alarm floods and bursts and the resulting models can extract possible root causes of faults.

Result items:

An algorithm to identifying the root cause of a fault

Comment: A modern production plant consists of many smaller systems which are connected to each other. Time synchronisation between the systems is often a problem whcih needs to be addressed for tasks like data acquisition.

Comment: Work package 6 develops HMI and decision support system solutions to aid the plant operators in tasks as condition monitoring, root cause analysis and optimization.

Comment: One option is to store the data generated by sending data to a cloud based system which offers services for data analysis and model learning.

Comment: Data from complex production plants is acquired and used to learn models of machines and production processes which are then used for simulation, optimization and diagnosis tasks.

Comment: Models are learned from process data and extended with expert knowledge which are then used to detect wear, predict maintenance windows and find root causes for errors during production.

Comment: Modern communication technologies like OPC-UA are used to acquire data from the controls. Data from distributed controls must be synchronized for complex plant models. Developed model learning strategies can be implemented within the controls themselves when the circumstances allow this.

Comment: The optimization can optimize different parameters/aspects of the plant, including but not limited to energy consumptions.

Comment: Normal behaviour models of the plants are learned durign operation. These models can then be used to monitor the condition of the machine. New situations not covered by the originally learned model can be added through adaptive learning.
Optimization algorithms can find sub-optimal configurations of the plant and improve them. Newly calculated system parameters are verified in a simulation before they are applied to the real plant.

Comment:

Optimization algorithms can calculate better plant configurations. Thesting these new configurations in a real plant can be very cost intensive as production may be compromised during configuration and testing. A virtual environment which is able to simulate new parameters and verify them is a big deal as the running production is not compromised during testing and evaluation of new parameters.

The main focus of IMPROVE are learned models. Manual modeling of system models is not suitable for the complex, fast chaning industrial plants we have today. Lots of expert knowlege is needed to manually create a model. Learned models can be created using only data and little to no expert knowledge is required depending on the technology.

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IMPROVE | Innovative Modeling Approaches for Production Systems to raise validatable efficiency

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Oliver Niggemann