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SELSUS Report Summary

Project ID: 609382
Funded under: FP7-NMP
Country: Germany

Periodic Report Summary 2 - SELSUS (Health Monitoring and Life-Long Capability Management for SELf-SUStaining Manufacturing Systems)

Project Context and Objectives:
Today’s production equipment must enable mass customization, has to enable to adapt various environmental impacts, and fluctuating production volume. Additionally, the cost pressure increased significantly, due to increasing suppliers. Along with this, to enable availability of production equipment and by that establish a high level of ability to deliver within supply-chain considering low level of inventory and buffer stock, just ensuring relative high level of reliability and availability at production level is not sufficient.
The European Union significantly supports industry, suppliers, research institutes and universities in facing this challenge to manage the switch to digitalization of the production, comprising component, equipment, and entire line and considering the entire life-cycle starting from planning phase, down to ramp-up, production, and re-use.
With a new and advanced machines, fixtures and tools, comprising extended sensor capabilities, smart materials and ICT self-diagnosis and self-awareness will be enabled within SelSus project and will also prove self-healing capabilities
Additionally, distributed diagnostic and predictive and renovation models will be an integrated part of smart devices, enabling prognosis failure modes, component degradations, and by that predict future downtimes of devices.
These goals are reflected in the work packages and milestones, focusing on conversing vision into real working machines and production lines. As one of the key objectives within SelSus, data will be gathered, transformed into information and knowledge, and by that may offer an added value to supplier and end-to-end-user using self-diagnosing and self-healing devices.
Sensor networks, built up of a variety of sensor nodes, will add the capability for distributed analysis, interoperable and delay-tolerant communication.
Integrated models and methodologies for predictive maintenance will enable constant life-long assessment, by forecasting degradation and deterioration trends of single components and equipment. Additionally, machine learning and data mining techniques, combined with discrete material flow simulation models will enable decision models, on how to anticipate unforeseen future malfunctions and downtimes.
These methodologies will be proofed and demonstrated on three different levels, which are device level, equipment level, and factory, respective line level.
As scheduled, one integrated task will be, to disseminate publishable results. Additionally, to website, Linked-In account, flyers, etc., the consortium organized a Smart Factory Workshop to directly communicate goals and results to industry and universities.

Project Results:
The main goals for the phase reported, being achieved, were to get the project started; aligning all partner and create a commitment to the overall project aims. Therefore, and as a first step, the elicitation of industrial requirements, use cases and demonstrators through bilateral meetings with the different project partners were of upmost importance. Based on the gathered industrial requirements and use-cases the overall concepts and architecture for the predictive maintenance environment were developed and described in the SelSus Architecture Concept Document. The relevant data for condition monitoring of smart devices and machines as well as the specification and the development of a conceptual framework for such devices were defined as the basis for further work in different WPs.
To achieve this, the analysis model structure and SelComp architecture has been evaluated and updated based on implementation decisions and feedback from demonstrators. To ensure developments address real business motivations, guidelines for new SelSus business models, considering major IPR aspects, has been defined.
As base for further developments, the common data model has been implemented into welding controls, linear axis and jigs and fixtures, enhanced with interface with system level. These have been equipped with WSNs (Wireless Sensor Networks) accessing Sensor SelComp template and a, so called, Dynamic Modular Software Reconfiguration, to increase flexibility to operate the sensing capabilities at shop-floor level. Additionally, a HMI using mobile application to increase the flexibility in data presentation, able to show sensor data from Sensor SelComp and recognize via WSN using QR Code at shop-floor level.
Furthermore, a structure of Sensor SelComp was created and reported based on several brainstorming sessions between all the partners. This structure was used to further develop a prototype to validate all the specified requirements, where sensors were successfully integrated, being able to monitor, access and manipulate the information, not only at the Component level (Sensor SelComp), but also at the System Level (Cloud). Further related investigations concerning the internal structure of SelComps, the connection to clouds and the interaction with human were performed. Regarding the topic of degradation prediction of wear-out components Bayesian network models were developed already. This includes the use of condition-based monitoring methods, identifying relationships between components failure and wear-out, revising and adapting methods for condition monitoring as well as feeding the models into the project’s diagnostic models. Based on information about the healthiness of linear axis a first model for diagnosis was developed. To demonstrate the interaction between sensors and Bayesian networks, an application that continuously measures spectra of permanently changing colors and queries a Bayesian network for the probabilities of 16 color states and visualizes the results in on-line mode was developed.
Based on use cases and scenarios for the decision support for renovation and repair strategy enables a better understanding what the decision support business process is and what the impact and solution strategies in case of real or expected downs would be.
Using the common data model for life-cycle knowledge is suitable to represent expert knowledge from the design phase of the machine tool and from the design phase of production systems as well as the lifelong gathered feedback in the usage phase. In addition to this, an enhancement of the FMEA modelling procedure including the way to model it in a common COTS-Tool has been derived.

Potential Impact:
The SelSus project`s results and deliverables will enable the manufacturing systems industry extending the lifetime and performance of machines and equipment as well as implementing innovative strategies for the efficient design of renovation, refit and repair activities. Based on knowledge on the current state of equipment regarding its potential failure risks, the degradation of its components, system availability as well as energy data, the performance of manufacturing equipment during their whole life cycle can be optimized, by introducing an integrative decision model. The model takes into account the distributed nature of knowledge and is capable of adapting to complex environments resulting from the end user’s requirements. It will help to reduce repair and renovation costs by means of improved monitoring strategies for predicting failures and assess component degradation. Furthermore it will support a better recovery of substituted materials and help eliminate hazardous situations. With the integration of prior knowledge and expertise which is Europe’s greatest resource, SelSus guarantees the European manufacturing industry an outstanding position within the global market.
SelSus will allow the component suppliers to enhance their high-quality precision automation components with inherent sensor and prediction capabilities for more intelligent predictive maintenance and renovation. While today, the typical component supplier can only suggest standard maintenance intervals and activities to its customers, the SelSus approach will allow customers to apply optimal predictive maintenance depending on the actual usage conditions. This will reduce scheduled downtimes for preventive maintenance as well as unscheduled downtimes for component failures. Consequently, SelSus will lead to significant added value for their customers during a component’s life-time which will allow the component suppliers to charge premium prices (an approximate 10% increase), but most importantly strengthen their position on the worldwide market for high quality automation components.
Applying the self-diagnosis and predictive maintenance and renovation methods proposed by SelSus, typical system integrators will be able to forecast eminent maintenance requirements more accurately and also react quicker to disruptive events and guide corrective actions leading to a projected reduction in system downtimes by 30%. Furthermore, by applying the proposed methods and procedures the root causes for failures and degradation trends become more transparent and can be resolved more rapidly. By using augmented reality and exact guidelines how to repair and upgrade the production system it is expected that repair and maintenance costs can be reduced drastically which results in an added value for the end user. This will increase value of machinery and equipment by approximately 10%.
The manufacturing systems of a typical end user have to be considered as mass production systems for products with very low profit margins. In terms of the low profit margins the SelSus project plays a significant role to increase the turnover by having fewer breakdowns, improving equipment performance and extending the lifetimes of its manufacturing systems. Currently maintenance is performed by ad hoc teams of the personnel that work with equipment on a daily basis, taking advantage of their experience and expertise to detect malfunctions and find a way to remedy these accordingly. By using predictive maintenance and intelligent diagnostics root causes of malfunctions can be detected and workarounds can be generated rapidly. Despite of efforts spent for a preventive maintenance, the lack of predictive maintenance still affects the machine functionality for several minutes and thus the manufacturing equipment performance. By using the enhanced availability model it is expected that the overall equipment efficiency (OEE) will improve.

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