Corina Postelnicu

Retrofitting a factory automation system to address market needs and societal changes

This paper presents the steps taken to retrofit a real multi robot factory automation testbed to achieve increased flexibility, reconfigurability and awareness of assets including energy-relevant, safety-relevant and quality-relevant parameters. Most changes made can be replicated on similar discrete manufacturing settings, as the leveraging technology is Web Services deploying Service Oriented Architecture. The newly introduced technologies and devices can greatly help to address both needs imposed by the market (e.g. fast product customization) and by the society (e.g. the energy savings desired in the manufacturing domain by 2020).

Key Performance Indicators for energy efficient asset management in a factory automation testbed

This paper presents the selection of a set of Key Performance Indicators (KPI) to improve the awareness of different manufacturing assets, including those related to energy efficiency. The implementation relies on Service Oriented Architecture deployed by Web Services and the further processing of the generated events by the application of an Event Processing Language based on rules. The proposed approach is illustrated with the implementation on a multi robot production line.

A cross-layer approach to energy management in manufacturing

Energy management systems (EMS) play an important role to achieve improvements in energy efficiency at manufacturing enterprises. Development and implementation of EMS is rather challenging due to the distributed nature of enterprises and heterogeneity of enterprise medium. Modern ICT solutions and improved architecture paradigms can help to overcome these obstacles. In this paper we propose an energy management system architecture based on SOA 2.0 technologies to ensure intra- and cross-layer interoperability of system components.

Agent-Based Control of Operational Conditions for Smart Factories: The Peak Load Management Scenario

Operational conditions define the minimum requirements for the manufacturing site to operate while at the same time restricting the optimization potential and limiting the opportunities for reduction of resource consumption. Parameters of the manufacturing ecosystem composing these conditions depend on numerous factors. High-level intelligent control is required in order to grasp the complexity of the dependencies between the manufacturing ecosystem parameters and expand opportunities for optimization. This paper describes an agent-based peak load management scenario and considers its implication for control architecture of smart factory operational conditions leveraging on SOA and collaborative intelligent agents paradigms.

Decision support tool for retrofitting a district towards district as a service

Successful adoption of Smart Grid requires technological readiness of all the players from generation plans to the end consumers. This implies many challenges, especially affecting the brownfield urban areas. A decision support platform, allowing evaluation of retrofitting options in terms of cost and performance is required to design and implement urban infrastructure upgrade measures, facilitating timely integration with modern energy systems. This paper presents the approach taken in the URB-Grade project in order to design such a system. Both methodology and overall technological approach are presented, together with description of demonstration scenarios foreseen.

Ecosystem oriented energy management: An implementation

Although traditionally seen as separate entities, factory and building automation systems are interdependent. Approaching the management of energy consumption in such settings in a holistic manner would lead to significant benefits in terms of performance. This paper presents an implementation of an ecosystem oriented energy management solution that considers both machines directly implicated in the production process, and the built environment the equipment resides in. The solution proposed is an embedded service oriented monitoring application applied to a real multirobot production testbed residing in a built environment similar to the situation in a real factory.

An open energy consumption-relevant factory automation dataset in the cloud

In order to fully exploit the potential of optimization algorithms for energy savings in factory automation settings, it is needed to bring together engineers that have knowledge of signal processing algorithms with shop floor engineers that are experienced with real manufacturing processes and factory automation settings. This paper presents an open database storing energy-relevant data from a multi-robot flexible factory automation system. The database is currently populated with data regarding equipment CAMX state events, process/cell/device energy consumption and quality. The database is accessible via a URL and will be further populated with data related to production performance, machine failures and temporary stops etc. The intention is to provide algorithm developers with the needed datasets for design of optimization algorithms for energy efficient manufacturing settings.

Embedded service oriented monitoring for the energy aware factory

This paper presents an embedded service oriented monitoring approach to enhance awareness of energy consumption at the shop floor and Manufacturing Execution System levels in factories. Appropriate aggregation of information allows fulfilling the needs of personnel having different levels of responsibility and knowledge. The method is applied on a real industrial testbed, originally utilized for mobile phone assembly.

Embedded service oriented diagnostics based on energy consumption data

This paper presents a method to identify at an early stage incipient faults in pieces of equipment typically used for transportation of parts in discrete manufacturing settings. The method relies on knowledge of the typical energy consumption of the described devices using different workloads (the energy signature of the machines). At runtime, Support Vector Machines (SVM) are used to classify the input energy data into two pattern descriptions, typical of regular workloads expected on the devices. Consecutive mismatches between the output of the SVM and the workloads observed in real time indicate possibility of incipient failure at device level. The method is implemented using Web Services deploying Service Oriented Architecture in a real multirobot factory automation industrial testbed, originally used for assembly of mobile phones.

CO-Based Outdoor Smart Lighting for Energy Aware Factory

Energy awareness together with holistic perception of consumption processes are one of the main factors contributing to efficient and sustainable performance of such complex systems as buildings, cities, and factories. Availability of relevant data and possibility for cross-domain integration become minimum requirements defining the success of the implementation. Emergence of cooperating smart objects, resulting from evolution in IoT and embedded devices, helps achieving both energy awareness and efficiency by offering possibility of sensing and acting over complex environments and overcome challenges associated with cross-domain integration. This chapter describes smart lighting application for the industrial outdoor environment implemented using cooperating objects featuring Semantic Web Service middleware. Presented use-case considers the university campus area comprising multipurpose outdoor area and neighbouring industrial laboratory facilities. The application is aiming efficient use of energy and possibility for integration with relevant industrial systems.