Abstract: Adherance of power quality standards is crucial for electricity network operation and bothindustrial and private customers. However, the transition to electricity supply based on renewable, de-centralized plants which feed in using inverters is accompanied by challenges regarding compliancewith power quality standards. Accordingly, detection and automated processing of relevant variablesof power quality is of increased importance. Digital signal processing offers various approaches of sig-nal evaluation each having individual advantages and disadvantages regarding different power qualityvariables. As a result of incresing decentralized feed in of regenerative plats using inverters, the si-nusoidal fundamental of the power system is distorted and harmonics occur. For elimination of thoseunwanted signal components, a variety of methods is available. Supple- menting current research ac-tivites a laboratory model of an active ﬁlter using low budget prototyping hardware is developed andevaluated. Therefore, an experimental circuit containing a band pass ﬁlter for signal adjustment aswell as a PWM with ampliﬁcation circuit for signal correction have been elaborated. Necessary cal-culations are performed by a standard Atmel microprocessor as used by Arduino Uno, including an8-bit analog to digital converter (ADC).
Abstract: In the field of energy supply for residential and commercial buildings, the optimal operation,system configuration and sizing of generation as well as storage technologies are essential stepsfor cost-efficient investments. In current research, flexibility options for electricity from growing renewableenergies attract attention. One of the considered options is the power-to-gas technology incombination with fuel cells. Linear Programs for optimal system operation exist for example for distributedenergy systems. In this study we propose a Mixed Integer Linear Program of a power-to-gasunit consisting of an electrolyzer, a fuel cell and a hydrogen storage. For the fuel cell a minimum loadand a non-linear efficiency curve is taken into account. The non-linear efficiency curve is approximatedby piecewise linearization. Bilinear products in the modeling of the efficiency curve are beingsubstituted to maintain full power plant sizing and operation functionality. Different fuels, such as naturalgas and hydrogen to be converted in the fuel cell, are implemented as well. As a result, we showthat a detailed model of the non-linear efficiency curve of a fuel cell leads to more accurate results concerningthe system operation. The configuration of system components in the observed energy systemchanges. Especially the battery system experiences a change in sizing and operation. However, solvingtime of the model is increasing dramatically. Our results demonstrate a valuable approach to comparethe results of a Linear Program to a Mixed Integer Linear Program. Hence giving the possibility toevaluate the necessity of detailed over simplified models regarding calculation of cost-effectiveness.
Abstract: Compressed air systems (CAS) on industrial plants consist of air compressors, compressed air reservoirs, compressed air lines and auxillaries as dehumidifiers, dust collectors, pneumatic oilers and pressure controllers. It is assumed that given suitable dimensioning, those industrial compressed-air systems can be used for demand side management purpose. In industrial CAS energy is transmitted by means of pressure difference and volumetric flow rate of the transmission medium compressed air. Alike electric circuits, they consist of various functional components which are flowed through by the compressed air. Furthermore the application of Kirchhoffs laws is possible to those systems. Hence, approximation of the behavior of industrial CAS is possible by arranging and connecting those components in an equivalent circuit diagram. As additional state variables of compressed air as humidity, temperature as well as contents of water, oil and dust are also to be considered, modeling of the individual components is more extensive. A general, abstract approach for the description of the individual components in the form of blackbox representations is outlined.
Abstract: In industrial companies, production orders are distributed to production equipment using tools of production planning and scheduling (PPS). Main targets are to approach full capacity and profit maximization. As a further dimension of optimization, the demand of compressed air of individual production steps and time-varying cost of compressed air due to fluctuating electricity prices can be considered in PPS systems. Hereby it is attempted to execute production steps associated with significant demand of compressed air in times of low compressed air cost. Based on the approach, a methodology for implementation of compressed air demand into a PPS tool is developed.
Abstract: This paper presents a method to support the development of energy management concepts for machine and plant construction. The energy management concepts are required to put the plant components into an energy saving mode during unproductive phases. These concepts then have to be implemented in the control software. Different dependencies in the production and process flow have to be considered when developing the concepts. Due to the complexity of production plants, a supporting simulation tool is planned to be implemented. With the aid of this tool, different energy management concepts and their derived control software can already be validated virtually in the planning and development phase. This presents an energetic extension of the so-called virtual commissioning concept. Conventional virtual commissioning involves only the process operation functionality in a virtual simulation model of the plant. Now, however, energetic functionalities are assigned to the different model components. Thus, a simulation of the energy consumption in different operation modes can be created for each component. Energy management concepts can only be developed if the components’ energy consumption is known in the different scenarios.
Abstract: Modern large-scale assembly lines need to deliver a highly varied and flexible output, while achieving 0 ppm scrap. This is becoming more and more demanding due to an increasing complexity of the products. Thus, it will be a major step in manufacturing processes to develop process monitoring strategies which increase productivity as well as flexibility and reliability of the entire assembly process. Therefore, it is necessary to advance the entire chained assembly line instead of only isolated processes and stations. For this reason, technological processes have to be assessed as a chain of upstream and downstream partial processes instead of being considered in isolation.  Moreover, data mining projects depend on the available data bases, while additional data sources may increase the derived knowledge.  These ideas are extendable by energy data measurements, besides process and quality data. Existing monitoring approaches to reduce scrap usually use dashboards linked with process and quality data.  Therefore, this paper presents a new methodology using data mining analysis of energy data for assembly presses as well as complete assembly lines for electromagnetic actuators. This novel holistic approach realized by a Quick Reaction System allows to increase efficiency, while decreasing energy and resource consumption for actuator manufacturing on large scale assembly lines. In particular, the data base consists of process and quality data, enriched by energy data measurements. This approach enables a comprehensive process characterization as well as monitoring of whole assembly lines by using data mining tools. Furthermore, this paper describes a quantitative evaluation of its data mining based event detection of critical process parameters.
Abstract: Conventional serial and workshop productions use specific parameter ranges to evaluate the quality of a process. Our research showed that parameters within tolerances do not ensure good quality of the final product due to malicious parameter combinations along the assembly line. Therefore, data sets from assembly processes like force-way or force-time curves and quality measurements are evaluated in this novel approach. Using Fourier Transform, k-means, decision trees and a dynamic envelope curve, classification and process monitoring are processed in time and frequency domain. This enables new possibilities to characterize quality and process data, for advanced error detection as well as a more simplified tracing of faults.
Here, holistic optimization and monitoring follows two strategies. First, a simplified tracing approach of malicious impacts regards quality results from test benches. Therefore, assembly processes are monitored and characterized by quality data. Second, defective influences, like tool break or calibration errors, are linked to variations of the usual process behavior. Here, the error detection approach focuses on process data from single assembly stations.
This approach uses three different methods. First, Fourier Transform extracts additional information from process, energy and quality data. Second, k-means algorithm is used to cluster quality data and extend the data base. Third, a decision tree classifies the quality of the final good and characterizes assembly processes. Last, results of k-means clustering and selected classification methods are compared. This combination allows to increase process quality, improve product quality and reduce failure costs.
Abstract: The advance of digitalization changes the requirements of processes in industrial production and assembly. For this reason, production and assembly must now be able to execute complex process steps. This is about quality and productivity expectations, as well as flexibility and reliability of production, lines and plants . Today, data is generated by almost every system, machine and sensor, yet it is hardly used for process optimization. Manufacturing processes are usually organized as workshop production or chained production systems, in addition to standalone machines [2,3]. Most analytic projects focus on chained systems and serial production, unlike individual machines and specific workshop production. Depending on manufacturing IT, process data from serial production is stored in data bases, which are usually optimized for traceability. Standalone machines and machines within workshop production are scarcely connected to a common data base. The required process data is stored either on the module itself or inside a local data base . The identification of dependencies between individual assembly processes, energy data and the quality of the finished product is necessary for an extended optimization. These optimizations can be process-specific, as well as environmental and resource related. Due to decentralized process data storages, an overall view of a dynamic order-oriented value chain is denied. Therefore, the potential of the machines is largely unused. Based on Data Mining, this advanced development can be counteracted by process monitoring and optimization. Therefore, this paper provides a solution for a virtual process data linkage of assembly stations. This enables the acquisition, processing, transformation and storage of unstructured raw data by special software and methods, which is also able to cope with chained production systems and standalone machines. For further analysis of interdependencies, a visualization is developed for advanced monitoring and optimization [5,6].
Abstract: Energy efficiency is a critical competitive factor. Transparency of energy consumption is the key for increasing efficiency of production. For this purpose, existing energy data management systems collect data such as power, gas or water consumption on field level, save them in databases, and aggregate them in reports. However, the identification of saving potentials and the definition of efficiency measures is carried out by energy experts and thus is dependent on a person’s knowledge. The documentation of knowledge about saving potentials and measures does not take place and relations among data and knowledge of various domains are not captured. In this paper, we provide an approach that allows the holistic capture and description of data and knowledge relations. Through the use of an ontology-based meta model, consumption data can be augmented with information about time and place of capture, data type, intended purpose and permissions, as well as interfaces to other systems and relations to knowledge elements. The semantic model is to capture relevant requirements of all information demanders within the energy data management cycle. Therefore, the model is capable of detecting efficiency deficits and retrieving relevant energy efficiency measures within a knowledge base. Thus, energy consumption data can be efficiently used and knowledge about efficiency can be sustainably preserved.