Arnim Reger

Identifying Energy Efficiency Potentials by Applying Flexible Measuring Systems

This paper presents a flexible measuring system to identify energy efficiency potentials in the context of the ISO 50001 standard. On the basis of five essential requirements the flexible measuring system was structured into 4 modules which can be separately extended or modified. As the flexibility was in focus of the development this system it is able to measure the energy consumption on a very detailed level of the components of a machine. In addition it can also acquire measurement data of different other sensor signals like temperatures, flow rates etc. To evaluate the usability of the system in order to identify energy efficiency measures a use case was conducted. Results of the measurement data as well as possible energy savings of the investigated machinery are discussed at the end of this paper.

Automated Pattern Recognition in Load Profiles of Milling Operations

Due to the introduction of an energy management system, a lot of existing manufacturing plants were equipped with energy measurement systems. With sufficient sample rates those retrofitted energy measuring systems could provide additional information beside active power and energy consumption. Each production plant is characterized by a process and product specific power consumption with an associated power signal. In this paper a method to determine the information content in power signals of milling operations is discussed. By using the cross correlation function and hidden markov models (HMM) for operation recognition and automatic derivation of energy key performance indicators (EnPI) can be realized. In addition, further production related key performance indicators (KPI) can be derived with pattern recognition in load and current profiles.

Analyzing the active power of variable frequency drives in manufacturing plants

In multi-axis machining centers the individual operating loads of axis and spindle drives are often unknown. Especially in serial production, drive systems are oversized including electric motors, frequency inverters and shaft drives. But also in special plant engineering, where multiple drive systems are connected to one intermediate circuit, the individual operating loads cannot be assessed. Subsequently, performance, operating points and energy efficiency are suboptimal. Economic disadvantages, such as the necessity of higher investments and increased operational costs, are associated. Therefore an approach to active power measurement behind the frequency inverter to separate the power consumption profiles of spindle and axis drives is necessary. In this paper two systems for an active power measurement for variable frequency drives are discussed. One of them has been developed by the authors themselves with the advantage of lower cost compared to commercially available systems. The systems were analyzed for performance, accuracy, installation effort and costs. The investigated active power measurement systems provide basic data for an optimal dimensioning of drive systems for industrial equipment manufacturers.

Methodology to Increase Energy Efficiency in Discrete Manufacturing

The utilisation phase of machinery in discrete manufacturing operations is characterized by changing economical and technical requirements like capacity, performance and as emerging requirement reduced energy consumption. Established industry practices as well as upcoming standards mainly focus on improving the energy efficiency by developing new machinery. Especially existing factories and the machinery in use offers energy saving potentials to be identified and to be capitalized by implementing energy saving retrofit measures. By doing so, the use of existing manufacturing machinery leads to a sustainable use of manufacturing equipment. The discussed research work therefore includes an approach to interpret in-process measurement data and to derive electric energy savings potentials. Based on this assessment, improvement measures like dimensioning, reduction of baseline energy-consumption by updating the PLC and minimisation of peak loads by energy management is engineered. Finally the financial impact of the obtained energy savings is quantified by evaluating the developed methodology during several use cases.

Lean Data Services: Detection of Operating States in Energy Profiles of Intralogistics Systems by Using Big Data Analytics

Due to the rising energy costs and the increasing competition on the market the improvement of the energy efficiency in production systems can be a great chance for companies to gain an advantage compared to their competitors. Therefore, the transparency of the energy consumption of the several processes of such systems has to be known in detail. In this paper a method for the detection of operating states in production systems, based on big data analysis is introduced. The developed algorithm automatically detects different operating states in the current profile and improves his accuracy the longer it is used. To evaluate the developed data analysis algorithm, a defined process with a conveyor belt was considered and measured several times. The algorithm defines clusters from the measurements and identifies classes for the several operating states. With the naive Bayes classifier it is possible to categorize the clusters more fine-grained and faster than with a correlation function. An advantage of the method is that each new classified instance is taken into account for future unclassified instances. So it is possible to integrate a continuous learning process in production processes and to consider a slowly drift of states. Also, the subsequent addition of classes and attributes that are represented in this work by clusters, is possible at any time.

Pattern recognition in load profiles of electric drives in manufacturing plants

With the introduction of energy management systems, an analysis of load profiles of manufacturing plants becomes increasingly important. Each manufacturing plant is characterized by a process and product specific power consumption. Often, electric drives are the main power consumers. In this paper methods for pattern recognition in load profiles of electric drives are presented on the example of a multiaxial lathe. A transfer of techniques used for speech recognition e.g. Hidden Markov Models, Fourier and Wavelet Transforms to manufacturing application is discussed. In combination with energy measurement systems, those techniques proved to be a good solution regarding energy efficiency calculations and derivation for key performance indicators. The investigated methods can also be applied to other process data with significant cost advantages, because a lot of process information can be extracted from a single sensor.