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

During a machine's design phase a lack of reliable specification data resulting from the use phase specific application leads to energy losses in various discrete manufacturing processes. The reason for instance could be inefficient design of drive components or insufficient machine control. In order to support machine designers with reliable input data to e.g. dimension drive components in energy efficient way, this contribution presents an approach how to measure and interprete energy consumption data of machines during its use phase. This can be applied to derive energy efficiency measures on components level. The identified measures then are implemented during the design phase of the next machine generation or realized during the machines use phase by energy efficient machine upgrading.

Rising energy costs, a green image and fulfilling of political demands are some reasons, why companies try to reduce their energy consumption. Biggest challenge is to identify potentials, derive measures and realize them. Although, measuring instruments are available in a great variety there exists no cheap and flexible system, which uses the measured line for supply and data transmission. This innovation, introduced in this paper, delivers a new possibility for post-crosslinking and smart energy monitoring. Based on current powerlinecommunication (PLC) technologies and modulation methods, a hardware was developed. A program handles the PLC and manages the measuring. The system has sensors for alternating voltage and current and calculates frequency, power factor, active, reactive and apparent power. For visualization and further analysis, a software was established. The complete system was evaluated in a production facility. The accuracy shows a difference of 1.5 % to a reference.

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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.