Automated Pattern Recognition in Load Profiles of Milling Operations

Abstract

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.

Process and Energy Data Acquisition on Machining Center and Individual Machine Components

“…Due to the high flexibility and fast adaptation possibilities of the system, sensors and sampling rates can easily be adapted for investigation on process, machine or even component level. Even Götze, Hamacher, Reger, et al confirmed in [18,20,21] that the precise adjustment of the measuring system is critical for the analysis.…”