University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

The PROGRES approach: language and environment

Abstract: Graphs play an important role within many areas of computer science. Rule-based languages, on the other hand, are well-suited for the description of transformation or inference processes on complex data structures. Why not combining two successful principles in one formalism? This is the common vision of the graph transformation community and the main motivation behind the development of PROGRES, a visual language that supports PROgramming with Graph REwriting Systems. The language PROGRES was developed having the following three design goals in mind: (1) Distinguish between data deenition and manipulation activities and use graph schemata to type-check graph transformation rules. (2) Do not rely on the rule-oriented paradigm for all purposes, but support also imperative programming with control structures. (3) Refrain users from the task to guarantee connuence of deened rules by keeping track of rewriting connicts and backtracking out of dead-end derivations. Its accompanying programming environment ooers assistance for creating, analyzing , compiling, and debugging graph transformations as well as for rapid prototyp-ing activities. Being an integrated set of tools with support for intertwining these activities, it combines the exibility of interpreted languages with the safeness of compiled and statically typed languages.

Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification

Abstract: This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of data-driven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research.