Name
Title | ||
|---|---|---|
A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers. |
Abstract | ||
|---|---|---|
Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device- and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the aggregation of control signals and process variables. Further challenges arise from the advance of flexible cyber-physical systems (CPS) and the industrial internet of things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. In this context, data-driven systems which are capable of unsupervised learning are promising approaches. The aim is the autonomous identification of significant process variables and patterns. This paper describes a machine learning approach for a condition and process monitoring system on the basis of pattern recognition within structure-borne noise of rotating cutting machinery. Process states are defined under application of non-negative matrix factorization (NMF). A production model is learned and deployed on the basis of Gaussian mixture models (GMM) and hidden Markov models (HMM) in a two stage process. Additionally a generic framework to ease the implementation of decentralized condition monitoring functionalities is given. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server. The approach is evaluated on the example of mill-turn centers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11740-018-0797-0 | Production Engineering |
Keywords | Field | DocType |
Condition monitoring systems, Smart factory, Cyber-physical systems, Machine learning, Unsupervised learning, Machine tools | Data-driven,Process state,Manufacturing engineering,Network topology,Unsupervised learning,Cyber-physical system,Condition monitoring,Engineering,Hidden Markov model,Mixture model,Distributed computing | Journal |
Volume | Issue | ISSN |
12 | 3-4 | 0944-6524 |
Citations | PageRank | References |
1 | 0.41 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Kißkalt | 1 | 1 | 0.41 |
| Hans Fleischmann | 2 | 1 | 0.41 |
| Sven Kreitlein | 3 | 1 | 0.41 |
| Manuel Knott | 4 | 1 | 0.41 |
| Jörg Franke | 5 | 26 | 20.00 |