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Cited by 11Digital Transformation in IndustryFault Detection and Control SystemsMachine Fault Diagnosis Techniques

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Streaming Synthetic Time Series for Simulated Condition Monitoring

Open access
Zenisek, Jan, Wolfartsberger, Josef, Sievi, Christoph, Affenzeller, Michael·2018·IFAC-PapersOnLine
DOI 10.1016/j.ifacol.2018.08.391
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Abstract

The transformation of the common production plant to a cyber-physical system is one of the most recent developments in manufacturing industry. The analysis of data streams produced by such sensor-equipped plants promises potential for process optimization. However, for competitive reasons and due to the novelty of the development, the access to publicly available real-world data is quite limited. Hence, businesses and researchers new to this area are often confronted with the cumbersome and complex task of synthesizing time series in order to gain first experiences. This work presents a novel concept and a corresponding software implementation for the stream-wise generation and publication of sensor data, to simulate condition monitoring of industrial production plants. The resulting configuration file-driven tool is capable of acting like a sensor-equipped plant by generating data points from various Gaussian Process-based models and mathematical expressions, or by replaying data sets. Moreover, it can be seamlessly connected with existing surrounding systems by using the MQTT protocol. In this context, the software aims at laying the foundation for real-world applications and improving them by providing a simulation tool to prototype and test with.

Overview

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Registry

Crossref · 10.1016/j.ifacol.2018.08.391

Cites

11 of 11 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Partially corrupted data from field devices affects real-time process control and time series analysis used for equipment reliability analysis, predictive maintenance, process optimization, condition monitoring, and quality control.

    “If there is even partially corrupted data coming from field devices, it affects not only real-time process control, but also time series analysis applied in various algorithms used for equipment reliability analysis i.e. predictive maintenance [44], [45], process optimization, condition monitoring [46], quality control [47], [48] and similar.”

    From SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0 (2022) · cites this paper as [45]

Citations

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Research works printed but not linked: 2

  1. Christ, M., Kempa-Liehr, A.W., and Feindt, M. (2016). Distributed and parallel time series feature extraction for industrial big data applications. arXiv preprint arXiv:1610.07717. · arXiv
  2. Hermann, M., Pentek, T., and Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. In 49th Hawaii International Conference on System Sciences (HICSS), 3928–3937. IEEE.

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Metadata

Authors
Zenisek, J., Wolfartsberger, J., Sievi, C., & Affenzeller, M.
Affiliations
  • Johannes Kepler University of Linz (AT)
  • University of Applied Sciences Upper Austria (AT)
Corresponding author
Zenisek, Jan
ORCID
Venue
IFAC-PapersOnLine
Publisher
Elsevier BV
Published
2018
Volume
51
Issue
11
Pages
643-648
ISSN
2405-8963 (ISSN-L)
DOI
10.1016/j.ifacol.2018.08.391
OpenAlex
W2891086480
Cited by
11
References
11
Citations by year
2026: 1 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 4 · 2018: 1
FWCI
1.24
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
IFAC-PapersOnLine (publishedVersion)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Keywords
synthetic time series generation, condition monitoring, cyber-physical systems, industrial production plants, sensor data generation, Gaussian processes, MQTT, data replay, process optimization

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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