@inproceedings{Kaupp2021,

title = {CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory},

author = {Lukas Kaupp and Heiko Webert and Kawa Nazemi and Bernhard Humm and Stephan Simons},

doi = {10.1016/j.procs.2021.01.265},

issn = {1877-0509},

year  = {2021},

date = {2021-02-17},

booktitle = {Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)},

volume = {180},

pages = {492-501},

publisher = {Elsevier},

series = {Procedia Computer Science},

abstract = {Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a smart factory form a complex environment making the fault diagnosis non-trivial. Along with a dataset, we give a first definition of contextual faults in the smart factory and name initial use cases. Additionally, the dataset encompasses all the data recorded in a current state-of-the-art smart factory. We also add additional information measured by our developed sensing units to enrich the smart factory data even further. In the end, we show a first approach to detect the contextual faults in a manual preliminary analysis of the recorded log data.},

keywords = {anomaly detection, contextual faults, cyber-physical systems, fault diagnosis, smart factory},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020db,

title = {An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

doi = {10.1109/IV51561.2020.00064},

isbn = {978-1-7281-9134-8},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {350-359},

publisher = {IEEE},

address = {New York, USA},

abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.},

keywords = {Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines, Protocols, Reasoning, Smart manufacturing, Task analysis, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}