2022 |
| 4. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Artikel In: Electronics, Bd. 11, Nr. 23, 2022, ISSN: 2079-9292. @article{electronics11233942,
title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},
author = { Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
journal = {Electronics},
volume = {11},
number = {23},
abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},
keywords = {Artificial Intelligence, Case Study, Data Analytics, Data Science, Data Visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions. |
| 3. | Lukas Kaupp; Bernhard Humm; Kawa Nazemi; Stephan Simons Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis Artikel In: Sensors, Bd. 22, Nr. 21, 2022, ISSN: 1424-8220. @article{s22218259,
title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis},
author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons},
doi = {10.3390/s22218259},
issn = {1424-8220},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
journal = {Sensors},
volume = {22},
number = {21},
abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.},
keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, smart factory, Smart manufacturing},
pubstate = {published},
tppubtype = {article}
}
Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert. |
| 2. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Buchkapitel In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. @inbook{Kaupp2022,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403--436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research. |
2021 |
| 1. | Lukas Kaupp; Heiko Webert; Kawa Nazemi; Bernhard Humm; Stephan Simons CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory Konferenzbeitrag In: Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020), S. 492-501, Elsevier, 2021, ISSN: 1877-0509. @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}
}
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. |
