Context-Aware Documentation in the Smart Factory

Beez, Ulrich; Kaupp, Lukas; Deuschel, Tilman; Humm, Bernhard; Schumann, Fabienne; Bock, Jürgen; Hülsmann, Jens

doi:10.1007/978-3-662-55433-3_12

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Additionally, V110 requests to rate the re-learnability of the software after a lengthy interruption. Both groups attested to the Flourish dashboard good re-learnability (median 5), but the domain experts were more confident (4)(5) than the junior professionals (3)(4)(5). Nevertheless, in summary, the median in all answers to the statements is between 4 and 5, with a majority of 5 (11 out of 18) which is a clear hint toward the usefulness, capabilities, and task adequacy of the Flourish dashboard and context-aware fault diagnosis.…”

Section: Standard Questionnairesmentioning

confidence: 91%

“…As the context-aware fault diagnosis and the Flourish dashboard are novelties for the audience, this rating weighs more for the evaluation and indicates how much the experience system varies from traditional fault diagnosis methods. Here, the median of the junior professionals (A: 2.5) indicates disagreement, but the majority oscillates between 2 and 4, with an answer over the whole scale (1)(2)(3)(4)(5). This is explainable by the technical background of the professionals and their various qualifications, which range from pre-graduate to post-graduate students.…”

Section: Standard Questionnairesmentioning

confidence: 94%

“…Following the principles of the TAOISM VA model, each context can be used as a foundation for a detection model that reflects a error model. Besides a prototypical end-toend implementation of the context-aware fault diagnosis, each aspect of the TAOISM VA model has undergone distinct research in different proof-of-principles [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

2022

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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.

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Section: Standard Questionnairesmentioning

confidence: 91%

Section: Standard Questionnairesmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

2022

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“…All information must be transformed for beneficial use in mathematical models or NNs. We already released several strategies to transform and enrich data, from supervised [ 8 , 9 ] to unsupervised approaches [ 3 , 10 ]. A conversion challenge exists to convert all events and their values to a numerical format with a uniform distribution.…”

Section: Background: Context-aware Diagnosismentioning

confidence: 99%

“…Our previous work focuses on different aspects of the TAOISM model [ 2 , 3 ], such as supervised [ 8 , 9 ] or unsupervised models [ 3 , 10 ], visualization theory [ 11 ] (in German) or data [ 6 ]. There are still faults in a smart factory that are yet uncovered.…”

Section: Background: Context-aware Diagnosismentioning

confidence: 99%

Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

Kaupp

Humm

Nazemi

et al. 2022

Sensors

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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.

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