Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model

Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard

doi:10.1007/978-3-030-93119-3_16

Cited by 2 publications

(31 citation statements)

References 38 publications

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“…Here, we propose a methodology of context-aware diagnosis, where context is used to mitigate complexity in the diagnosis process in every stage of the analytical process. We released the TAOISM model [ 2 , 3 ] to integrate the context in the diagnosis process and scope discussions about different parts of the process. The model should be seen as open and living and as a guideline for future research affected throughout context-aware diagnosis processes.…”

Section: Background: Context-aware Diagnosismentioning

confidence: 99%

“…Context is a “situation within which something exists or happens, and that can help to explain it”. This is a vague definition, so we made an initial attempt to define the context of the current production situation mathematically [ 2 , 3 ]. The context is a window (or multiple windows) around an outlying timestamp over all available information sources [ 2 ] that may encompass other contexts in so-called context hierarchies [ 3 ].…”

Section: Background: Context-aware Diagnosismentioning

confidence: 99%

“…(2) We provide a novel voting strategy to facilitate minority voting in AE ensembles to detect outliers. (3) We detail the TAOISM model with an intelligent data selection strategy to support the context-aware diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, we proposed a Visual Analytics (VA) model called TAOISM (contex t - a ware diagn o sis i n s mart m anufacturing [ 2 , 3 ]). The TAOISM model reflects the context-aware fault diagnosis process in a smart factory.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, context facilitation may mitigate the increased complexity and help speed up problem solving in a rapidly changing environment under the Industry 4.0 paradigm. Along with new interaction possibilities, visualizations and models, we were able to show that context can be used in graph neural networks to reduce training time while maintaining a small reconstruction error for improved predictions and outlier detection [ 3 ]. Context-aware diagnosis with the TAOISM model is a part of Cognitive Manufacturing (CM).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Background: Context-aware Diagnosismentioning

confidence: 99%

Section: Background: Context-aware Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kaupp

Humm

Nazemi

et al. 2022

Sensors

Self Cite

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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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Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model

Cited by 2 publications

References 38 publications

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

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

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

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