CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory

Kaupp, Lukas; Webert, Heiko; Nazemi, Kawa; Humm, Bernhard; Simons, Stephan

doi:10.1016/j.procs.2021.01.265

Cited by 15 publications

(35 citation statements)

References 5 publications

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“…So while following the described fault handling process, sufficient work should be invested into accurate data. Only very few data sets in the context of industrial applications and smart manufacturing are available for open access [ 17 ], which exacerbates validation of the defined fault handling process significantly. Therefore, validation shall be fetched in a later stage.…”

Section: Discussionmentioning

confidence: 99%

“…As modern production sites implement Internet of Things (IoT) devices [ 17 ] and several sensors to monitor machine condition, the data amount can also reach a high complexity. Sensors monitor various variables, based on both physics, like vibration, temperature, pressure, and based on processes of a manufacturing system, like process deviations, control settings, and machine specifications [ 1 , 17 ]. The complexity in data can be handled, e.g., via dimensionality reduction methods.…”

Section: Requirements For Effective Fault Handlingmentioning

confidence: 99%

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Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

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Section: Discussionmentioning

confidence: 99%

Section: Requirements For Effective Fault Handlingmentioning

confidence: 99%

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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“…A domain expert evaluates the variables found with the method to determine how valuable they are for later analysis if one of the contextual error occurs. [6]. A monorail shuttle system connects the high-bay storage with a six-axis assembly robot, a press, the optical and weight inspection and the electrical inspection station.…”

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confidence: 99%

“…Figure 4. Production-equal smart factory at the DUAS[6]. A monorail shuttle system connects the high-bay storage with a six-axis assembly robot, a press, the optical and weight inspection and the electrical inspection station.…”

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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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