Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0

Kaupp, Lukas; Beez, Ulrich; Hülsmann, Jens; Humm, Bernhard

doi:10.1007/978-3-030-20257-6_5

Cited by 9 publications

(17 citation statements)

References 18 publications

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“…Although these three effects are deterministic, they are also dependent on random variables, such as the clock-duty cycle and frequency, the packet length, and the duration of the blocking, respectively. In conclusion, deterministic jitter J deter (ξ) is a Gaussian distribution, according to the central limit theorem (48) with mean value m det and standard deviation s det .…”

Section: Malfunction Modelingmentioning

confidence: 99%

“…Using different techniques, datasets are transformed, and anomalous data are removed. Techniques based on digital encoders [48], machine learning [49], statistical indicators [50], performance indicators [16] or hybrid approaches [51] have been described. Although these schemes are useful, they cannot be employed in real time, and many other potential malfunctions, such as packet losses, cannot be addressed through these solutions.…”

Section: State Of the Art On Sensor Interoperability In Industry 40 Scenariosmentioning

confidence: 99%

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Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Bordel

2021

Sensors

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Industry 4.0 is envisioned to transform the entire economical ecosystem by the inclusion of new paradigms, such as cyber-physical systems or artificial intelligence, into the production systems and solutions. One of the main benefits of this revolution is the increase in the production systems’ efficiency, thanks to real-time algorithms and automatic decision-making mechanisms. However, at the software level, these innovative algorithms are very sensitive to the quality of received data. Common malfunctions in sensor nodes, such as delays, numerical errors, corrupted data or inactivity periods, may cause a critical problem if an inadequate decision is made based on those data. Many systems remove this risk by seamlessly integrating the sensor nodes and the high-level components, but this situation substantially reduces the impact of the Industry 4.0 paradigm and increases its deployment cost. Therefore, new solutions that guarantee the interoperability of all sensors with the software elements in Industry 4.0 solutions are needed. In this paper, we propose a solution based on numerical algorithms following a predictor-corrector architecture. Using a combination of techniques, such as Lagrange polynomial and Hermite interpolation, data series may be adapted to the requirements of Industry 4.0 software algorithms. Series may be expanded, contracted or completed using predicted samples, which are later updated and corrected using the real information (if received). Results show the proposed solution works in real time, increases the quality of data series in a relevant way and reduces the error probability in Industry 4.0 systems.

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Section: Malfunction Modelingmentioning

confidence: 99%

Section: State Of the Art On Sensor Interoperability In Industry 40 Scenariosmentioning

confidence: 99%

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Bordel

2021

Sensors

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“…Lee et al, 2016; Trunzer et al, 2017) and clustering methods (Y. Wang et al, 2017). Some studies use data warehousing databases and dashboards (Kirchen et al, 2017; Neuböck & Schrefl, 2015; Vathoopan et al, 2018; Zheng & Wu, 2017), and other studies used Neural Networks (Kaupp et al, 2019; C.‐J. Kuo et al, 2017; Qin et al, 2017; Subakti & Jiang, 2018; Tieng et al, 2018).…”

Section: Literature Review Analysismentioning

confidence: 99%

“…. Some studies use data warehousing databases and dashboards(Kirchen, Schütz, Folmer, & Vogel-Heuser 2017;Neuböck & Schrefl 2015;Vathoopan, Johny, Zoitl, & Knoll 2018;Zheng & Wu 2017), and other studies used Neural Networks(Kaupp, Beez, Hülsmann, & Humm 2019;C.-J. Kuo, Ting, Chen, Yang, & Chen 2017;Qin, Liu, & Grosvenor 2017;Subakti & Jiang 2018;Tieng et al 2018).…”

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Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

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Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

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“…Here, we focus on cyber-physical systems (CPS) in Industry 4.0, which is also known as cyber-physical production systems (CPPS) [ 9 ]. A CPS consists of both cyber-elements, e.g., software-modules and physical-components, e.g., sensors and actuators [ 6 , 10 ] and their interaction [ 11 , 12 ]. Regarding the distribution of the term CPPS in research, we also use the broader term of a CPS focusing on production systems.…”

Section: Introductionmentioning

confidence: 99%

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

Self Cite

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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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Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0

Cited by 9 publications

References 18 publications

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Business analytics in Industry 4.0: A systematic review

Fault Handling in Industry 4.0: Definition, Process and Applications

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