Robot control and decision making through real-time sensors monitoring and analysis for industry 4.0 implementation on aerospace component manufacturing

Wijaya, Tomi; Caesarendra, Wahyu; Pappachan, Bobby Kaniyamkudy; Tjahjowidodo, Tegoeh; Wee, Arthur; Roslan, Muhammad Izzat

doi:10.1109/pacrim.2017.8121928

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Carvajal Soto, Tavakolizadeh and Gyulai point out that the implementation of a sensor data analysis system would not be possible without sufficient security measures to maintain correct data accessible only by confidential employees [14]. Security measures identified in the cited publications include trusted devices, data encryption and network protection across all infrastructural components [22], [23], [24].…”

Section: Securitymentioning

confidence: 99%

Requirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review

Stahmann

Rieger

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the rising number of sensors and interconnected devices capable of emitting data in millisecond frequencies. Streaming analytics offers promising methodologies that can support handling and analysis of data volume and variety. Transparency and control over real-time data can increase production efficiency in tightly connected machine environments. Data transparency may avoid time-consuming assessment of machines to detect anomalous machine behavior causing production inefficiencies or failures. This paper aims to identify requirements to implement streaming analytics for the detection of anomalies in Industrie 4.0 production machine groups through a structured literature review.

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Section: Securitymentioning

confidence: 99%