An intelligent data capturing framework to improve condition monitoring and anomaly detection for industrial machines

Robyns, Steven; Helsen, Stijn; Weckx, Sam; Bhoi, Sachin Kumar; Baghdadi, Mohamed El; Hegazy, Omar; Smet, Jasper De

doi:10.1016/j.procs.2022.12.267

Cited by 4 publications

(1 citation statement)

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“…However, they do not involve integration with Microsoft Azure Cloud and IoTEdge technology specifically. Paper [75] describes a framework for data capturing and processing in the cloud but without Azure IoT Edge technology. Therefore, the literature lacks publications that characterize commercial systems for anomaly detection for hydraulic power units with the use of Azure cloud and Azure IoT Edge.…”

Section: Algorithmsmentioning

confidence: 99%

Anomaly Detection for Hydraulic Power Units—A Case Study

Fic,

Czornik,

Rosikowski

2023

Future Internet

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This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow.

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Section: Algorithmsmentioning

confidence: 99%