On the Performance of Machine Learning at the Network Edge to Detect Industrial IoT Faults

Santo, Yuri; Dalmazo, Bruno L.; Immich, Roger; Riker, Andre

doi:10.1109/nca57778.2022.10013585

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…In this framework, edge computing provides fast processing of machine learning classifiers. Based on the study performed in our previous work [ 4 ], DASIF relies on a two-tier machine learning for precise and fast IoT fault detection. The fault classification is double-checked using a decision tree and Gaussian naive Bayes.…”

Section: Detection and Alert State For Industrial Internet Of Things ...mentioning

confidence: 99%

“…Under this state, the network decreases the communication interval and uses data replication and multiple-path communication to achieve higher communication reliability. In our previous work [ 4 ], we conducted a study to select the best ML models for the DASIF framework. In [ 4 ], we evaluated six machine learning classifiers measuring accuracy, precision, recall, F1 score, training time, and response time.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [ 4 ], we conducted a study to select the best ML models for the DASIF framework. In [ 4 ], we evaluated six machine learning classifiers measuring accuracy, precision, recall, F1 score, training time, and response time.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Santo

Immich

Dalmazo

et al. 2023

Sensors

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Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.

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Section: Detection and Alert State For Industrial Internet Of Things ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Santo

Immich

Dalmazo

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

On the Performance of Machine Learning at the Network Edge to Detect Industrial IoT Faults

Cited by 1 publication

References 15 publications

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

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