EH-Edge--An Energy Harvesting-Driven Edge IoT Platform for Online Failure Prediction of Rail Transit Vehicles: A case study of a cloud, edge, and end device collaborative computing paradigm

Yang, Dong; Cui, Enfang; Wang, Hongchao; Zhang, Hongke

doi:10.1109/mvt.2021.3053193

Cited by 4 publications

(3 citation statements)

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“…In addition, The hybrid method in AI proves to be more accurate than other propose models [8,23,27,29]. This approach leverages the strengths of multiple algorithms, resulting in better performance, scalability and accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…• Heterogeneous ensemble methods: which use different types of base models like CNN-LSTM [29], CNN-Bi-LSTM [8], FO-SVM [27].…”

Section: Resultsmentioning

confidence: 99%

“…It enhances manufacturing by real-time monitoring and predictive maintenance, thereby improving efficiency, reducing costs, and increasing safety in various industries [2,4]. IoT's application spans sectors such as smart farming [5,6], industry [3], smart healthcare [7], transportation [8], energy, and utilities [9]. Failure prediction is central to proactive maintenance, aiming to anticipate and prevent future failures in machinery and components.…”

Section: Internet Of Things and Failure Prediction In Proactive Maint...mentioning

confidence: 99%

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A survey on AI Approaches for Internet of Things Devices Failure Prediction

Khattach,

Moussaoui,

Hassine

2023

E3S Web of Conf.

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The use of Internet of Things (IoT) devices has experienced a substantial surge in various sectors, including manufacturing, healthcare, agriculture, and transportation. Nonetheless, the susceptibility of these devices to failures has emerged as a significant concern, contributing to costly periods of inactivity and diminished productivity. Consequently, the development of sophisticated and precise techniques for forecasting device failures in advance has become imperative. This research paper thoroughly investigates and analyses the most recent advancements and scholarly inquiries pertaining to the implementation of artificial intelligence methodologies, notably machine learning and deep learning, in the realm of predicting and averting IoT device failures. These AI-based approaches can be trained on extensive historical datasets, enabling the detection of distinctive patterns and anomalies that serve as potential precursors to device malfunctions. By incorporating these innovative failure prediction techniques into their operations, organizations can actively identify and address potential issues, thereby minimizing the adverse repercussions of device failures on their overall performance and functionality.

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Section: Resultsmentioning

confidence: 99%

“…• Heterogeneous ensemble methods: which use different types of base models like CNN-LSTM [29], CNN-Bi-LSTM [8], FO-SVM [27].…”

Section: Resultsmentioning

confidence: 99%