SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

Jiahao, Yao; Jiang, Xiaoning; Wang, Shouguang; Jiang, Kelei; Yu, Xiaohan

doi:10.1109/access.2020.3034939

Cited by 16 publications

(4 citation statements)

References 35 publications

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“…In theory, the DNN can extract more advanced features. However, for the design of a high-performance identification network, which often needs tens or hundreds of layers, the gradient vanity problem has become a problem in DNN training [25][26][27][28][29][30].…”

Section: Lstm Cellmentioning

confidence: 99%

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

Xie,

Shi,

Xue

et al. 2024

JMSE

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The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems.

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Section: Lstm Cellmentioning

confidence: 99%

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

Xie,

Shi,

Xue

et al. 2024

JMSE

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“…The time interval is very short, and each process is like monopolizing the processor. Among the aforementioned common operating systems, Linux is a time-sharing operating system, and VxWorks, uC/OS-II, and RTLinux are real-time operating systems [24].…”

Section: Overview Of Embedded Systemsmentioning

confidence: 99%

Research on concentration detection algorithm of embedded gas detection system based on deep learning

Sun

2023

Preprint

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In daily life, whether it is the house where people live or the car they drive, air pollution can be seen almost everywhere, and human life and health are seriously threatened. There is an urgent need for a detection system for fast and accurate detection and assessment of ambient air quality. On the other hand, in the medical field, the detection of human exhaled gas can realize non-invasive and rapid early disease screening, but to achieve this purpose, a fast, accurate, highly sensitive and selective gas detection system is also required. At present, the research and development of gas detection system has attracted more and more attention of scholars, and the gas detection technology using porphyrin sensor array (PSA) chip is a hot topic in this research direction in recent years. Compared with traditional methods, this method has many obvious advantages and can meet the needs of fast, accurate and sensitive detection. In this paper, a PSA chip is used as a gas sensor, a set of PSA image acquisition and processing software is designed and developed on the Linux platform, and successfully transplanted to the ARM platform to build a complete gas detection system. Next, the data was collected and processed with ammonia gas as the detection object, and the quantitative analysis model of gas concentration was studied.

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“…Jiahao. Y and et al [110] To suggested a method for detecting faults in the GS-IoT system using a combination of SVM and BiLSTM by analyzing the OM and ES communication to emotional expression. The Author evaluated the data in real-time and identified the fault forms using a dynamic approach.…”

Section: Fatayer T and Azara M [109] Classification Ofmentioning

confidence: 99%

A State of the Art Survey of Machine Learning Algorithms for IoT Security

Jahwar

Zeebaree

2021

AJRCoS

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The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

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SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

Cited by 16 publications

References 35 publications

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

Research on concentration detection algorithm of embedded gas detection system based on deep learning

A State of the Art Survey of Machine Learning Algorithms for IoT Security

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