A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism

Autor:	Sun, Zhannan Guo, Yinlin Hao, Hanwen Shi, Zhenyu Wu, Yuhu Wu, Ximing
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	fault diagnosis convolutional block attention module deep learning long short-term memory convolutional neural network
Zdroj:	Energies; Volume 16; Issue 13; Pages: 5230
ISSN:	1996-1073
DOI:	10.3390/en16135230
Popis:	Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model’s accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=multidiscipl::07170d6f330ec73884197985a5ca21c3 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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