A clustered blueprint separable convolutional neural network with high precision for high-speed train bogie fault diagnosis

Jia, Xinming; Qin, Na; Huang, Deqing; Zhang, Yiming; Du, Jiahao

doi:10.1016/j.neucom.2022.05.056

Cited by 14 publications

(1 citation statement)

References 38 publications

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“…Deng et al adopted ensemble feature optimization to classify chemical process faults by using dynamic convolutional neural networks [10]. Jia et al designed a KMedoids clustering method based on dynamic time warping [11]. He et al Proposed an NSGAII-CNN algorithm to improve the diagnosis accuracy and efficiency for nuclear power systems [12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Hong

Tian

2023

Processes

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Machine learning for online monitoring of abnormalities in fluid catalytic cracking process (FCC) operations is crucial to the efficient processing of petroleum resources. A novel identification method is proposed in this paper to solve this problem, which combines cyclic two-step clustering analysis with a convolutional neural network (CTSC-CNN). Firstly, through correlation analysis and transfer entropy analysis, key variables are effectively selected. Then, the clustering results of abnormal conditions are subdivided by a cyclic two-step clustering (CTSC) method with excellent clustering performance. A convolutional neural network (CNN) is used to effectively identify the types of abnormal operating conditions, and the identification results are stored in the sample database. With this method, the unknown abnormal operating conditions before can be identified in time. The application of the CTSC-CNN method to the absorption stabilization system in the catalytic cracking process shows that this method has a high ability to identify abnormal operating conditions. Its use plays an important role in ensuring the safety of the actual industrial production process and reducing safety risks.

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Section: Introductionmentioning

confidence: 99%