Traceability and analysis method for measurement laboratory testing data based on intelligent Internet of Things and deep belief network

A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of...

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Veröffentlicht in:Journal of intelligent systems Jg. 33; H. 1; S. 6821 - 30
Hauptverfasser: He, PeiDong, Li, XiaoJun, Shen, WenQi, Deng, ShuYu, Xiao, Li, Zhang, Yang Fan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: De Gruyter 04.10.2024
Schlagworte:
abnormal analysis
cloud-management-edge-end
cosine similarity
data traceability
deep belief network
gaussian Bernoulli restricted Boltzmann machine
intelligent IoT
metrology laboratory
stacked denoising autoencoder model
ISSN:2191-026X, 2191-026X
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Zusammenfassung:A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of anomalies. First, a data analysis system for the metrology laboratory is designed based on an intelligent IoT architecture of “cloud-management-edge-end.” Then, the Gaussian Bernoulli-Restricted Boltzmann machine is introduced to improve the DBN model, which is deployed on the edge side for learning the ledger data sample library to determine the anomaly detection data of the metrology device. Finally, a stacked denoising autoencoder model is used in the cloud center to extract historical electricity consumption curve features, and the cause of anomalies is determined by calculating the cosine similarity between it and the target device feature curve to complete traceability analysis. Based on the selected dataset, the proposed method is experimentally demonstrated, and the results show that its traceability accuracy and time consumption are 88.72% and 3.949 s, respectively, which can meet the detection requirements of the metrology laboratory.
ISSN:2191-026X
2191-026X
DOI:10.1515/jisys-2024-0076