An early warning method of abnormal state of laboratory equipment based on Internet of things and running big data

Article type: Research Article

Authors: Zheng, Guokai^{; *} | Yi, Luxia

Affiliations: Guangzhou College of Technology and Business, Guangzhou 510850, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In order to improve the early warning effect of equipment abnormal state and shorten the early warning time, this paper designs an early warning method of laboratory equipment abnormal state based on the Internet of things and running big data. Collect the running status data of laboratory equipment in the environment of Internet of things, and implement dimension reduction processing on the collected running status data. After the dimensionality reduction, extract the abnormal characteristics of big data of laboratory equipment running. On the basis of iterative update, the real-time feature analysis results are compared with the abnormal feature set, and the early warning response program is started according to the abnormal. According to the experimental results, the maximum false alarm rate of this method is only 1.34%, and the abnormal state response is always kept below 4.0 s when applied, which fully proves that this method effectively realizes the design expectation.

Keywords: Internet of things, equipment running, big data collection, dimension reduction treatment, feature extraction, abnormal state warning

DOI: 10.3233/WEB-220052

Journal: Web Intelligence, vol. 21, no. 3, pp. 261-270, 2023