Design and Stage Analysis of a Non-Invasive Monitoring System for Determining Formaldehyde Release From Wood-Based Panels by the Chamber Method

• Published in: IEEE access Vol. 12; pp. 137952 - 137969
• Main Authors: Ye, Jinfei, Lu, Yang, Wang, Kuokuo, Jiao, Qingchun, Chen, Xuwen, Wang, Lijun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air monitoring; Air quality; Algorithms; BTSVM; chamber method; Chambers; Cluster analysis; Clustering; Data analysis; Environmental monitoring; Formaldehyde; FRWBP; k-means; non-invasive; Noninvasive treatment; Panels; PCA; Principal component analysis; Principal components analysis; Real time; Sensor phenomena and characterization; Support vector machines; Temperature measurement; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3455375

With the extensive application of wood-based panels in the home decor industry, determining formaldehyde release form wood-based panels (FRWBP) is crucial for ensuring environmental air quality and human health. This article addresses the issues of insufficient monitoring of the testing stages for FRWBP and the difficulty in tracing the operation records of operator during determining FRWBP by the chamber method. It proposes a system for monitoring and analyzing the testing stages of FRWBP based on electrical monitoring. The system employs non-invasive sensing technology that does not interfere with normal testing process to collect real-time electrical usage data from the devices. The data is uploaded to an industrial computer for storage and processing. Statistical features of the electrical time series data are extracted using a sliding window approach and optimized with Principal Component Analysis (PCA). Subsequently, the binary tree support vector machine algorithm (BTSVM) optimized by k-means clustering algorithm is used to identify the detection states of the wood-based panels. Experimental results show that the system can achieve traceability of the formaldehyde detection stage records of artificial boards, and the designed algorithm model can also effectively identify the detection status of wood-based panels. The system and methods proposed in this paper have significant prospects for implementation in monitoring and traceability management within the testing industry.