LAPSE:2023.5085
Published Article
LAPSE:2023.5085
Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases
February 23, 2023
Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.
Record ID
Keywords
empirical mode decomposition, intrinsic mode functions, linear discriminant analysis, statistical process control
Subject
Suggested Citation
Jen CH, Wang CC. Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases. (2023). LAPSE:2023.5085
Author Affiliations
Journal Name
Processes
Volume
9
Issue
9
First Page
1510
Year
2021
Publication Date
2021-08-26
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9091510, Publication Type: Journal Article
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Published Article
LAPSE:2023.5085
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doi:10.3390/pr9091510
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[v1] (Original Submission)
Feb 23, 2023
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Feb 23, 2023
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