Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

Chih-Hung Jen,Chien-Chih Wang

doi:10.3390/pr9091510

Chih-Hung Jen, Chien-Chih Wang

Open Access

https://doi.org/10.3390/pr9091510

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Abstract

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.

Highlights

Accepted: 24 August 2021Network technology, cloud computing, and big data analysis are being gradually integrated with industrial automation in a digital transformation known as Industry 4.0.For example, the Internet of Things can be used to develop a smart monitoring system to enhance the transparency and automate the operation of a factory
The focus of this study was the construction of a real-time monitoring system that can quickly identify abnormalities based on a sound frequency signal
The reconstructed signals were converted into multiscale entropy (MSE) profiles using EquaTable 1 presents the fitting results of the different models to the signals, which were tions (9) and (10)

Summary

Introduction

Accepted: 24 August 2021Network technology, cloud computing, and big data analysis are being gradually integrated with industrial automation in a digital transformation known as Industry 4.0.For example, the Internet of Things can be used to develop a smart monitoring system to enhance the transparency and automate the operation of a factory. Cloud computing, and big data analysis are being gradually integrated with industrial automation in a digital transformation known as Industry 4.0. In the field of process monitoring, fault detection and diagnosis (FDD) is focused on detecting abnormal situations, done through modeling, signal processing, and intelligence computation. The FDD methods can generally be classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods [1]. Yan et al proposed a hybrid method to detect faults for chiller subsystems, only using the normal data to implement the training procedure. This online monitoring framework was constructed to use an extended Kalman filter (EKF)

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Conclusion