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.
Automated melt electrowritting platform with real-time process monitoring
