Abstract
Process monitoring is an important research problem in numerous areas. This paper proposes a novel process monitoring scheme by integrating the recurrence plot (RP) method and the control chart technique. Recently, the RP method has emerged as an effective tool to analyze waveform signals. However, unlike the existing RP methods that employ recurrence quantification analysis (RQA) to quantify the recurrence plot by a few summary statistics; we propose new concepts of template recurrence plots and continuous-scale recurrence plots to characterize the waveform signals. A new feature extraction method is developed based on continuous-scale recurrence plot. Then, a monitoring statistic based on the top- approach is constructed from the continuous-scale recurrence plot. Finally, a bootstrap control chart is built to detect the signal changes based on the constructed monitoring statistics. The comprehensive simulation studies show that the proposed monitoring scheme outperforms other RQA-based control charts. In addition, a real case study of progressive stamping processes is implemented to further evaluate the performance of the proposed scheme for process monitoring.
Highlights
To improve product quality, system safety and reliability, advanced methods of process monitoring and fault detection become increasingly important in many manufacturing processes
In order to overcome this limitation, we propose a novel process monitoring scheme to directly monitor the changes in the recurrence plot (RP) plot instead of using the recurrence quantification analysis (RQA) features by integrating with control chart technique for process monitoring
Jones and Woodall [29] have provided a comprehensive investigation of several bootstrap control charts and compared their performance, which shows that the bootstrap method can significantly improve the performance of the control chart technique when the monitoring statistics do not satisfy the assumption of following the normal distribution
Summary
System safety and reliability, advanced methods of process monitoring and fault detection become increasingly important in many manufacturing processes. Williams et al [9] proposed to monitor the dose-response profiles by developing a four-parameter logistic regression model These approaches are parametric with an assumption that the profile data should follow a specified functional form. Develop a nonparametric approach for linear system identification It assumes that the profile data under a specific process condition follow a multivariate normal distribution when using the principal component analysis method. Zhou and Zhang [21] developed a bootstrap control chart based on the recurrence quantification measures to monitor vibration signals This method did not consider the correlation between different signals. Since the RP method has several advantages to analyze the nonlinear profile data, our proposed process monitoring scheme does not make any assumption on the functional form or characteristics of the profiles data.
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