Identification of patterns in control charts for processes with statistically correlated noise

Abstract

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A control chart pattern recognition system for feedback-control processes

“…Two sets of 5600 patterns, 800 of each type, were generated; one of the sets was produced using the methodology proposed by De la Torre Gutiérrez & Pham (2018), and the other following the PGS adopted in most of the CCPR literature Pham & Oztemel, 1996). Each synthesised pattern consisted of a random sequence of length n=60, sampled at time t 1 , t 2 ,....,t 60 .…”

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

“…Disturbances contribute to the presence of systematic control chart patterns (CCPs) for a process. The recognition of CCPs is very important since CCPs are typically associated with certain root causes that antagonistically influence the process [3][4][5][6].…”

“…Traditional SPC charts presume that the process outputs should be independent, which is not a practical consideration for real-world applications [21][22][23]. Correlated process outputs cause increases in false alarm signals and misinterpretation of the function of SPC charts [4,18]. In fact, correlation broadly exists in continuous and chemical processes [4,24,25].…”

“…Correlated process outputs cause increases in false alarm signals and misinterpretation of the function of SPC charts [4,18]. In fact, correlation broadly exists in continuous and chemical processes [4,24,25]. The integration of EPC and SPC mechanisms is commonly used to overcome this independence difficulty for SPC applications [18,20,26,27].…”

“…In [4], the systems were represented as an AR(1), a moving average (MA(1)) or an autoregressive moving average (ARMA(1, 1)) model. The effect of autocorrelation on the pattern recognition capabilities was investigated.…”