A novel autonomous machine learning technique for recognizing control chart patterns

Abstract

A number of strategies have been developed for controlling and monitoring the production process since the quality of the product has emerged as one of the key concerns in today's manufacturing sector. Control charts are the best tools for monitoring and adjusting products and processes. This research proposes a novel automatic method for the recognition of nine control chart patterns (CCPs) based on novel autonomous machine learning. The classification portion and the tuning portion make up the two main components of this procedure. Support Vector Machine (SVM) have demonstrated outstanding performance over the past few years on a variety of applications, including signal dispensation, speech detection, and image processing. SVM is consequently employed as the intelligent classifier for the recognition of CCPs in the classification phase. One key challenge with SVM is that it requires a high level of expertise to choose appropriate parameters, such as the quantity of kernel and their spatial diameters, knowledge rate, etc. It is difficult to fine-tune the SVM parameters because of their domestic dependence. These problems led to the employment of the Harmony Search (HS) Algorithm for the best tuning of SVM parameters in the tuning section of the proposed technique. Instead of depending on any feature engineering procedures, the suggested method, in contrast to the popular CCPs recognition methods, takes raw data and runs it through many hidden layers to obtain the best feature representation. The quantitative and simulation results demonstrate the suggested method's performance advantage over the earlier methods.