# Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering

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## Abstract

Various types of abnormal control chart patterns can be linked to certain assignable causes in industrial processes. Hence, control chart patterns recognition methods are crucial in identifying process malfunctioning and source of variations. Recently, the hybrid soft computing methods have been implemented to achieve high recognition accuracy. These hybrid methods are complicated, because they require optimizing algorithms. This paper investigates the design of efficient hybrid recognition method for widely investigated eight types of *X*-bar control chart patterns. The proposed method includes two main parts: the features selection and extraction part and the recognizer design part. In the features selection and extraction part, eight statistical features are proposed as an effective representation of the patterns. In the recognizer design part, an adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy c-mean (FCM) is proposed. Results indicate that the proposed hybrid method (FCM-ANFIS) has a smaller set of features and compact recognizer design without the need of optimizing algorithm. Furthermore, computational results have achieved 99.82% recognition accuracy which is comparable to published results in the literature.

## Keywords

Control chart patterns recognition Fuzzy clustering ANFIS Statistical features## Notes

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest. Also, this research work has not been submitted for publication nor has it been published in part or in whole elsewhere. We verify to the fact that all authors listed on the title page have contributed significantly to the work, have read the document, confirm to the validity and legitimacy of the data and its explanation, and agree to its submission to the Journal of Neural Computing and Applications.

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