Universiti Teknologi Malaysia Institutional Repository

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

Zaman, Munawar and Hassan, Adnan (2019) Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering. Neural Computing and Applications, 31 (10). pp. 5935-5949. ISSN 0941-0643

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Official URL: http://dx.doi.org/10.1007/s00521-018-3388-2


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.

Item Type:Article
Uncontrolled Keywords:ANFIS, Fuzzy clustering
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:88492
Deposited By: Widya Wahid
Deposited On:15 Dec 2020 08:07
Last Modified:15 Dec 2020 08:07

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