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Automatic feature selection for unsupervised clustering of cycle-based signals in manufacturing processes

Recent developments in sensing and computer technology have resulted in most manufacturing processes becoming a data-rich environment. A cycle-based signal refers to an analog or digital signal that is obtained during each repetition of an operation cycle in a manufacturing process. It is a very important class of in-process sensing signals for manufacturing processes because it contains extensive information on the process condition and product quality (e.g., the forming force signal in forging processes). In contrast with currently available supervised classification approaches that heavily depend on the training dataset or engineering field knowledge, this paper aims to develop an automatic feature selection method for the unsupervised clustering of cycle-base signals. First, principal component analysis is applied to the raw signals. Then a new method is proposed to select information containing principal components to allow clustering to be performed. The dimension of the problem can be significantly reduced through the use of these two steps. Finally, a model-based clustering method is applied to the selected principal components to find the clusters in the cycle-based signals. A numerical example and a real-world example of a forging process are used to illustrate the effectiveness of the proposed method. The proposed technique is an important data pre-processing technique for the monitoring and diagnostic system development using cycle-based signals for manufacturing processes.