In modern manufacturing systems, online sensing has been increasingly used for process monitoring and fault diagnosis. In many practical situations, the output of the sensing system are represented by time ordered data know as “profiles” and “waveform signals”. Most of previous works dealt with cases, in which the production process was characterized by single profiles. In some industrial practices, however, the online sensing system is so designed that it records more than one profile at each operation cycle. For example, in multi-operation forging processes with transfer or progressive dies, in order to measure the tonnage force exerted on dies, four sensors are used. To effectively analyze multi-channel profiles, it is crucial to develop a method that considers the information of inter-relationship among different profile channels. There is little research in the literature on analyzing multi-channel profiles. In this paper, for the purpose of monitoring and fault diagnosis, we propose a method for analyzing multi-channel profile based on uncorrelated multilinear principal component analysis. We show the effectiveness of the proposed method by using a case study on a multi-operation forging process.
Multilinear Principal Component Analysis for Multichannel Nonlinear Signals
PACELLA, Massimo
2012-01-01
Abstract
In modern manufacturing systems, online sensing has been increasingly used for process monitoring and fault diagnosis. In many practical situations, the output of the sensing system are represented by time ordered data know as “profiles” and “waveform signals”. Most of previous works dealt with cases, in which the production process was characterized by single profiles. In some industrial practices, however, the online sensing system is so designed that it records more than one profile at each operation cycle. For example, in multi-operation forging processes with transfer or progressive dies, in order to measure the tonnage force exerted on dies, four sensors are used. To effectively analyze multi-channel profiles, it is crucial to develop a method that considers the information of inter-relationship among different profile channels. There is little research in the literature on analyzing multi-channel profiles. In this paper, for the purpose of monitoring and fault diagnosis, we propose a method for analyzing multi-channel profile based on uncorrelated multilinear principal component analysis. We show the effectiveness of the proposed method by using a case study on a multi-operation forging process.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
