Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis

Abstract

In modern manufacturing systems, online sensing is being increasingly used for process monitoring and fault diagnosis. In many practical situations, the output of the sensing system is represented by time-ordered data known as profiles or waveform signals. Most of the work reported in the literature has dealt with cases in which the production process is characterized by single profiles. In some industrial practices, however, the online sensing system is designed so that it records more than one profile at each operation cycle. For example, in multi-operation forging processes with transfer or progressive dies, four sensors are used to measure the tonnage force exerted on dies. To effectively analyze multichannel profiles, it is crucial to develop a method that considers the interrelationships between different profile channels. A method for analyzing multichannel profiles based on uncorrelated multilinear principal component analysis is proposed in this article for the purpose of characterizing process variations, fault detection, and fault diagnosis. The effectiveness of the proposed method is demonstrated by using simulations and a case study on a multi-operation forging process.

Keywords:

• Classification
• dimension reduction
• principal component analysis
• tensor-to-vector projection

Acknowledgements

This research is jointly supported by Fullbright Scholarship, Forging Industrial Educational & Research Foundation, and NSF Grant DMI F031508. The authors would also thank OG Technologies and FormTech for their help and support on experimental tests and data collection, and thank the editor and referees for their insightful comments.