Failure Detection in Assembly: Force Signature Analysis

Alberto Rodriguez, David Bourne, Matthew T. Mason, Gregory F. Rossano and JianJun Wang
Conference Paper, IEEE Conference on Automation Science and Engineering (CASE 2010), August, 2010

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This paper addresses failure detection in automated parts assembly, using the force signature captured during the contact phase of the assembly process. We use a supervised learning approach, specifically a Support Vector Machine (SVM), to distinguish between successful and failed assemblies. This paper describes our implementation and experimental results obtained with an electronic assembly application. We also analyze the tradeoff between system accuracy and number of training examples. We show that a less expensive sensor (a single-axis load cell instead of a six-axis force/torque sensor) provides enough information to detect failure. Finally, we use Principal Component Analysis (PCA) to compress the force signature and as a result reduce the number of examples required to train the system.

author = {Alberto Rodriguez and David Bourne and Matthew T. Mason and Gregory F. Rossano and JianJun Wang},
title = {Failure Detection in Assembly: Force Signature Analysis},
booktitle = {IEEE Conference on Automation Science and Engineering (CASE 2010)},
year = {2010},
month = {August},
keywords = {assembly, force signature, signature analysis, failure detection, SVM, PCA},
} 2017-09-13T10:40:38-04:00