Kernel Density-Based Pattern Classification in Blind Fasteners Installation
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In this work we introduce a kernel density-based pattern classification approach for the automatic identification of behavioral patterns from monitoring data related to blind fasteners installation. High density regions are estimated from feature space to establish behavioral patterns, automatically removing outliers and noisy instances in an iterative process. First the kernel density estimator is applied on the fastener features representing the quality of the installation. Then the behavioral patterns are identified from resulting high density regions, also considering the proximity between instances. Patterns are computed as the average of related monitoring torque-rotation diagrams. New fastening installations can be thus automatically classified in an online fashion. In order to show the validity of the approach, experiments have been conducted on real fastening data. Experimental results show an accurate pattern identification and classification approach, obtaining a global accuracy over \(78\%\) and improving current detection capabilities and existing evaluation systems.
KeywordsKernel density estimator Behavioral patterns Unsupervised classification Outlier detection Blind fasteners installation Machine learning
These results are part of a project that has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement 686827.
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