AKM2D: An adaptive framework for online sensing and anomaly quantification. (1st September 2020)

• Record Type: Journal Article
• Authors: Yan, Hao; Paynabar, Kamran; Shi, Jianjun
• Is Part Of: IISE transactions. Volume 52:Number 9(2020)
• Page Start: 1032
• Page End: 1046
• Publication Date: 2020-09-01
• Subjects: Adaptive sampling -- space-filling design -- exploration and exploitation -- kernel methods -- anomaly quantification; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2019.1681606
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: In point-based sensing systems such as coordinate measuring machines and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification. Most of the existing sequential sampling methodologies focus on reducing the overall fitting error for the entire sampling space. However, in many anomaly quantification applications, the main goal is to estimate sparse anomalous regions at pixel-level accurately. In this article, we develop a novel framework named Adaptive Kernelized Maximum-Minimum Distance ( AKM 2 D ) to speed up the inspection and anomaly detection process through an intelligent sequential sampling scheme integrated with fast estimation and detection. The proposed method balances the sampling efforts between the space-filling sampling (exploration) and focused sampling near the anomalous region (exploitation). The proposed methodology is validated by conducting simulations and a case study of anomaly detection in composite sheets using a guided wave test.