AKM$^2$D : An Adaptive Framework for Online Sensing and Anomaly Quantification

TL;DR

This paper introduces AKM$^2$D, an adaptive framework for online sensing that efficiently detects sparse anomalies by balancing exploration and exploitation in sequential sampling, validated through simulations and a case study.

Contribution

It presents a novel adaptive sampling framework that accelerates anomaly detection in point-based sensing systems by combining systematic exploration with focused exploitation.

Findings

Effective detection of sparse anomalies demonstrated.

Significant reduction in sensing time achieved.

Validated through simulations and real-world case study.

Abstract

In point-based sensing systems such as coordinate measuring machines (CMM) 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 in the pixel-level accurately. In this paper, 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.