Unsupervised Fault Detection using SAM with a Moving Window Approach

TL;DR

This paper introduces an unsupervised fault detection method combining the Segment Anything Model (SAM) with a moving window approach and EWMA for continuous monitoring, achieving high accuracy without labeled data.

Contribution

The novel approach integrates SAM with a moving window and clustering, enhancing fault detection accuracy and robustness without requiring fine-tuning or labeled datasets.

Findings

Achieved 0.96 accuracy on a real case study.

Attained 0.86 accuracy across open datasets.

Outperformed existing methods significantly.

Abstract

Automated f ault detection and monitoring in engineering are critical but frequently difficult owing to the necessity for collecting and labeling large amounts of defective samples . We present an unsupervised method that uses the high end Segment Anything Model (SAM) and a moving window approach. SAM has gained recognition in AI image segmentation communities for its accuracy and versatility. However, its performance can be inconsistent when dealing with certain unexpected shapes , such as shadows and subtle surface irregularities. This limitation raise s concerns about its applicability for fault detection in real world scenarios We aim to overcome these challenges without requiring fine tun ing or labeled data. Our technique divides pictures into smaller windows, which are subsequently processed using SAM. This increases the accuracy of fault identification by focusing on localized details. We compute the sizes of the segmented sections and then us e a clustering technique to discover consistent fault areas while filtering out noise. To further improve the method's robustness , we propose adding the Exponentially Weighted Moving Average (EWMA) technique for continuous monitoring in industrial settings, which would improve the method's capacity to trace faults over time. We compare our method to various well established methods u sing a real case study where our model achieve s 0.96 accuracy compared to 0. 8 5 for the second best method. W e also compare our method us ing two open source datasets where our model attains a consistent 0. 86 accuracy across the datasets compared to 0.53 and 0.54 for second best model s.