  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.
