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.

This paper introduces TRACE-GPT, which stands for Time-seRies Anomaly-detection with Convolutional Embedding and Generative Pre-trained Transformers. TRACE-GPT is designed to pre-train univariate time-series sensor data and detect faults on unlabeled datasets in semiconductor manufacturing. In semiconductor industry, classifying abnormal time-series sensor data from normal data is important because it is directly related to wafer defect. However, small, unlabeled, and even mixed training data without enough anomalies make classification tasks difficult. In this research, we capture features of time-series data with temporal convolutional embedding and Generative Pre-trained Transformer (GPT) to classify abnormal sequences from normal sequences using cross entropy loss. We prove that our model shows better performance than previous unsupervised models with both an open dataset, the University of California Riverside (UCR) time-series classification archive, and the process log of our Chemical Vapor Deposition (CVD) equipment. Our model has the highest F1 score at Equal Error Rate (EER) across all datasets and is only 0.026 below the supervised state-of-the-art baseline on the open dataset.