Image anomaly detection benchmark in industrial manufacturing

Pipeline training and inference Anomalib models UI in Anomaly Detection

Repository linked to "Anomaly detection in Smart-manufacturing era: A review"

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

Anomaly Detection in Additive Manufacturing using Deep Learning

Developed a method for estimating in-control distribution parameters on a dataset with 209 attributes to carry out the Phase-II analysis and to detect out of control data for future observations .Leveraged principal component analysis to reduce number of attributes to 4 which explained 80% variation of dataset .Utilized m-CUSUM, T2 and m-EWMA control charts and their combinations to analyze out of control & in-control data.

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End-to-end anomaly detection platform featuring automated ML pipelines, Kubernetes deployment, MLFlow integration, and web app for real-time visual inspection and data collection in manufacturing environments.

This project implements a Vector Quantized Variational Autoencoder (VQ-VAE) model for automated defect detection in product images, leveraging the power of unsupervised learning to identify anomalies in industrial and manufacturing workflows.

The PCB Anomaly Detection System automates defect identification in printed circuit boards (PCBs) during manufacturing, enhancing quality control and efficiency. High-resolution images are captured and preprocessed, and machine learning models detect anomalies like shorts and missing components.

This project aims to predict certain properties of a multi-stage continuous-flow manufacturing process. The goal is to develop predictive models that can be used in real-time production environments for tasks such as process control and anomaly detection.

This project demonstrates an end-to-end anomaly detection pipeline for IoT sensor data using both Isolation Forest (Unsupervised Machine Learning) and LSTM Autoencoder (Deep Learning – PyTorch). The goal is to identify unusual temperature readings that may indicate equipment malfunction or maintenance needs in a manufacturing environment.

We have publicly released our source codes and benchmark data to enable others reproduce our work. In particular, we are publicly releasing, with this submission, our smart manufacturing database corpus of 4 datasets. This resource will encourage the community to standardize efforts at benchmarking anomaly detection in this important domain. We also encourage the community to expand this resource by contributing their new datasets and models.

Deep learning is a part of machine learning and is well applied in many areas with messy and unstructured data. The inspection in industries manufacturing is an essential requirement because outlier or anomaly can be detected on product manufacturing. Done in the efficient way, that mean by introducing the machine learning and deep learning in the process of anomaly detection, the productivity and the quality of the product can be improved. In this project we performed therefore the Convolutional autoencoder and the semantic segmentation methods like U-Net and FCN and we compared them with each other to see which one is the most performantsuch as pictures. On this project, we applied

anomaly detection in real life time series sensor data

Anomaly detection in manufacturing video streams using CNNs

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Advanced Quality Control

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Leveraged pre-trained ResNet-50 to extract rich, discriminative hierarchical features, enabling the effective detection of anomaly region to automatically detect products with defects.

Anomaly detection in pharmaceutical manufacturing using EDA and unsupervised machine learning on multivariate time series data.

An event handler API written in Django for anomaly detection in manufacturing pipelines. Completed under the observance of the Secure Systems and Architectures Lab @ UCLA.

Real-time defect detection system using OpenCV and ML anomaly modeling to identify scratches, color tints, and misalignments in manufacturing images with 90%+ accuracy.

End-to-end project to forecast production & power consumption and detect anomalies in manufacturing data. Includes EDA, feature engineering, modeling, anomaly detection, and a FastAPI app for serving forecasts and alerts.

Major Project - Industry 4.0 tinyML Anomaly Detection and prevention. Using embedded systems and machine learning algorithm. Effective for machines used in manufacturing plants.

AI-driven industrial monitoring system for real-time anomaly detection and fault prediction in chemical manufacturing processes using XGBoost, Random Forest, and Isolation Forest models.

This project evaluates three anomaly detection methods on two wafer datasets (Wafer 1 and Wafer 2) under realistic manufacturing constraints. The emphasis is **not headline accuracy**, but model behavior, failure modes, and anomaly detection effectiveness in highly imbalanced data.

Active Learning Workshop where your team will develop a Predictive Maintenance Dashboard application, the visibility tool of an Anomaly Detection and response management workflow in a manufacturing facility.

Universal AI Quality Inspector: An automated industrial defect detection system leveraging State-of-the-Art Generative AI to identify manufacturing anomalies like rust, fractures, and deformations in real-time