High dimensional data analysis for anomaly detection and quality improvement

Analysis of large-scale high-dimensional data with a complex heterogeneous data structure to extract information or useful features is vital for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. Chapter 3 focuses on detecting anomalies from high-dimensional data. Traditionally, most of the image-based anomaly detection methods perform denoising and detection sequentially, which affects detection accuracy and efficiency. In this chapter, A novel methodology, named smooth-sparse decomposition (SSD), is proposed to exploit regularized high-dimensional regression to decompose an image and separate anomalous regions simultaneously by solving a large-scale optimization problem. Chapter 4 extends this to spatial-temporal functional data by extending SSD to spatiotemporal smooth-sparse decomposition (ST-SSD), with a likelihood ratio test to detect the time of change accurately based on the detected anomaly. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are also developed. The proposed methodology is also applied to tonnage signals, rolling inspection data and solar flare monitoring. Chapter 5 considers the adaptive sampling problem for high-dimensional data. A novel adaptive sampling framework, named Adaptive Kernelized Maximum-Minimum Distance is proposed to adaptively estimate the sparse anomalous region. 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 also applied to a case study of anomaly detection in composite sheets using a guided wave test. Chapter 6 explores the penalized tensor regression to model the tensor response data with the process variables. Regularized Tucker decomposition and regularized tensor regression methods are developed, which model the structured point cloud data as tensors and link the point cloud data with the process variables. The performance of the proposed method is evaluated through simulation and a real case study of turning process optimization.Ph.D

Modeling, monitoring, and diagnosis of complex systems with high-dimensional streaming data

With the development of technology, sensing systems became ubiquitous. As a result, a wide variety of complex systems are continuously monitored by hundreds of sensors collecting large volumes of rich data. Learning the structure of complex systems, from sensing data, provides unique opportunities for real-time process monitoring and for accurate fault diagnosis in a wide range of applications. This dissertation presents new methodologies to analyze the high-dimensional data collected by sensors to learn the interactions between different entities in complex systems for system monitoring and diagnosis.Ph.D

Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection

abstract: Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.Dissertation/ThesisMasters Thesis Industrial Engineering 201