A Contrast-Based Approach to the Identification of Texture Faults

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max-min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max -min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

Automation of painted slate inspection

This thesis is concerned with the problem of how to detect visual defects on painted slates using an automated visual inspection system. The vision system that has been developed consists of two major components. The first component addresses issues such as the mechanical implementation and interfacing the inspection system with the optical and sensing equipment whereas the second component involves the development of an image processing algorithm able to identify the visual defects present on the slate surface. The visual defects can be roughly classified into two distinct categories. In this way, substrate faults occur when the slate is not fully formed or has excess material whilst paint faults describe a slate of uneven colour or gloss level. A key element in successfully imaging the slate surface defects is the illumination set-up. After extensive testing, an effective collimated lighting topology was selected and is described in detail. Imaging the slate surface was challenging because it is dark coloured, glossy and has depth profile non-uniformities. A four component image processing algorithm was designed to detect the range of defect types. The constituent components are global mean threshold, adaptive signal threshold, labelling, edge detection and labelling. Having proven a solution on the laboratory test bed, a prototype conveyor-based inspection system was assembled in order to replicate a factory-style environment. Robustness tests were performed on 400 slates and a 97% success rate was achieved. This thesis is concluded with a discussion on the feasibility of progressing this project to installation on an automated production line

KD-ART: Should we intensify or diversify tests to kill mutants?

CONTEXT: Adaptive Random Testing (ART) spreads test cases evenly over the input domain. Yet once a fault is found, decisions must be made to diversify or intensify subsequent inputs. Diversification employs a wide range of tests to increase the chances of finding new faults. Intensification selects test inputs similar to those previously shown to be successful. OBJECTIVE: Explore the trade-off between diversification and intensification to kill mutants. METHOD: We augment Adaptive Random Testing (ART) to estimate the Kernel Density (KD–ART) of input values found to kill mutants. KD–ART was first proposed at the 10th International Workshop on Mutation Analysis. We now extend this work to handle real world non numeric applications. Specifically we incorporate a technique to support programs with input parameters that have composite data types (such as arrays and structs). RESULTS: Intensification is the most effective strategy for the numerical programs (it achieves 8.5% higher mutation score than ART). By contrast, diversification seems more effective for programs with composite inputs. KD–ART kills mutants 15.4 times faster than ART. CONCLUSION: Intensify tests for numerical types, but diversify them for composite types

Computational Analysis of Neutron Scattering Data

This work explores potential methods for use in the detection and classification of defects within crystal structures via analysis of diffuse scattering data generated by single crystal neutron scattering experiments. The proposed defect detection methodology uses machine learning and image processing techniques to perform image texture analysis on neutron diffraction patterns generated by neutron scattering simulations. Once the methodology is presented, it is tested via a series of defect detection problems of increasing difficulty which utilize neutron scattering data simulated by a number of simulation techniques. As the problem difficulty is increased, the defect detection methodology is refined in order to adapt to challenges presented by the more difficult detection problems. The refinement process includes the development of a data-driven scaling method that aids in the texture analysis process by enhancing diffuse scattering textures in the diffraction patterns. The evaluation process for the defect detection methodology includes analysis and comparison of the computational complexities of the machine learning and image processing techniques. As part of this complexity analysis, a detailed study of the ORB keypoint extraction algorithm is also conducted and the computational complexity of the ORB algorithm is derived