A Framework for Prognostics Reasoning

The use of system data to make predictions about the future system state commonly known as prognostics is a rapidly developing field. Prognostics seeks to build on current diagnostic equipment capabilities for its predictive capability. Many military systems including the Joint Strike Fighter (JSF) are planning to include on-board prognostics systems to enhance system supportability and affordability. Current research efforts supporting these developments tend to focus on developing a prognostic tool for one specific system component. This dissertation research presents a comprehensive literature review of these developing research efforts. It also develops presents a mathematical model for the optimum allocation of prognostics sensors and their associated classifiers on a given system and all of its components. The model assumptions about system criticality are consistent with current industrial philosophies. This research also develops methodologies for combine sensor classifiers to allow for the selection of the best sensor ensemble

Bagging of complementary neural networks with double dynamic weight averaging

Ensemble technique has been widely applied in regression problems. This paper proposes a novel approach of the ensemble of Complementary Neural Network (CMTNN) using double dynamic weight averaging. In order to enhance the diversity in the ensemble, different training datasets created based on bagging technique are applied to an ensemble of pairs of feed-forward back-propagation neural networks created to predict the level of truth and falsity values. In order to obtain more accuracy, uncertainties in the prediction of truth and falsity values are used to weight the prediction results in two steps. In the first step, the weight is used to average the truth and the falsity values whereas the weight in the second step is used to calculate the final regression output. The proposed approach has been tested with benchmarking UCI data sets. The results derived from our technique improve the prediction performance while compared to the traditional ensemble of neural networks which is predicted based on only the truth values. Furthermore, the obtained results from our novel approach outperform the results from the existing ensemble of complementary neural network

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared

Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

Realistic adversarial machine learning to improve network intrusion detection

Modern organizations can significantly benefit from the use of Artificial Intelligence (AI), and more specifically Machine Learning (ML), to tackle the growing number and increasing sophistication of cyber-attacks targeting their business processes. However, there are several technological and ethical challenges that undermine the trustworthiness of AI. One of the main challenges is the lack of robustness, which is an essential property to ensure that ML is used in a secure way. Improving robustness is no easy task because ML is inherently susceptible to adversarial examples: data samples with subtle perturbations that cause unexpected behaviors in ML models. ML engineers and security practitioners still lack the knowledge and tools to prevent such disruptions, so adversarial examples pose a major threat to ML and to the intelligent Network Intrusion Detection (NID) systems that rely on it. This thesis presents a methodology for a trustworthy adversarial robustness analysis of multiple ML models, and an intelligent method for the generation of realistic adversarial examples in complex tabular data domains like the NID domain: Adaptative Perturbation Pattern Method (A2PM). It is demonstrated that a successful adversarial attack is not guaranteed to be a successful cyber-attack, and that adversarial data perturbations can only be realistic if they are simultaneously valid and coherent, complying with the domain constraints of a real communication network and the class-specific constraints of a certain cyber-attack class. A2PM can be used for adversarial attacks, to iteratively cause misclassifications, and adversarial training, to perform data augmentation with slightly perturbed data samples. Two case studies were conducted to evaluate its suitability for the NID domain. The first verified that the generated perturbations preserved both validity and coherence in Enterprise and Internet-of Things (IoT) network scenarios, achieving realism. The second verified that adversarial training with simple perturbations enables the models to retain a good generalization to regular IoT network traffic flows, in addition to being more robust to adversarial examples. The key takeaway of this thesis is: ML models can be incredibly valuable to improve a cybersecurity system, but their own vulnerabilities must not be disregarded. It is essential to continue the research efforts to improve the security and trustworthiness of ML and of the intelligent systems that rely on it.Organizações modernas podem beneficiar significativamente do uso de Inteligência Artificial (AI), e mais especificamente Aprendizagem Automática (ML), para enfrentar a crescente quantidade e sofisticação de ciberataques direcionados aos seus processos de negócio. No entanto, há vários desafios tecnológicos e éticos que comprometem a confiabilidade da AI. Um dos maiores desafios é a falta de robustez, que é uma propriedade essencial para garantir que se usa ML de forma segura. Melhorar a robustez não é uma tarefa fácil porque ML é inerentemente suscetível a exemplos adversos: amostras de dados com perturbações subtis que causam comportamentos inesperados em modelos ML. Engenheiros de ML e profissionais de segurança ainda não têm o conhecimento nem asferramentas necessárias para prevenir tais disrupções, por isso os exemplos adversos representam uma grande ameaça a ML e aos sistemas de Deteção de Intrusões de Rede (NID) que dependem de ML. Esta tese apresenta uma metodologia para uma análise da robustez de múltiplos modelos ML, e um método inteligente para a geração de exemplos adversos realistas em domínios de dados tabulares complexos como o domínio NID: Método de Perturbação com Padrões Adaptativos (A2PM). É demonstrado que um ataque adverso bem-sucedido não é garantidamente um ciberataque bem-sucedido, e que as perturbações adversas só são realistas se forem simultaneamente válidas e coerentes, cumprindo as restrições de domínio de uma rede de computadores real e as restrições específicas de uma certa classe de ciberataque. A2PM pode ser usado para ataques adversos, para iterativamente causar erros de classificação, e para treino adverso, para realizar aumento de dados com amostras ligeiramente perturbadas. Foram efetuados dois casos de estudo para avaliar a sua adequação ao domínio NID. O primeiro verificou que as perturbações preservaram tanto a validade como a coerência em cenários de redes Empresariais e Internet-das-Coisas (IoT), alcançando o realismo. O segundo verificou que o treino adverso com perturbações simples permitiu aos modelos reter uma boa generalização a fluxos de tráfego de rede IoT, para além de serem mais robustos contra exemplos adversos. A principal conclusão desta tese é: os modelos ML podem ser incrivelmente valiosos para melhorar um sistema de cibersegurança, mas as suas próprias vulnerabilidades não devem ser negligenciadas. É essencial continuar os esforços de investigação para melhorar a segurança e a confiabilidade de ML e dos sistemas inteligentes que dependem de ML

Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making