Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

Task-driven data fusion for additive manufacturing

Additive manufacturing (AM) is a critical technology for the next industrial revolution, offering the prospect of mass customization, flexible production, and on-demand manufacturing. However, difficulties in understanding underlying mechanisms and identifying latent factors that influence AM processes build up barriers to in-depth research and hinder its widespread adoption in industries. Recent advancements in data sensing and collection technologies have enabled capturing extensive data from AM production for analytics to improve process reliability and part quality. However, modelling the complex relationships between the manufacturing process and its outcomes is challenging due to the multi-physics nature of AM processes. The critical information of AM production is embedded within multi-source, multi-dimensional, and multi-modal heterogeneous data, leading to difficulties when jointly analysing. Therefore, how to bridge the gap between the multi-physics interactions and their outcomes through heterogeneous data analytics becomes a crucial research challenge. Data fusion strategies and techniques can effectively leverage multi-faceted information. Since AM tasks can have various requirements, the corresponding fusion techniques should be task-specific. Hence, this thesis will focus on how to deal with task-driven data fusion for AM. To address the challenges stated above, a comprehensive task-driven data fusion framework and methodology are proposed to provide systematic guidelines to identify, collect, characterise, and fuse AM data for supporting decision-making activities. In this framework, AM data is classified into three major categories, process-input data, process-generated data, and validation data. The proposed methodology consists of three steps, including the identification of data analytics types, data required for tasks, acquisition, and characterization, and task-driven data fusion techniques. To implement the framework and methodology, critical strategies for multi-source and multi-hierarchy data fusion, and Cloud-edge fusion, are introduced and the detailed approaches are described in the following chapters. One of the major challenges in AM data fusion is that the multi-source data normally has various dimensions, involving nested hierarchies. To fuse this data for analytics, a hybrid deep learning (DL) model called M-CNN-LSTM is developed. In general, two levels of data and information are focused on, layer level and build level. In the proposed hybrid model, the CNN part is used to extract features from layer-wise images of sliced 3D models, and the LSTM is used to process the layer-level data concatenated with convolutional features for time-series modelling. The build-level information is used as input into a separate neural network and merged with the CNN-LSTM for final predictions. An experimental study on an energy consumption prediction task was conducted where the results demonstrated the merits of the proposed approach. In many AM tasks at the initial stage, it is usually time-consuming and costly to acquire sufficient data for training DL-based models. Additionally, these models are hard to make fast inferences during production. Hence, a Cloud-edge fusion paradigm based on transfer learning and knowledge distillation (KD)-enabled incremental learning is proposed to tackle the challenges. The proposed methodology consists of three main steps, including (1) transfer learning for feature extraction, (2) base model building via deep mutual learning (DML) and model ensemble, and (3) multi-stage KD-enabled incremental learning. The 3-step method is developed to transfer knowledge from the ensemble model to the compressed model and learn new knowledge incrementally from newly collected data. After each incremental learning in the Cloud platform, the compressed model will be updated to the edge devices for making inferences on the incoming new data. An experimental study on the AM energy consumption prediction task was carried out for demonstration. Under the proposed task-driven data fusion framework and methodology, case studies focusing on three different AM tasks, (1) mechanical property prediction of additively manufactured lattice structures (LS), (2) porosity defects classification of parts, and (3) investigating the effect of the remelting process on part density, were carried out for demonstration. Experimental results were presented and discussed, revealing the feasibility and effectiveness of the proposed framework and approaches. This research aims to pave the way for leveraging AM data with various sources and modalities to support decision-making for AM tasks using data fusion and advanced data analytics techniques. The feasibility and effectiveness of the developed fusion strategies and methods demonstrate their potential to facilitate the AM industry, making it more adaptable and responsive to the dynamic demands of modern manufacturing

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov