Manufacturing, Materials and Design

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Browsing Manufacturing, Materials and Design by Publisher "IEEE"

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    From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines
    (IEEE, 2024-12-08) Fuad, Mohd Fazril Irfan Ahmad; Khan, Samir; Erkoyuncu, John Ahmet
    The performance of prognostic models relies heavily on the form and trend of the extracted features. However, the raw data collected from physical systems are inherently noisy, large in volume, and exhibit significant variability, which makes them unsuitable for direct use in prognostics. These characteristics poorly reflect the degradation behavior of physical systems and contribute to the uncertainty of prognostic outcome. Hence, transforming this data into relevant features and carefully selecting them is crucial for meeting the specific needs of prognostic models. This paper aims to address data processing challenges by focusing on extraction and selection of high-quality monotonic features which clearly reflect the degradation and can reduce prognostics uncertainty. The proposed framework comprises three main stages: Data pre-processing, feature extraction, and feature selection. It includes a fitness analysis to evaluate the monotonicity and trendability of features supplemented by visual inspections to identify relevant features. Applied to the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset from the NASA Ames Prognostics Data Repository, the framework reduces noise, improves feature monotonicity and trendability, and facilitates the selection of useful features - essential aspects for effective prognostic methods.
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    An interpretable temporal convolutional framework for Granger causality analysis
    (IEEE, 2025) Dong, Aoxiang; Starr, Andrew; Zhao, Yifan
    Most existing parametric approaches for detecting linear or nonlinear Granger causality (GC) face challenges in estimating appropriate time delays, a critical factor for accurate GC detection. This issue becomes particularly pronounced in nonlinear complex systems, which are often opaque and consist of numerous components or variables. In this paper, we propose a novel temporal convolutional network (TCN)-based end-to-end GC detection approach called the Interpretable Temporal Convolutional Framework (ITCF). Unlike conventional deep learning models, which act like a “black box” and are difficult to analyse the interactions between variables, the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction. Specifically, GC is obtained by employing the Least Absolute Shrinkage and Selection Operator (Lasso) regression during the prediction of multivariate time series using TCN. Then, time delays can be estimated by interpreting the TCN kernels. We propose a convolutional Hierarchical Group Lasso (cHGL), a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection. Additionally, as far as we are concerned, this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL, which enabling causal channel selection and inducing sparsity within each TCN channel to remove redundant temporal information, ultimately creating an end-to-end GC detection framework. The testing results of four experiments, involving two simulations and two real data, demonstrate that the proposed ITCF, in comparison with state-of-the-art, offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics, limited data lengths, or numerous variables.
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    ROSE+ : A robustness-optimized security scheme against cascading failures in multipath TCP under LDDoS attack streams
    (IEEE, 2024-12-17) Nie, Jinquan; Ji, Lejun; Jiang, Yirui; Ma, Young; Cao, Yuanlong
    Multipath TCP leverages parallel data transmission across multiple paths to improve transmission rates, reliability, and resource utilization. However, Multipath TCP faces severe network security and communication reliability challenges when exposed to low-rate distributed denial-of-service (LDDoS) attacks. In this paper, we propose a robustness optimization security scheme against cascading failures in Multipath TCP (ROSE+) to tackle the challenges posed by Low-rate Distributed Denial of Service (LDDoS) attacks on network security and communication reliability. The scheme integrates the intricate network load-capacity cascading failures model and leverages the unique characteristics of multipath TCP to facilitate the redistribution of load traffic at ineffectiveness nodes, thereby alleviating the cascading failures induced by LDDoS attack streams. Additionally, we optimize the robustness of communication transmission systems by devising a load-capacity cascading failures model. The experimental results demonstrate that the scheme reduces the probability of cascading failures by 20.07%. This research provides new ideas and methods to improve the robustness and destruction resistance of multipath TCP transmission.
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    When Multipath QUIC meets model predictive control and band sparse network coding: a novel multipathing solution for video streaming over heterogeneous wireless networks
    (IEEE, 2025) Cao, Yuanlong; Zhang, Haopeng; Jiang, Ming; Jiang, Yirui; Nie, Jinquan
    Multipath Quick UDP Internet Connections (MPQUIC) integrated with network coding offers a promising approach to improving the Quality of Experience (QoE) for video services over heterogeneous wireless networks. However, a significant challenge arises when encoding nodes transmit potentially redundant packets while awaiting decoding acknowledgments (ACKs) from endpoints. This behavior can limit effective transmission rates, thereby degrading real-time streaming performance and user QoE. In this paper, we propose MP2-QUIC, which addresses these challenges through a novel adaptive Model Predictive Control (MPC) framework for MPQUIC that optimizes both congestion window and encoding redundancy parameters via a discrete state transition model. By incorporating operating point linearization and leveraging the Central Limit Theorem, MP2-QUIC effectively enhances the control performance and effective throughput of the model in heterogeneous wireless network environments. MP2-QUIC further employs Band-Sparse Network Coding (Band-SNC) to minimize computational complexity at endpoints, while utilizing queuing theory principles to determine optimal encoded packet quantities. This integrated approach significantly enhances end-user QoE, and the experimental results demonstrate MP2-QUIC’s superior performance compared to existing MPQUIC encoding solutions, yielding a 68.85% reduction in peak decoding overhead and marked improvements in Peak Signal-to-Noise Ratio (PSNR).