Showing papers in "Quality and Reliability Engineering International in 2019"
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TL;DR: This work proposes and investigates the use of the degree corrected stochastic block model (DCSBM) to model and monitor dynamic networks that undergo a significant structural change, and applies statistical process monitoring techniques to the estimated parameters of the DCSBM to identify significant structural changes in the network.
Abstract: Funding information Division of Mathematical Sciences, Grant/Award Number: DMS 1830547 Abstract In many applications, it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. We propose and investigate the use of the degree corrected stochastic block model (DCSBM) to model and monitor dynamic networks that undergo a significant structural change. We apply statistical process monitoring techniques to the estimated parameters of the DCSBM to identify significant structural changes in the network. We apply our surveillance strategy to a dynamic US Senate covoting network. We detect significant changes in the political network that reflect both times of cohesion and times of polarization among Republican and Democratic party members. Our analysis demonstrates that the DCSBM monitoring procedure effectively detects local and global structural changes in complex networks, providing useful insights into the modeled system. The DCSBM approach is an example of a general framework that combines parametric random graph models and statistical process monitoring techniques for network surveillance.
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TL;DR: This work has presented a framework based upon a hodiernal nature‐inspired metaheuristic called multiobjective gray wolf optimizer (MOGWO) algorithm, which mimic the hierarchal and hunting behavior of gray wolves for technical specifications optimization of residual heat removal system (RHRS) of an NPP safety system.
TL;DR: A hybrid approach that is combined an exponential weighted moving average (EWMA) control chart for anomaly detection and machine learning models such as support vector regression (SVR) and random forest regression (RFR) with differential evolution (DE) algorithm to predict the RULs of ball bearings.
TL;DR: The South African Researchers Chair Initiative (SARCHI) as discussed by the authors is the South African Research Chair Chair at the University of Pretoria, South Africa, which was established in 2003.
Abstract: The South African Researchers Chair Initiative (SARCHI)
Chair at the University of Pretoria.
TL;DR: This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for MPPR in manufacturing processes and provides the guideline in developing deep learning‐based MSPC systems.
TL;DR: An analytical approach based on paths and integrals is proposed to analyze reliability of nonrepairable hardware‐software co‐design systems considering interactions between hardware and software during the system performance degradation and failure process.