688 research outputs found
Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes
The book documents 25 papers collected from the Special Issue âAdvances in Condition Monitoring, Optimization and Control for Complex Industrial Processesâ, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors
30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)
Proceedings of COMADEM 201
Design-Time Quantification of Integrity in Cyber-Physical-Systems
In a software system it is possible to quantify the amount of information
that is leaked or corrupted by analysing the flows of information present in
the source code. In a cyber-physical system, information flows are not only
present at the digital level, but also at a physical level, and to and fro the
two levels. In this work, we provide a methodology to formally analyse a
Cyber-Physical System composite model (combining physics and control) using an
information flow-theoretic approach. We use this approach to quantify the level
of vulnerability of a system with respect to attackers with different
capabilities. We illustrate our approach by means of a water distribution case
study
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Reliability study under the smart grid paradigm using computational intelligent techniques and renewable energy sources.
Doctoral Degree. University of KwaZulu-Natal, Durban.The increase in the demand for a reliable electricity supply by the utilities and consumers has
necessitated the evaluation of the reliability of power systems. A reliable electricity supply is
characterized by no or minimal duration and frequency of supply outages. Current power systems
are changing due to increasing power demand and depletion of fossil fuel deposits. These
changes are related to smart grids which are intelligent electric networks that are capable of using
demand management methods, supporting communication devices and monitoring of consumer
energy consumption. They can also integrate renewable energy sources thereby reducing reliance
on fossils fuel sources. The main objective of this study is to optimize power systems operations
and improve reliability. Different optimization methods are proposed in this study to address the
issues of power systems operations. These optimization problems consider different constraints for
maximum operations of the power systems. Case studies are used to confirm the proposed methods
using the historical and climatic data for the City of Pietermaritzburg (29.37°S and 30.23°E), and
Newcastle (27.71°S, 29.99°E) South Africa. Firstly, the implementation of the back-propagation
algorithm method of the artificial neural networks (ANNs) for designing a predictive model for
power system outage is proposed. The results obtained are found to be satisfactory. In situations
where there is the problem of accessibility to large system data and presence of multiple
system constraints, another method is proposed. This second technique proposes the application
of a maximum entropy function-based multi-constrained event-driven outage prediction model,
using the collaborative neural network (CONN) algorithm. The outcome is better than the conventional
event-driven methods. Lastly, an adaptive model predictive control (AMPC) method with
the integration of renewable energy sources (RESs) and a battery energy storage system (BESS)
is proposed to further improve the reliability of the power system. The developed method uses
a modified Roy Billinton Test System (RBTS) to implement the reliability improvement of the
power system. The proposed computational intelligent techniques fulfil the necessities of operation
robustness, implementation simplicity and reliability improvement of the power systems
Artificial neural networks for vibration based inverse parametric identifications: A review
Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNsâ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression
This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions
Signal and data processing for machine olfaction and chemical sensing: A review
Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing
Molecular Dynamics Simulation
Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate âfirst-principlesâ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardlyâdealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...
- âŠ