Review and Application of Model and Spectral Analysis Based Fault Detection and Isolation Scheme in Actuators and Sensors
H. Bal,
S. K. Mohanty,
N. P. Mahalik,
B. B. Biswal
Issue:
Volume 3, Issue 4, August 2015
Pages:
49-55
Received:
15 June 2015
Accepted:
25 June 2015
Published:
10 July 2015
Abstract: For condition monitoring of machineries and systems conventional method such as hardware or sensor based error checking scheme were in use. As the automated systems are becoming complex, recently most of the condition-monitoring schemes have been applying sophisticated analytical tools and methods to achieve improved performance. The objective of this paper is to demonstrate model based Fault Detection and Isolation (FDI) schemes for mechatronic systems and devices. First we have reviewed FDI approaches and implementation schemes. Then, we have developed two frameworks: model and spectral signature based for the implementation of FDI schemes. The model based feature estimation and spectral analysis based multiresolution methods are implemented in exemplar devices such as actuators and sensors used in mechatronic systems. Based on the frameworks, the diagnostics and isolation algorithms were developed using MATLAB code. The algorithms are capable of detecting and isolating faults within the systems. The study is comprehensive and the implementation scenarios can be extendible to many types of systems and devices used in the mechatronic domain.
Abstract: For condition monitoring of machineries and systems conventional method such as hardware or sensor based error checking scheme were in use. As the automated systems are becoming complex, recently most of the condition-monitoring schemes have been applying sophisticated analytical tools and methods to achieve improved performance. The objective of t...
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Evolutionary Model for Virus Propagation on Networks
Arnold Adimabua Ojugo,
Fidelis Obukowho Aghware,
Rume Elizabeth Yoro,
Mary Oluwatoyin Yerokun,
Andrew Okonji Eboka,
Christiana Nneamaka Anujeonye,
Fidelia Ngozi Efozia
Issue:
Volume 3, Issue 4, August 2015
Pages:
56-62
Received:
11 July 2015
Accepted:
21 July 2015
Published:
31 July 2015
Abstract: The significant research activity into the logarithmic analysis of complex networks will yield engines that will minimize virus propagation over networks. This task of virus propagation is a recurring subject and design of complex models will yield solutions used in a number of events not limited to and include its propagation, network immunization, resource management, capacity service distribution, dataflow, adoption of viral marketing amongst others. Machine learning, stochastic models are successfully employed to predict virus propagation and its effects on networks. This study employs SI-models for independent cascade and the dynamic models with Enron dataset (of e-mail addresses) and presents comparative result using varied machine models. It samples 25,000 e-mails of Enron dataset with Entropy and Information Gain computed to address issues of blocking, targeting and extent of virus spread on graphs. Study addressed the problem of the expected spread immunization and the expected epidemic spread minimization; but not the epidemic threshold (for space constraint).
Abstract: The significant research activity into the logarithmic analysis of complex networks will yield engines that will minimize virus propagation over networks. This task of virus propagation is a recurring subject and design of complex models will yield solutions used in a number of events not limited to and include its propagation, network immunization...
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