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  Detection of Bias, Drift, Freeze and Abrupt Sensor Failure using Intelligent Dedicated Observer Based Fault Detection and Isolation for Three Interacting Tank Process  
  Authors : C. Amritha; U. Sabura Banu
  Cite as: ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-124.pdf


This paper presents a design of MANFIS (Multiple Adaptive Neuro Fuzzy Inference System) based sensor Fault Detection and Isolation (FDI) scheme for a three interacting tank system. Three pairs of dedicated observers are designed to estimate the three states of the system. The observers designed are fuzzy systems whose optimal membership functions and rule base are determined by neural networks. The difference between the estimated and measured value is called as residuals. Decision functions are determined from the residuals. These functions are compared to a threshold value, when the value of these functions exceed a particular threshold, the presence of fault is indicated. The FDI designed is implemented to detect sensor bias, abrupt sensor failure, sensor drift and sensor freeze types of sensor faults.


Published In : IJCSN Journal Volume 2, Issue 6

Date of Publication : 01 December 2013

Pages : 37 - 46

Figures : 15

Tables : 04

Publication Link : ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-124.pdf




C. Amritha : Bachelor of Engineering(Electronics and Instrumentation), Masters (Electronics and Instrumentation). Currently working as Assistant professor in VelTech Technical University .Research interest includes nano_sensors and control engineering.

U. Sabura Banu : Masters,PhD. Currently working as an associate professor in B. S. Abdur Rehman University. Cuurent area of research includes fractional order controllers and biomedical instruments.








Bias fault

Sensor freeze

Sensor drift

Abrupt sensor failure

Fault detection and isolation




EACP provides better lifetime for nodes compared to SEP and CBRP. In addition to reducing energy dissipation, EACP successfully distributes energy-usage among the nodes in the network such that the nodes die randomly and at essentially the same rate. We have used only residual energy for head selection procedure. Presently, EACP consumes higher computational power due to reporting and cluster head selection. The second limitation is that the performances have been compared with standard SEP and CBRP algorithm. Performance of other sensor network head selection like PEGASIS, EEHC, TEEN etc. have not been considered. We only considered energy heterogeneity in future; we take computation heterogeneity as well as link heterogeneity and test the result how it improves the lifetime of system.










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