Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Kalman Filter (KF) is widely used in process industries as state estimator to diagnose the faults either in the sensor, actuator or in the plant because of its recursive nature. But, due to increase in non-linearity and exogenous perturbations in the monitored plant, it is often difficult to use a simple KF as state estimator for nonlinear process monitoring purposes. Thus, the first objective of this paper is to design an Adaptive Linear H∞ Filter ( ALH∞F) using gain scheduling algorithm to estimate nonlinear process states in the presence of unknown noise statistics and unmodeled dynamics. Next the designed ALH∞F is used to detect sensor and actuator faults which may occur either sequentially or simultaneously using Multi Model ALH∞F (MMALH∞F). The proposed estimator is demonstrated on Continuously Stirred Tank Reactor (CSTR) process to show the efficacy. And the performance of MMALH∞F is compared with MMALKF. The proposed MMALH∞F is detecting and isolating the faults exactly in the presence of unknown noise statistics and unmodeled dynamics.

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

WSEAS Transactions on Systems

Fault detection and diagnosis in non-linear process using multi model adaptive h∞ filter

The momentous need for optimal operational and maintenance costs of wind energy drives can be accomplished by efficient monitoring of these systems. This allows us to detect the deterioration of the wind energy conversion system's health in early stage. As the frequent inaccessibility of the wind generators due to their positioning heights and as well as the rapid development in offshore wind farms triggering the importance of condition supervision and diagnosis of faults in wind energy conversion systems (WECS). To handle any fault occurred in the current sensor of doubly fed induction generator, a commonly used generator in wind turbines kalman filter is used to detect the sensor fault. Using the generalized observer scheme and dedicated observer scheme the detection and isolation of multiple sensor faults was explained. © 2013 IEEE.

VIT Unitversity

2013 International Conference on Power, Energy and Control (ICPEC)

A survey of fault detection and isolation in wind turbine drives

For the analysis of the extended Kalman filter we have chosen non-linear systems continuous stirred tank reactor as it is widely used in different types of industries specially chemical and petrochemical industries and also in power plant. Though there are so many improved version of Kalman filter, the extended Kalman filter is widely used as it is proven to be most reliable, trusted and easily implementable estimator even for the complex system that to in real time applications. © Research India Publications.

World University root node

International Journal of Applied Engineering Research

State estimation of non-linear process using extended Kalman filter

For safe and economical process operations, state estimation of dynamic systems is an important prerequisite, since all the process variables are generally not measurable. Keeping this in view, a linear and non-linear observer has been developed to estimate the state of a noisy dynamic system. Also chemical processes have been suffering from several faults since many decades. Finding the system faults and isolating them is essential because if a fault occurs in the plant, there will be serious economic losses due to drop in productivity. To serve this purpose, an Adaptive Linear Kalman Filter (ALKF) and an Extended Kalman Filter (EKF) have been designed to detect and isolate the faults. Of all the faults, sensor faults are taken into consideration in this paper. Our task is to compare the results of two estimators and to identify the better one, in estimating the state of non-linear system along with bias detection. Simulation studies are also included to estimate the residuals of the estimators. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

International Review on Modelling and Simulations

State estimation and sensor bias detection using Adaptive Linear Kalman Filter

Mechanical Engineering Series Mechatronic Systems

Fault Detection and Diagnosis in Mechatronic Systems

In the field of autonomous vehicles, lane detection and control plays an important role. In autonomous driving the vehicle has to follow the path to avoid the collision. A deep learning technique is used to detect the curved path in autonomous vehicles. In this paper a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labelling tool box for deep learning is used to detect the curved path in autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy is achieved in detecting path.