Optimization of Sensor Locations and Sensitivity Analysis for Engine Health Monitoring Using Minimum Interference Algorithms

Abstract

The global optimization of sensor locations and a sensitivity analysis based on the minimization of interferences due to wireless communications between sensors are studied in the presence of additive white Gaussian noise (AWGN). We used a Gram matrix approach for robust determination of sensor locations by minimizing the interferences (maximizing the signal strength) among sensors for engine health monitoring systems. In order to solve the problem of optimum placement, an iterative algorithm for maximizing the determinant of the Gram matrix is proposed and implemented. The sensitivity criterion proposed in this paper is the spectral number of the Frobenius norm of the Gram matrix associated with sensor readings. We derived the necessary conditions under which the number of sensors and the optimal sensor locations will remain unchanged when the data measured for sensitivity analysis is affected by AWGN. Our theoretical results are verified by simulations providing details concerning numerical implementations.

Optimising Sensor Location for an Enhanced Gearbox Condition Monitoring System

“…Cotae [5] and [6] proposed sensors positioning method to reduce the traces of the covariance matrix which associated with the structural parameters estimation. Tongpadungrod et al [7] used Principal Component Analysis (PCA) to evaluate the optimum location for sensors.…”

Optimal measurement locations for parameter estimation of non linear distributed parameter systems

“…Nahor et al (2003) minimised the ratio of the largest to the smaller eigenvalue of the FIM to compute optimal temperature sensor positions for food processes. To the best of our knowledge, these techniques have not been applied yet to systems described by partial differential equations, in spite of their resolute advantages (Uciński, 1999, Löhner and Camelli, 2005, Waterhouse et al, 2009, Vande, 2000, Venkateswarlu and Kumar, 2006, Punithakumar et al, 2006, Peng, 2005, Cotae et al, 2008, Balsa-Canto et al, 2008. Christofides and Antoniades (2000, 2001 presented new approaches to calculate the optimal actuator/sensor locations of uncertain transportreaction systems under control.…”

Work In Progress: Teaching Wireless Sensor Networks Through Laboratory Experiments

“…Energy efficient system design in wireless sensor networks has become the top priority in order to provide the maximum possible lifetime of a given network [3]. Many communication, power management, and data dissemination protocols have been specifically designed for Wireless Sensor Networks where energy awareness is an essential design issue.…”

“…How ours is different: Given the sensing locations, the fundamental problem is to determine the optimal locations of nodes together with optimal energy provided to them so that the network is alive during the desired lifetime with minimum energy consumed [3,4]. Requested equipment within the proposed experiment: This laboratory experiment project focuses on the optimal placement of the nodes.…”

“…This article discusses the possibility of teaching wireless sensor network communication through laboratory experiments. We focus on the following experiments: optimum placement of sensors in wireless sensor networks [3], [19], [21], sensor placement for effective diagnosis of multiple faults [5], [7], [8], [10], and continuous health monitoring using wearable biosensors [4]. For each project we presents the related work, how the proposed project is different, the requested equipment within the undergoing experiment and the anticipated results.…”