Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Effects of mass of charge on loop heat pipe operational characteristics

Experimental results on a loop heat pipe, using R134a as the working fluid, indicates that the liquid inventory in the compensation chamber can significantly influence the operating characteristics. The large liquid inventory in the compensation chamber, under terrestrial conditions, can result in loss of thermal coupling between the compensation chamber and the evaporator core. This causes the operating temperature to increase monotonically. This phenomenon, which has been experimentally observed, is reported in this paper. A theoretical model to predict the steady-state performance of a loop heat pipe with a weak thermal link between the compensation chamber and the core, as observed in the experiment, is also presented. The predicted and the experimentally determined temperatures correlate well.

Nonideal negative resistors and capacitors using an operational amplifier

Negative impedance converters (NIC's) may be used to realize negative driving-point impedances. The effect of the nonideal characteristics of the operational amplifier such as finite frequencydependent gain and output impedance on the performance of the negative impedances is analyzed. Detailed equivalent circuits showing the additional positive or negative inductive impedances due to the nonideal characteristics are given for negative resistance and negative capacitance realizations, and their relative performances are compared. The experimental results confirm the validity of the equivalent circuits. The effect of the slew rate of the operational amplifier on the maximum signal-handling capability (SHC) of the negative impedances at high frequencies is studied. Practical design considerations for achieving wider bandwidth as well as improved SHC are discussed.

Characteristics of nonideal operational amplifiers

The departures of the operational amplifiers (OA's) from the ideal performance and their effect on VCV's in the inverting and noninverting mode are discussed. It is found that for the same ideal gain, the bandwidths for the inverting and noninverting modes are different, the former being less. Complete equivalent circuits describing the frequency dependance of the input and output impedances for both modes are given. In particular, the output impedance is shown to be inductive for the frequencies of interest, and this is also confirmed by experimental results.

Channel Volatiles Of South Indian Cordierites As Indicators Of Metamorphic Fluid Composition

The channel volatiles in cordierites of the Precambrian high-grade metapelites from southern and eastern Karnataka northern Tamil Nadu and southern Kerala were analyzed in an attempt to use them as metamorphic fluid fugacity indicators. Infrared powder absorption spectra, used to characterize the channel volatiles, showed that all the 21 analyzed cordierites have H2O and CO2 as the channel volatiles, indicating the predominantly H2O-CO2 composition of the metamorphic fluids. The H2O fraction in the metamorphic fluid was computed using a published thermodynamic method in conjunction with gravimetrically determined cordierite channel H2O content, available P - T estimates and an appropriate equation of state for the H2O - CO2 fluids. The IR data and these calculated X(H2O) values indicate an overall correlation between the variation in the relative proportion of H2O and CO2 in the fluids and the metamorphic grade. The average computed X(H2O) values are: 0.78 for the amphibolite facies eastern Karnataka pelites, 0.36 for the amphibolite facies southern Karnataka pelites, 0.19 for the southern Karnataka transitional zone rocks and 0.13 for the northern Tamil Nadu granulites. Consistently low X(H2O) values, at about 0.2, were obtained for the orthopyroxene-bearing assemblages.

Composite material and piezoelectric coefficient uncertainty effects on structural health monitoring using feedback control gains as damage indicators

This article addresses uncertainty effect on the health monitoring of a smart structure using control gain shifts as damage indicators. A finite element model of the smart composite plate with surface-bonded piezoelectric sensors and actuators is formulated using first-order shear deformation theory and a matrix crack model is integrated into the finite element model. A constant gain velocity/position feedback control algorithm is used to provide active damping to the structure. Numerical results show that the response of the structure is changed due to matrix cracks and this change can be compensated by actively tuning the feedback controller. This change in control gain can be used as a damage indicator for structural health monitoring. Monte Carlo simulation is conducted to study the effect of material uncertainty on the damage indicator by considering composite material properties and piezoelectric coefficients as independent random variables. It is found that the change in position feedback control gain is a robust damage indicator.

WOMM: A Weak Operational Memory Model

Memory models of shared memory concurrent programs define the values a read of a shared memory location is allowed to see. Such memory models are typically weaker than the intuitive sequential consistency semantics to allow efficient execution. In this paper, we present WOMM (abbreviation for Weak Operational Memory Model) that formally unifies two sources of weak behavior in hardware memory models: reordering of instructions and weakly consistent memory. We show that a large number of optimizations are allowed by WOMM. We also show that WOMM is weaker than a number of hardware memory models. Consequently, if a program behaves correctly under WOMM, it will be correct with respect to those hardware memory models. Hence, WOMM can be used as a formally specified abstraction of the hardware memory models. Moreover; unlike most weak memory models, WOMM is described using operational semantics, making it easy to integrate into a model checker for concurrent programs. We further show that WOMM has an important property - it has sequential consistency semantics for datarace-free programs.

Odonataas Indicators of Riparian Ecosystem Health A Case Study From South Western Karnakata, India

The influence of riparian land use on the diversity an~ distribution were investigated by sampling 113 localities covering 4 districts in south-western Karnataka. A total of 55 species in 12 families were recorded. Streams, rivers and lakes had higher diversity than marshes and sea coast. However, lakes had low endemism than streams and rivers. Streams flowing through evergreen forests had higher diversity and endemism. Human impacted riparian zones such as paddy fields had relatively lower species richness. However, streams flowing through forestry plantations had higher diversity than other natural riparian zones such as dry deciduous, moist deciduous and semi evergreen forests. Myristica swamps-a relict evergreen forest marsh had low diversity and high endemism. Odonate communities of lentic ecosystems, and human impacted streams and rivers were characterized by widespread generalist species. Endemics and habitat specialists were. restricted to streams and rivers with undisturbed riparian zone. The study documents possible odonate community change due to human impact: The influence of riparian 'Ianduse change on odonate community is also discussed.

Operational experience on a grid connected 100 kWe biomass gasification power plant in Karnataka, India

The paper reports the operational experience from a 100 kWe gasification power plant connected to the grid in Karnataka. Biomass Energy for Rural India (BERI) is a program that implemented gasification based power generation with an installed capacity of 0.88 MWe distributed over three locations to meet the electrical energy needs in the district of Tumkur. The operation of one 100 kWe power plant was found unsatisfactory and not meeting the designed performance. The Indian Institute of Science, Bangalore, the technology developer, took the initiative to ensure the system operation, capacity building and prove the designed performance. The power plant connected to the grid consists of the IISc gasification system which includes reactor, cooling, cleaning system, fuel drier and water treatment system to meet the producer gas quality for an engine. The producer gas is used as a fuel in Cummins India Limited, GTA 855 G model, turbo charged engine and the power output is connected to the grid. The system has operated for over 1000 continuous hours, with only about 70 h of grid outages. The total biomass consumption for 1035 h of operation was 111 t at an average of 107 kg/h. Total energy generated was 80.6 MWh reducing over loot of CO(2) emissions. The overall specific fuel consumption was about 1.36 kg/kWh, amounting to an overall efficiency from biomass to electricity of about 18%. The present operations indicate that a maintenance schedule for the plant can be at the end of 1000 h. The results for another 1000 h of operation by the local team are also presented. (C) 2011 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Calibration and linearity verification of capacitance type cryo level indicators using cryogenically multiplexed diode array

In space application the precision level measurement of cryogenic liquids in the storage tanks is done using triple redundant capacitance level sensor, for control and safety point of view. The linearity of each sensor element depends upon the cylindricity and concentricity of the internal and external electrodes. The complexity of calibrating all sensors together has been addressed by two step calibration methodology which has been developed and used for the calibration of six capacitance sensors. All calibrations are done using Liquid Nitrogen (LN2) as a cryogenic fluid. In the first step of calibration, one of the elements of Liquid Hydrogen (LH2) level sensor is calibrated using 700mm eleven point discrete diode array. Four wire method has been used for the diode array. Thus a linearity curve for a single element of LH2 is obtained. In second step of calibration, using the equation thus obtained for the above sensor, it is considered as a reference for calibrating remaining elements of the same LH2 sensor and other level sensor (either Liquid Oxygen (LOX) or LH2). The elimination of stray capacitance for the capacitance level probes has been attempted. The automatic data logging of capacitance values through GPIB is done using LabVIEW 8.5.

Arithmetic coding based lossless compression schemes for power system steady state operational data

Introduction of processor based instruments in power systems is resulting in the rapid growth of the measured data volume. The present practice in most of the utilities is to store only some of the important data in a retrievable fashion for a limited period. Subsequently even this data is either deleted or stored in some back up devices. The investigations presented here explore the application of lossless data compression techniques for the purpose of archiving all the operational data - so that they can be put to more effective use. Four arithmetic coding methods suitably modified for handling power system steady state operational data are proposed here. The performance of the proposed methods are evaluated using actual data pertaining to the Southern Regional Grid of India. (C) 2012 Elsevier Ltd. All rights reserved.

Operational inferences on VANETs in 802.16e and 802.11p with improved performance by Congestion alert

Mobile WiMAX is a burgeoning network technology with diverse applications, one of them being used for VANETs. The performance metrics such as Mean Throughput and Packet Loss Ratio for the operations of VANETs adopting 802.16e are computed through simulation techniques. Next we evaluated the similar performance of VANETs employing 802.11p, also known as WAVE (Wireless Access in Vehicular Environment). The simulation model proposed is close to reality as we have generated mobility traces for both the cases using a traffic simulator (SUMO), and fed it into network simulator (NS2) based on their operations in a typical urban scenario for VANETs. In sequel, a VANET application called `Street Congestion Alert' is developed to assess the performances of these two technologies. For this application, TraCI is used for coupling SUMO and NS2 in a feedback loop to set up a realistic simulation scenario. Our inferences show that the Mobile WiMAX performs better than WAVE for larger network sizes.