Browse papers by author

David Siegel and Jay Lee
Publication Target: 
IJPHM
Submission Type: 
Full Paper

This paper presents a health assessment methodology, as well as specific residual processing and figure of merit algorithms for anemometers in two different configurations. The methodology and algorithms are applied to data sets provided by the Prognostics and Health Management Society 2011 Data Challenge. The two configurations consist of the “paired” data set in which two anemometers are positioned at the same height, and the “shear” data set which includes an array of anemometers at different heights.

Publication Year: 
2011
Publication Volume: 
2
Publication Issue: 
2
Publication Control Number: 
014
Page Count: 
12
Submission Keywords: 
Auto-Associative Neural Network
K-means Clustering
Anemometer
Sensor Fault Detection
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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Ephraim Suhir
Publication Target: 
IJPHM
Submission Type: 
Technical Brief

Reliability evaluations and assurances cannot be delayed until the device (system) is fabricated and put into operation.

Publication Year: 
2011
Publication Volume: 
2
Publication Issue: 
2
Publication Control Number: 
013
Page Count: 
5
Submission Keywords: 
remaining useful lifetime
probabilistic approach
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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V. N. Smelyanskiy, V. Hafiychuk, D. G. Luchinsky, J. Miller, C. Banks, and R. Tyson
Submission Type: 
Full Paper

Wave propagation is investigated in sandwich composite panels using analytical approach for layered materials, Mindlin plate theory and finite element modeling in the context of developing an on-board structural health monitoring system. We present an analysis of the dispersion curves for composite sandwich structures based on numerical simulation of the Lamb and Shear wave. The small viscoelasticity was introduced to show coupling between different modes in sandwich panel.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
002
Submission Keywords: 
sandwich panel
guided waves
debond
scattering
Submission Topic Areas: 
Modeling and simulation
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Subrat Nanda and Xiaohui Hu
Submission Type: 
Full Paper

GE monitors a large number of heavy duty equipment for energy generation, locomotives and aviation. These monitoring and diagnostic centers located world-wide sense, derive, transmit, analyze and view terabytes of sensory and calculated data each year. This is used to arrive at critical decisions pertaining to equipment life management - like useful life estimation, inventory planning and finally assuring a minimum level of performance to GE customers.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
065
Submission Keywords: 
condition monitoring
Data Acquisition
data preprocessing
Data-driven and model-based prognostics
Submission Topic Areas: 
CBM and informed logistics
Industrial applications
Systems and platform applications
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Masoud Rabiei, Mohammad Modarres, and Paul Hoffman
Submission Type: 
Full Paper

The work presented in this paper is focused on monitoring fatigue crack growth in metallic structures using acoustic emission (AE) technology. Three different methods are proposed to utilize the information obtained from in-situ monitoring for structural health management.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
050
Submission Keywords: 
acoustic emission
fatigue crack growth
structural health management
Bayesian inference
fusion
Submission Topic Areas: 
Health management system design and engineering
Physics of failure
Structural health monitoring
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Rafik HAJRYA, Nazih Mechbal, and Michel Vergé
Submission Type: 
Full Paper

In structural health monitoring, features extraction from measured data plays an important role. In order to enhance information about damage, we propose in this paper, a new damage detection methodology, based on the Hilbert transform and multivariate analysis. Using measurements given by distributed sensors of a smart composite structure, we apply the Hilbert transform to calculate an envelope matrix. This matrix is then treated using multivariate analysis. The subspaces associated to the envelope matrix are used to define a damage index (DI).

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
059
Submission Keywords: 
Multivariate analysis
Hilbert transform
angle between subspaces
perturbation theory of matrices
singular value decomposition
Smart composite strcuture
Submission Topic Areas: 
Structural health monitoring
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Michael T. Koopmans, Stephen Meicke, Irem Y. Tumer, and Robert Paasch
Submission Type: 
Full Paper

Ocean waves can provide a renewable and secure energy supply to coastal residents around the world. Yet, to safely harness and convert the available energy, issues such as bearing reliability and maintainability need to be resolved. This paper presents the application of a PHM based research methodology to derive empirical models for estimating the wear of polymer bearings installed on wave energy converters. Forming the foundation of the approach is an applicable wave model, sample data set, and experimental test stand to impose loading conditions similar to that expected in real seas.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
030
Submission Keywords: 
wear
test stand
bearings
ocean renewables
wave energy
wave energy converter
PHM system design and engineering
Submission Topic Areas: 
CBM and informed logistics
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Industrial applications
Standards and methodologies
Systems and platform applications
Technology maturation
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Peter K. Ghavami and Kailash Kapur
Submission Type: 
Full Paper

Prognostics and prediction of patient’s short term physiological health status is of critical importance in medicine because it affords medical interventions that prevent escalating medical complications. Accurate prediction of the patient’s health status offers many benefits including faster recovery, lower medical costs and better clinical outcomes. This study proposes a prognostics engine to predict patient physiological status. The prognostics engine builds models from historical clinical data using neural network as its computational kernel.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
031
Submission Keywords: 
Medical Prognostics
medical prediction
Neural Networks
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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Iman Sadeghzadeh, Ankit Mehta, Youmin Zhang, and Camille-Alain Rabbath
Submission Type: 
Full Paper

Based on two successfully and widely used control techniques in many industrial applications under normal (fault-free) operation conditions, the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and Model Reference Adaptive Control (MRAC) strategies have been extended, implemented, and experimentally tested on a quadrotor helicopter Unmanned Aerial Vehicle (UAV) test-bed available at Concordia University, for the purpose of investigation of these two typical and different control techniques as two useful Fault-Tolerant Control (FTC) approaches.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
034
Submission Topic Areas: 
Automated reconfiguration
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H. Senoussi, B. Chebel-Morello, M. Denaï, and N. Zerhouni
Submission Type: 
Full Paper

In this work, we will develop a fault detection system which is identified as a classification task. The classes are the nominal or malfunctioning state. To develop a decision system it is important to select among the data collected by the supervision system, only those carrying relevant information related to the decision task. There are two objectives presented in this paper, the first one is to use data mining techniques to improve fault detection tasks.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
035
Submission Keywords: 
fault detection
PHM sensors and detection methodologies
Data-driven and model-based prognostics
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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