Publications – ICSL

Fault Diagnosis and Failure Prognosis for Engineering Systems

Books and Book Chapters

2006

  • Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., and Wu, B., Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley and Sons, September, 2006.

2003

  • Zhang, Y., Britton, D., Zhang, Y., Heck, B. and Vachtsevanos, G., An Integrated Systems Approach to Monitoring and Control of Complex Industrial Processes, in Recent Advances in Intelligent Systems and Signal Processing, Electrical and Computer Engineering Series, WSEAS Press, pp. 54-59, 2003.
  • Propes, N., and Vachtsevanos, G., “Fuzzy Petri Net Based Mode Identification Algorithm for Fault Diagnosis of Complex Systems,” SPIE Vol. 5107 System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, edited by Peter Willet, Kirubarajan Thiagalingam, SPIE, Bellingham, WA, August 2003.
  • Mufti, M., Vachtsevanos, G., and Dorrity, L., “An Intelligent Approach to Fabric Defect Detection in Textile Processes,” in Machine Vision for the Inspection of Natural Products, edited by Mark Graves, pp. 279-303, Springer, January 2003.
  • Wills, L. M., Kannan, S., Sander, S., Guler, M., Heck, B., Prasad, J.V.R., Schrage, D., and Vachtsevanos G., “A Prototype Open Control Platform for Reconfigurable Control Systems,” Software-Enabled Control: Information Technologies for Dynamical Systems, (Samad, T. and Balas, G., Eds.), IEEE Press, pp. 63-84, April 2003.

2002

  • Echauz, J. and Vachtsevanos, G., “Radial Wavelet Neural Networks,” in Foundations of Wavelet Networks and Applications, edited by Iyengar, S. S., Cho, E. C. and Phela, V. V. Chapman and Hall/CRC, Chapter 7, pp. 155-170, July 2002.
  • Echauz, J. and Vachtsevanos, G., “Separating Order from Disorder,” in Foundations of Wavelet Networks and Applications, edited by Iyengar, S. S., Cho, E. C. and Phela, V. V., Chapman and Hall/CRC, Chapter 6, pp. 145-154, July 2002.

2000

  • Hadden, G., Bergstrom, P., Samad, T., Bennett, B. H., Vachtsevanos, G., and Van Dyke, J., “System Health Management for Complex Systems,” in Automation, Control and Complexity, An Integrated Approach, Edited by Samad, T. and Weyrauch, J. John Wiley and Sons, Ltd, pp. 191-214, June 2000.
  • Pirovolou, D. and Vachtsevanos, G., Output Tracking Using Fuzzy Neural Networks, Fuzzy Control, Synthesis and Analysis, edited by Fariwata, S., Filev, D. and Langari, R., John Wiley and Sons, pp. 335-348, May 2000.
  • Vachtsevanos, G. and Wang, P., “Integrated Process Design and Control – A New Paradigm with Applications to Mechatronics, Techniques and Applications,” Vol. 1, Industrial Manufacturing, Edited by Cornelius T. Leondes, pp. 1- 44, Gordon and Breach Science Publishers, November 2000.

Journal Papers

2010

Abstract—Machine prognosis is a significant part of Condition-Based Maintenance (CBM) and intends to monitor and track the time evolution of the fault so that maintenance can be performed or the task be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function (pdf). An on-line update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. The results show that it outperforms classical condition predictors.

Abstract—This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFS) and Bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An on-line model update scheme is developed on the basis of the Probability Density Function (PDF) of the NFS’s residuals between the actual and predicted condition data. Bayesian estimation algorithms adopt the model’s predicted data as prior information in combination with on-line measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive Bayesian algorithm called particle filtering is utilized to calculate in real-time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated and then predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases — a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors — recurrent neural networks (RNN), neuro-fuzzy systems (NFS) and recurrent neuro-fuzzy systems (RNFS). The results demonstrate that the proposed approach can predict machine conditions more accurately.

2009

Abstract—This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and
failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation
of two autonomous modules. A fault detection and identification (FDI) module uses
a hybrid state-space model of the plant and a PF algorithm to estimate the state probability
density function (pdf) of the system and calculates the probability of a fault condition in realtime.
Once the anomalous condition is detected, the available state pdf estimates are used as
initial conditions in prognostic routines. The failure prognostic module, on the other hand,
predicts the evolution in time of the fault indicator and computes the pdf of the remaining
useful life (RUL) of the faulty subsystem, using a non-linear state-space model (with unknown
time-varying parameters) and a PF algorithm that updates the current state estimate. The
outcome of the prognosis module provides information about the precision and accuracy of
long-term predictions, RUL expectations and 95% confidence intervals for the condition under
study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the
proposed approach.

Abstract—Fault diagnosis and failure prognosis are essential
techniques in improving the safety of many mechanical systems.
However, vibration signals are often corrupted by noise; therefore,
the performance of diagnostic and prognostic algorithms is
degraded. In this paper, a novel denoising structure is proposed
and applied to vibration signals collected from a testbed of the
helicopter main gearbox subjected to a seeded fault. The proposed
structure integrates a denoising algorithm, feature extraction, failure
prognosis, and vibration modeling into a synergistic system.
Performance indexes associated with the quality of the extracted
features and failure prognosis are addressed, before and after
denoising, for validation purposes.

Abstract— The tum-to-tum short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park’s vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.

2008

Abstract—This paper introduces an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a particle-filtering (PF) algorithm to estimate the state probability density function (pdf) of the system and calculates the probability of a fault condition in real-time. Once the anomalous condition is detected, the available state pdf estimates are used as initial conditions in prognostic routines. The failure prognostic module, on the other hand, predicts the evolution in time of the fault indicator and computes the pdf of the remaining useful life (RUL) of the faulty subsystem, using a nonlinear state-space model (with unknown time-varying parameters) and a particle-filtering algorithm that updates the current state estimate. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the proposed approach.

Abstract—Fault diagnosis and failure prognosis are essential techniques in improving the safety of many mechanical systems. However, vibration signals are often corrupted by noise; therefore, the performance of diagnostic and prognostic algorithms is degraded. In this paper, a novel denoising structure is proposed and applied to vibration signals collected from a testbed of the helicopter main gearbox subjected to a seeded fault. The proposed structure integrates a denoising algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system. Performance indexes associated with the quality of the extracted features and failure prognosis are addressed, before and after denoising, for validation purposes.

  • Ginart, A., Brown, D., Kalgren, P., Roemer, M., “On-line Ringing Characterization as PHM Technique for Power Drive and Electrical Machinery,” in press, to be published in IEEE Transactions on Instrumentation & Measurement, 2008.

Abstract—Embeddable features easily incorporated in traditional power drive systems are identified for prognostic health management (PHM) systems. This novel technique utilizes the original PWM waveform produced by the inverter to evaluate the power electronic circuit and electric machine against transistor degradation. Evaluation of the primary feature, ringing characterization, with experimental data demonstrates its viability as a practical real-time power device health-state indicator.

Abstract—Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition Based Maintenance (CBM) requires that the health of critical components/systems be monitored and diagnostic/prognostic strategies be developed to detect and identify incipient failures and predict the failing component’s remaining useful life (RUL). Typically, vibration and other key indicators on-board an aircraft are severely corrupted by noise thus curtailing the ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution de-noising scheme that employs a vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed approach.

Abstract-With increased system complexity, Condition-Based Maintenance (CBM) becomes a promising solution to system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.

2007

Abstract—This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant (with unknown time-varying parameters) and a particle filtering (PF) algorithm to estimate the probability density function (pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life (RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

  • Brown, D., Kalgren, P., Roemer, M., “Electronic Prognostics – A Case Study Using Switched-Mode Power Supplies (SMPS),” IEEE Instrumentations & Measurement, vol. 10, no. 4, pp. 20-26, August 2007.

Abstract—Increased awareness of potential cost savings and improved reliability associated with condition based maintenance (CBM) for avionic systems has generated interest in the research and development of novel electronic prognostic and health management (PHM) solutions. This paper describes the process, with related examples, used to develop prognostics algorithms for a commercially available switched-mode power supply (SMPS) using corroborative evidence sources. The process begins with a Pareto analysis indicating the primary modes of failure. Critical components are identified using a three-tier failure mode and effects analysis (FMEA) by investigating device, circuit, and system parameters sensitive to degradation. Once acceleration factors, or sources of degradation, are known damage accumulation failure models for each critical component are derived from highly accelerated life tests (HALT). Then, healthy components are systematically degraded to varying levels of severity by performing highly accelerated stress testing (HAST). These components are used in seeded fault tests to identify system- level parameters sensitive to device damage. Features extracted from data recorded during seeded fault tests are used to derive feature- based failure models. Finally, reasoning and data fusion algorithms are applied to both models to generate corroborative remaining useful life (RUL) predictions.

  • Brown, D., Kalgren, P., Byington, C., Roemer, M., “Electronic prognostics – A case study using global positioning system (GPS),” Microelectronics Reliability, vol. 47, no. 12, pp. 1874-1881, December 2007.

Abstract—Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements have spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radio frequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified prognostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a commercial off the shelf (COTS) GPS receiver to within ±5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio.

2005

  • Saha, B. and Vachtsevanos, G., “A Model-Based Reasoning Approach to System Fault Diagnosis, WSEAS Transactions on Systems, Issue 8, Vol. 5, pp. 1997 – 2004, August 2006.

  • Bae, H., Kim, Y-T., Kim, S. and G, Vachtsevanos, “Datamining Roadmap to Extract Inference Rules and Design Data Models from Process Data of Industrial Applications”, International Journal of Fuzzy Logic and Intelligent Systems, Vol. 5, No. 3, pp. 200-205, September 2005.

2003

  • Heck, B., Wills, L., and Vachtsevanos G., “Software Technology for Implementing Reusable, Distributed Control Systems,” IEEE Control Systems Magazine, Vol. 23, No. 1, pp. 21-35, February 2003. (IEEE Control Systems Magazine Outstanding Paper Award for the years 2002-2003)

  • Guler, M., Clements, S., Wills, L., Heck, B., and Vachtsevanos, G., “Generic Transition Management for Reconfigurable Hybrid Control Systems,” IEEE Control Systems Magazine, Vol. 23, No. 1, pp. 36-49, February 2003.

  • Mufti, M. and Vachtsevanos, G., “Fuzzy Wavelets for Feature Extraction and Intelligent Control”, International Journal of Fuzzy Systems, Vol. 5, No. 2, pp. 141-150, June 2003.

2001

  • Wills, L., Kannan, S., Sander, S., Guler, M., Heck, B., Prasad, J.V.R., Schrage, D., and Vachtsevanos, G., “An Open Platform for Reconfigurable Control,” IEEE Control Systems Magazine, Vol. 21, No. 3, pp. 49-64, June 2001.

  • Esteller, R., Vachtsevanos, G., Echauz, J., and Litt, B., “A Comparison of Fractal Dimension Algorithms Using Synthetic and Experimental Data,” IEEE Transactions on Circuits and Systems – 1: Fundamental Theory and Applications, Vol. 48, No. 2, pp. 177-183, February 2001.

  • Wang, P. and Vachtsevanos, G. “Fault Prognostics Using Dynamic Wavelet Neural Networks”, Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol.15, pp. 349-365, 2001.

2000

  • Kim, S., and Vachtsevanos, G., “An Intelligent Approach to Integration and Control of Textile Processes,” the International Journal of Information Sciences, special issue on “Analytical Fuzzy Control Theory with Applications”, Vol. 123, pp. 181-199, 2000.

  • Rufus, F., and Vachtsevanos, G., “Design of Mode-to-Mode Fuzzy Controllers,” International Journal of Intelligent Systems, Vol. 15, June 7, pp. 657-685, 2000.

Conference Papers

2010

  • Bole, B., Brown, D., Pei, H. L., Goebel, K., Tang, L., and Vachtsevanos, G., “Fault Adaptive Control of Overatuated Systems Using Prognostic Estimation,” in Annual Conference of the Prognostics and Health Management Society, (Portland, OR), 2010.

  • Brown, D., Abbas, M., Ginart, A., Ali, I., Kalgren, P., and Vachtsevanos, G., “Turn-off time as a precursor for gate bipolar transistor latch-up faults in electric motor drives,” in Annual Conference of the Prognostics and Health Management Society, (Portland, OR), 2010.

Abstract—This paper presents a .NET framework as the integrating software platform linking all constituent modules of the fault diagnosis and failure prognosis architecture. The inherent characteristics of the .NET framework provide the proposed system with a generic architecture for fault diagnosis and failure prognosis for a variety of applications. Functioning as data processing, feature extraction, fault diagnosis and failure prognosis, the corresponding modules in the system are built as .NET components that are developed separately and independently in any of the .NET languages. With the use of Bayesian estimation theory, a generic particle-filtering-based framework is integrated in the system for fault diagnosis and failure prognosis. The system is tested in two different
applications — bearing spalling fault diagnosis and failure prognosis and brushless DC motor turn-to-turn winding fault diagnosis. The results suggest that the system is capable of meeting performance requirements specified by both the developer and the user for a variety of engineering systems.

Abstract—Machine remaining useful life (RUL) prediction is a key part of Condition-Based Maintenance (CBM), which provides the time evolution of the fault indicator so that maintenance can be performed to avoid catastrophic failures. This paper proposes a new RUL prediction method based on adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which predicts the time evolution of the fault indicator and computes the probability density function(pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter to describe the fault propagation process; the high-order particle filter uses real-time data to update the current state estimates so as to improve the prediction accuracy. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results show that it outperforms the conventional ANFIS predictor.

Abstract—This paper presents a novel set of uncertainty measures
to quantify the impact of input uncertainty on nonlinear
prognosis systems. A Particle Filtering-based method is
also presented that uses this set of uncertainty measures
to quantify, in real time, the impact of load, environmental,
and other stresses for long-term prediction. Furthermore,
this work shows how these measures can be used
to implement a novel feedback correction loop aimed to
suggest modifications, at a system input level, with the
purpose of extending the remaining useful life of a faulty
nonlinear, non-Gaussian system. The correction scheme
is tested and illustrated using real vibration feature data
from a fatigue-driven fault in a critical aircraft component.

2009

Abstract—A complex system such as an aircraft engine, is
composed of several components or subsystems which
interact with each other in several ways. These constituent
components/subsystems and their interactions make up the
whole system. When a fault condition arises in one of the
components, not only that component behavior changes but
the interaction of that component with the other system
constituents may also change. This might result in spreading
the effect of that fault to other components in a domino like
effect, until the overall system fails. This paper presents a
modular methodology to analyze propagation of faults from
one subsystem to the other subsystems. The application
domain focuses on an aero propulsion system of the
turbofan type.

Abstract—Complex engineering systems consist of many
subsystems. Each of the subsystems is composed
of a large number of components. While faults
arise at component level, sensing capabilities are
limited to subsystem level, and system operations
and maintenance practices are scheduled based
on system level paremeters. This paper presents
a hierarchical architecture to analyze the effects
of system level parameters on component level
faults of dominant failure modes of a complex
system. An aeropropulsion system of turbofan
type has been used as the application domain. In
most of the cases, engine life is limited due to
cracks in high-pressure turbine blades. In this paper,
it is assumed that creep is the only active failure
mechanism. Based on a finite-element model
of the turbine blades available in the open literature,
design of experiments (DoE) methodology
is used to build a subsystem-level model. A simulation
package of a commercial aircraft engine
is then used to obtain system-level results.

Abstract—In complex systems, there are few critical failure
modes. Prognostic models are focused at predicting the
evolution of those critical faults, assuming that other
subsystems in the same system are performing according to
their design specifications. In practice, however, all the
subsystems are undergoing deterioration that might accelerate
the time evolution of the critical fault mode. This paper aims at
analyzing this aspect, i.e. interaction between different fault
modes in various subsystems, of the failure prognostic
problem. The application domain focuses on an aero
propulsion system of the turbofan type. Creep in the highpressure
turbine blade is one of the most critical failure modes
of aircraft engines. The effects of health deterioration of lowpressure
compressor and high–pressure compressor on creep
damage of high-pressure turbine blades are investigated and
modeled.

  • Orchard, M., Tang, L., Goebel, K., and Vachtsevanos, G., “A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events,” First Annual Conference of the Prognostics and Health Management Society – PHM09, 2009, San Diego, CA, USA.
  • Brown, D., Georgoulas, G., Bole, B., Pei, H.L, Orchard, M., Tang, L., Saha, B., Saxena, A., Goebel, K., and Vachtsevanos, G., “Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators,” First Annual Conference of the Prognostics and Health Management Society – PHM09, 2009, San Diego, CA, USA.

2008

  • Abbas, M., Vachtsevanos, G., “A Fault Propagation Analysis Methodology Based on Energy Flow,” International Conference on Prognostics and Health Management 2008.

Abstract—Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

Abstract—This paper introduces a novel Prognostics-enhanced Automated Contingency Management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity Unmanned Aerial Vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircrafts.

Abstract-This endeavor introduces a novel methodology for the early detection of anomalies or faults with confidence in critical aircraft systems. The anomaly detector is based on particle filtering estimation techniques and is intended to recognize deviations from the nominal operation of a system or component. An appropriate baseline is defined first from available historical data and is expressed as a probability density function in the particle filtering framework. Real-time vibration signals and information regarding the system’s operational state are made available on-line and pre-processed to de-noise the data and extract useful features or condition indicators. Statistical analysis is performed on these features and the latter are compared, as they progress in time, with baseline statistics. The algorithm determines the probability of anomalous conditions, the probability of false alarms, and an indicator that reports the time instant of the presence of a fault or anomaly for a specified confidence level. A case study with actual experimental data derived from seeded fault testing is used to demonstrate the efficacy of the proposed approach.

Abstract—This paper introduces a methodology to detect as early as possible with specified degree of confidence and prescribed false alarm rate an anomaly or novelty (incipient failure) associated with critical components/subsystems of an engineered system that is configured to monitor continuously its health status. Innovative features of the enabling technologies include a Bayesian estimation framework, called particle filtering, that employs features or condition indicators derived from sensor data in combination with simple models of the system’s degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme provides the probability of abnormal condition and the probability of false alarm. The presence of an anomaly is confirmed for a given confidence level. The efficacy of the proposed anomaly detection architecture is illustrated with test data acquired from components typically found on aircraft and monitored via a test rig appropriately instrumented.

Abstract— Resolver sensors are utilized as absolute position transducers to control the position and speed of actuators in many flight critical applications where robustness accuracy and ability to operate in extreme environmental conditions are required. To ensure these requirements, several designs for self-diagnosing sensors were proposed in the past. However, such designs require modifications to the transducer itself. This paper proposes a new approach for real-time tracking of resolver faults with the ability to perform fault accommodation in the event of a fault. A formation based on the physical operating principles for a resolver sensor is provided. Diagnostic measures are identified for use in statistical-based fault-detection routines. Then, estimates for the resolver mismatch are tracked using a time-varying Kalman filter. Fault accommodation is achieved by applying the tracked estimates to adjust for the resolver mismatch. Finally, the fault detection and accommodation routines were evaluated in Simulink for an electro-mechanical actuator (EMA).

Abstract—In this paper, an anomaly detection structure, in which different types of anomaly detection routines can be applied, is proposed. Bearing fault modes and their effects on the bearing vibration are discussed. Based on this, a feature extraction method is developed to overcome the limitation of time domain features. Experimental data from bearings under different operating conditions are used to verify the proposed method. The results show that the extracted feature has a monotonic decrease trend as the dimension of fault increases. The feature also has the ability to compensate the variation of rotating speed. The proposed structure are verified with three different detection routines, pdf-based, k-nearest neighbor, and particle-filter-based approaches.

2007

Abstract – Fault diagnosis and failure prognosis are essential techniques to improve safety of many mechanical systems. However, vibration signals are often corrupted by noise and, therefore, the performance of diagnostic and prognostic routines is degraded. In this paper, a novel de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a de-noising algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.

Abstract – This paper introduces the design of an integrated framework for on-board fault diagnosis and failure prognosis of a helicopter transmission component, and describes briefly its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4) combine Bayesian estimation algorithms and measurements to detect and identify the fault and predict remaining useful life with specified confidence and minimum false alarms.

Abstract—Increasing demand for improved reliability and survivability of mission-critical systems is driving the development of health monitoring and Automated Contingency Management (ACM) systems. An ACM system is expected to adapt autonomously to fault conditions with the goal of still
achieving mission objectives by allowing some degradation in system performance within permissible limits. ACM performance depends on supporting technologies like sensors and anomaly detection, diagnostic/prognostic and reasoning algorithms. This paper presents the development of a generic prototype test bench software framework for developing and validating ACM systems for advanced propulsion systems called the Propulsion ACM (PACM) Test Bench. The architecture has been implemented for a Monopropellant Propulsion System (MPS) to demonstrate the validity of the approach. A Simulink model of the MPS has been developed along with a fault injection module. It has been shown that the ACM system is capable of mitigating the failures by searching for an optimal strategy. Furthermore, few relevant experiments have been presented to show proof of concepts.

Abstract—Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition Based Maintenance (CBM) requires that the health of critical components/systems be monitored and diagnositics/prognostic strategies be developed to detect and identify incipient failures and predict the failing component’s remaining useful life (RUL). Typically, vibration and other key indicators on-board on aircraft are severely corrupted by noise thus curtailing our ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution de-noising scheme that employs vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed scheme.

Abstract – Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for monitoring, modeling, data processing, fault diagnosis, and failure prognosis of critical electrical components such as the battery. The enabling technologies include signal processing, sensor selection and placement, selection and extraction of optimum condition indicators, and accurate fault diagnosis and failure prognosis algorithms that are based on both the physics of failure models and Bayesian estimation methods. The proposed architecture is implementable on-board an Electronic Control Unit (ECU) requiring minimum computational resources. Potential benefits include reduction in maintenance costs, improved asset reliability and availability and longer life of critical components.

  • Ginart, A., Brown, D., Kalgren, P., Roemer, M., “On-line Ringing Characterization as PHM Technique for Power Drive and Electrical Machinery,” AUTOTESTCON 2007, September 17-20, 2007.

Abstract – Embeddable features easily incorporated in traditional power drive systems are identified for prognostic health management (PHM) systems. This novel technique utilizes the original PWM waveform produced by the inverter to evaluate the power electronic circuit and electric machine against transistor degradation. Evaluation of the primary feature, ringing characterization, with experimental data demonstrates its viability as a practical real-time power device health-state indicator.

  • Patrick, R., Ferri, A., Vachtsevanos, G., “Detection of Carrier-Plate Cracks Using Vibration Spectra”, ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2007 September 4-7, 2007, Las Vegas, Nevada, USA

Abstract – This paper examines the problem of identifying cracks in planetary gear systems through use of vibration sensors on the stationary gearbox housing. In particular, the effect of unequal spacing of planet gears relative to the rotating carrier plate on various frequency components in the vibration spectra is studied. The mathematical analysis is validated with experimental data comparing the vibration signature of helicopter transmissions operating either normally or with damage leading to shifts in the planet gear positions. The theory presented is able to explain certain features and trends in the measured vibration signals of healthy and faulty transmissions. The characterization offered may serve as a means of detecting damage in planetary gear systems.

2006

  • Orsagh, R., Brown, D., Roemer, M. Dabnev, T. Hess, A., “Prognostic Health Management for Avionic Systems,” IEEE Aerospace Conference, March 11-14, 2006.

Abstract – This paper presents an integrated approach to switching mode power supply health management that implements techniques from engineering disciplines including statistical reliability modeling, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Novel features extracted from sensed parameters such as temperature, power quality, and efficiency were analyzed using advanced fault detection and damage accumulation algorithms. Using model-based assessments in the absence of fault indications, and updating the model-based assessments with sensed information when it becomes available provides health state awareness at any point in time. Intelligent fusion of this diagnostic information with historical component reliability statistics provides a robust health state awareness as the basis for accurate prognostic predictions. Complementary prognostic techniques including analysis of projected operating conditions by physics-based component aging models, empirical (trending) models, and system level failure progression models will be used to develop verifiable prognostic models. The diagnostic techniques, and prognostic models have been demonstrated through accelerated failure testing of switching mode power supplies.

  • Brown, D., Kalgren, P., Roemer, M., “Electronic Prognostics – A Case Study Switching Mode Power Supplies (SMPS),” AUTOTESTCON 2006, September 18-21, 2006.

Abstract – Increased awareness of potential cost savings and improved reliability associated with condition based maintenance (CBM) for avionic systems has generated interest in the research and development of novel electronic prognostic and health management (PHM) solutions. This paper describes the process, with related examples, used to develop prognostics algorithms for a commercially available switched-mode power supply (SMPS) using corroborative evidence sources. The process begins with a Pareto analysis indicating the primary modes of failure. Critical components are identified using a three-tier failure mode and effects analysis (FMEA) by investigating device, circuit, and system parameters sensitive to degradation. Once acceleration factors, or sources of degradation, are known damage accumulation failure models for each critical component are derived from highly accelerated life tests (HALT). Then, healthy components are systematically degraded to varying levels of severity by performing highly accelerated stress testing (HAST). These components are used in seeded fault tests to identify system- level parameters sensitive to device damage. Features extracted from data recorded during seeded fault tests are used to derive feature- based failure models. Finally, reasoning and data fusion algorithms are applied to both models to generate corroborative remaining useful life (RUL) predictions.

Abstract—Automated Contingency Management (ACM), or the ability to confidently and autonomously adapt to fault and/or contingency conditions with the goal of still achieving mission objectives, can be considered the ultimate technological goal of a health management system. To establish confidence on the ACM system, objective performance evaluations should be executed. The need for verification and validation (V&V) techniques for ACM has also been specifically identified by DOD agencies and within the NASA community recently. This paper presents a general process and related techniques for developing and validating ACM systems for advanced propulsion systems. A novel ACM modeling paradigm, optimization-based ACM strategies, V&V approaches and performance metrics are developed. While some well-established formal methods such as model checking techniques are applicable to some sub-problems, this research has been more focused on innovative informal methods that attempt to address ACM performance requirements, optimality, robustness, etc. A pressure fed, monopropellant propulsion system for a small space flight vehicle is utilized as initial proof-of-concept implementation for the proposed techniques and preliminary simulation results are presented.

2005

  • Brown, D., Kalgren, P., Byington, C., Orsagh, R., “Electronic Prognostics – A Case Study Using Global Positioning System (GPS),” AUTOTESTCON 2005, September 26-29, 2005. (Best Paper Award)

Abstract – Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements have spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radio frequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified prognostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a commercial off the shelf (COTS) GPS receiver to within ±5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio.

  • Orsagh, R., Brown, D., Kalgren, P., “Looking past BIT – incipient fault detection and prognostics for avionics system power supplies,” AUTOTESTCON 2005, September 26-29, 2005.

Abstract – This paper presents an approach to enhancing current avionic system power supply diagnostics with prognostic techniques for improved equipment health management. The approach integrates techniques from engineering disciplines including automated testing, incipient fault detection and classification, fault to failure progression modeling, statistical reliability analysis, and automated reasoning. Novel features extracted from sensed parameters such as power quality, component operating temperature, control loop signature, and efficiency are analyzed using advanced fault detection and damage accumulation algorithms. Intelligent fusion of this diagnostic information with historical component reliability statistics provides a robust health state awareness as the basis for accurate prognostic predictions. Complementary prognostic techniques including analysis of projected operating conditions by physics-based component aging models and system level failure progression models are used to develop predictions of future equipment health. The diagnostic techniques and prognostic models have been demonstrated through accelerated failure testing of switching mode power supplies.

  • Khawaja, T., Vachtsevanos, G. and Wu, B., “Reasoning about Uncertainty in Prognosis: A Confidence Prediction Neural Network Approach”, Proceedings of NAFIPS ’05 Conference, North American Fuzzy Information Processing Society, (invited), Ann Arbor, Michigan, June 22-25, 2005.

  • Saxena, A., Wu, B., and Vachtsevanos, G., “Integrated Diagnosis and Prognosis Architecture for Fleet Vehicles Using Dynamic Case Based Reasoning”, Proceedings of IEEE Autotestcon ’05 Conference, Orlando, FL, pp. 96-104, September 25-30, 2005.

  • Saxena, A., Wu, B., and Vachtsevanos, G., “Processing Textual Information from Industrial Systems Using Semantic Networks”, 2nd Indian International Conferences on Artificial Intelligence (IICAI 05), Pune, India, December 20-22, 2005.

  • Tang, L., Kacprzynski, G., Roemer, M., Vachtsevanos, G., Patterson-Hine, A., “Automated Contingency Management Design for Advanced Propulsion Systems,” Infotech@Aerospace, Arlington, Virginia. 26 – 29 September 2005.

Patents and Contracts

List of Patents.
List of Grants and Contracts.

Biomedical Engineering/Neurotechnology/Cardiotechnology/Sleep Research

Journal Papers

2006

  • Wiggins, M., Gerstenfeld, E., Vachtsevanos, G., and Litt, B., “Electrogram Features are Superior to Clinical Characteristics for Predicting Atrial Fibrillation After Coronary Artery Bypass Graft Surgery”, Journal of the American College of Cardiology (JACC), Vol. 47, No. 4 (supplement A), pp. 12A-13A, February 21, 2006.

2005

  • Wiggins, M., Saad, A., Litt, B., and Vachtsevanos, G., “Evolving a Bayesian Classifer for ECG-based Age Classification in Medication Applications, Journal of Artificial Intelligence in Medicine, Elsevier Publishers, submitted August 31, 2005.

2003

  • Wiggins, M., Zhao, L., Vachtsevanos, G. and Litt, B., “Non-Invasive Cardiac Risk Stratification Using Wavelet Coefficients”, WSEAS Transactions on Computers, Issue 3, Vol. 2, pp. 720-723, July 2003.

2001

  • Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M. and Vachtsevanos, G., “Epileptic Seizures May Begin Hours in Advance of Clinical Onset: A Report of Five Patients,” Neuron, Vol. 30, pp. 51-64, April, 2001.

Conference Papers

2007

Abstract – Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome in adults. As it is a focal disorder, i.e. limited to a particular area of the brain, it is often curable through surgery when drug therapies fail to control seizures. In order to ensure successful surgical outcome, the abnormal brain regions must be localized as accurately as possible to prevent the removal of healthy tissue. We report on a novel voxel-based procedure for discrimination between normal and epileptogenic brain tissue through feature analysis of continuous arterial spin labeling (ASL) perfusion functional magnetic resonance imaging (fMRI) data. Five TLE patients and three healthy controls were studied. Features were extracted from the fMRI time series of each subject to determine which individual features and combinations of features could correctly separate
epileptogenic and normal brain tissue.

Abstract – The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance and computational complexities have been studied previously. This paper compares the performance of classical sequential methods, a floating search method, and the “globally optimal” branch and bound
algorithm when applied to functional MRI and intracranial EEG to classify pathological events. We find that the sequential floating forward technique outperforms the other methodologies for these particular data. Previous works have found branch and bound to be a superior feature subset selection technique; however, in this application, the branch and bound algorithm fails to create subsets with better classification accuracy.

2006

Abstract – The extreme artifact module, a component of a three class (extreme, movement, and eye)
artifact removal system is presented. Extreme artifacts are characterized by polysomnographic
data segments which are so severely corrupted by artifact that no useful information may be
recovered.

2004

ABSTRACT
Image analysis is a crucial step in processing microarray data generated by gene expression studies, which have
been used extensively in understanding the molecular mechanisms of injury and recovery. A novel image analysis
method utilizing an efficient snake-based multichannel image segmentation algorithm is proposed to analyze the
microarray data. The preliminary experimental results show that this method achieved very high segmentation
accuracy, which guarantees accurate feature measurements.

Patents and Contracts

List of Patents.
List of Grants and Contracts.

Unmanned Aerial Vehicles

Books and Book Chapters

2005

  • Reimann, J. and Vachtsevanos, G., “Computational Methods in Pursuit-Evasion Problems”, Lecture Series on Computer and Computational Sciences, Volume 4, pp. 487 – 491, 2005.

2004

  • Vachtsevanos, G., Rufus, F., Prasad, J.V.R., Yavrucuk, I., Schrage, D., Heck, B. and Wills, L., “An Intelligent Methodology for Real-time Adaptive Mode Transitioning and Limit Avoidance of Unmanned Aerial Vehicles,” Software-Enabled Control: Information Technologies for Dynamical Systems, Samad, T. and Balas, G., Eds.), IEEE Press, pp. 225-252, April 2003.

Journal Papers

2007

  • Ludington, B., Reimann, J., and Vachtsevanos, G., “Target Tracking and Adversarial Reasoning for Unmanned Aerial Vehicles”, IEEE Aerospace Conference, Big Sky, Montana, 2007.

  • P. Jones and G. Vachtsevanos, Multi-Unmanned Aerial Vehicle Coverage Planner for Area Surveillance Missions, Proceedings of the AIAA Guidance Navigation and Control Conference, 2007.

  • P. Jones, B. Ludington, J. Reimann, G. Vachtsevanos, Intelligent Control of Unmanned Aerial Vehicles for Improved Autonomy, European Control Conference, 2007.

2006

  • Vachtsevanos, G. and Ludington, B. “Unmanned Aerial Vehicles: Challenges and Technologies for Improved Autonomy”, WSEAS Transactions on Systems, Issue 9, Volume 5, pp. 2164 – 2171, September 2006.

  • Reimann, J. and Vachtsevanos, G., “UAVs in urban operations: Target Interception and Containment”. Journal of Intelligent and Robotic Systems, Springer, p. 383-396, 2006.

  • Jianhua Ge, Liang Tang, Reimann, J., and Vachtsevanos, G.,”Hierarchical decomposition approach for pursuit-evasion differential game with multiple players” Aerospace Conference, 2006 IEEE 4-11 March 2006

  • Ludington, B., Johnson, E., and Vachtsevanos, G., “Augmenting UAV Autonomy: Vision-Based Navigation and Target Tracking for Unmanned Aerial Vehicles,” IEEE Robotics and Automation Magazine, vol. 13, issue. 3, 2006, pp 63-71.

  • Vachtsevanos, G. and Ludington, B. “Unmanned Aerial Vehicles: Challenges and Technologies for Improved Autonomy”, WSEAS Transactions on Systems, Issue 9, Volume 5, pp. 2164 – 2171, September 2006.

2005

  • Vachtsevanos, G., Tang, L., Drozeski, G., and Gutierrez, L., “From Mission Planning to Flight Control of Unmanned Aerial Vehicles: Strategies and Implementation Tools”, Annual Reviews in Control, Vol. 29, No 1, pp. 101-115, April 2005.

Abstract-This paper reviews aspects of unmanned aerial vehicle (UAV) autonomy as suggested by the Autonomous Control Logic chart of the U.S. DoD UAV autonomy roadmap; levels of vehicle autonomy addressed through intelligent control practices and a hierarchical/intelligent control architecture are presented for UAVs. Basic modules of the control hierarchy and their enabling technologies are reviewed; of special interest, from an intelligent control perspective, are the middle and high echelons of the hierarchy. Here, mission planning, trajectory generation and vehicle navigation routines are proposed for the highest level. At the middle level, the control role is portrayed by mode transitioning, envelope protection, real-time adaptation and fault detection/control reconfiguration algorithms which are intended to safeguard the UAV’s integrity in the event of component failures, extreme operating conditions or external disturbances. The UAV thus exhibits
attributes of robustness and operational reliability assuring a satisfactory degree of autonomy. The control technologies are demonstrated through flight testing results.

  • Reimann, J. and Vachtsevanos, G., “Computational Methods in Pursuit-Evasion Problems”, Lecture Series on Computer and Computational Sciences, Brill Academic Publishers, Vol. 1, pp. 1-3, 2005.

  • Reimann, J. and Vachtsevanos, G., “UAVs in urban operations: Target Interception and Containment,” Accepted for publication in the Journal of Intelligent and Robotic Systems, Springer.

Conference Papers

2006

  • Reimann, J., Vachtsevanos, G., Ge, J., and Tang, L., “An Approach to controlling swarms of unmanned aerial vehicles in adversarial situations,” Paper presented at the AIAA Guidance, Navigation and Control Conference and Exhibit, Colorado, USA, 2006.
  • Ge, J., Tang, L., Reimann, J., and Vachtsevanos, G., “Suboptimal approaches to multiplayer pursuit-evasion differential games,” Paper presented at the AIAA Guidance, Navigation and Control Conference and Exhibit, Colorado, USA, 2006.

  • Ludington, B., Reimann, J., Barlas, I., and Vachtsevanos, G., “Target Tracking with Unmanned Aerial Vehicles: From Single to Swarm Vehicle Autonomy and Intelligence,” in Proceedings of the 14th Mediterranean Conference on Control and Automation, Ancona, Italy, 2006.

2005

  • Hegazy, T., Ludington, B., and Vachtsevanos, G., “Reconnaissance and Surveillance in Urban Terrain with Unmanned Aerial Vehicles,” in Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, 2005.

  • Ludington, B., Tang, L., and Vachtsevanos, G., “Reconnaissance and Surveillance in Urban Terrain with Unmanned Aerial Vehicles,” in Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, 2005.

  • Drozeski, G. and Vachtsevanos, G., “A Fault-Tolerant Architecture with Reconfigurable Path Planning Applied to an Unmanned Rotorcraft”, Proceedings of the American Helicopter Society 61st Annual Forum, Grapevine, TX, June 1-3, 2005.

  • Hegazy,T., Ludington, B. and Vachtsevanos, G., “Reconnaissance and Surveillance in Urban Terrain with Unmanned Aerial Vehicles”, Proceedings of 16th IFAC World Congress, Prague, Czech Republic, July 4-8, 2005.

  • Drozeski, G., Saha, B. and Vachtsevanos, G., “A Fault-Tolerant Architecture for an Unmanned Rotorcraft”, Proceedings of the AHS nternational Specialists’ Meeting on Unmanned Rotorcraft, Design, Control and Testing, Chandler AZ, January 18-20, 2005.

  • Drozeski, G., Saha, B., and Vachtsevanos, G., “A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles”, Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 5-12, 2005.

  • Ludington, B., Tang, L. and Vachtsevanos, G., “Target Tracking in an Urban Environment Using Particle Filters”, Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 5-12, 2005.

  • Vachtsevanos, G., Ludington, B., Reimann, J., Antsaklis, P. and Valavanis, K., “Modeling and Control of Unmanned Aerial Vehicles – Current Status and Future Directions,” in Proceedings of the Workshop on Modeling and Control of Complex Systems, Los, Cyprus, 2005.

  • Johnson, E., Schrage, D. and Vachtsevanos, G., “Software Enabled Control Experiments with University-Operated Unmanned Aircraft”, “UAVs in Academia”, Invited session of the AIAA Infotech@Aerospace Conference, September 26 – 29, 2005.

2004

  • Vachtsevanos, G., Tang, L., and Reimann, J., “An Intelligent Approach to Coordinated Control of Multiple Unmanned Aerial Vehicles,” Presented at the American Helicopter Society 60th Annual Forum, Baltimore, MD, USA, 2004.

Patents and Contracts

List of Patents.
List of Grants and Contracts.

Micro Aerial Vehicles

Journal Papers

2010

Ratti, J., Vachtsevanos, G., “A Biologically – Inspired Micro Aerial Vehicle: Sensing, Modeling and Control Strategies,” Journal of Intelligent & Robotic Systems, 60(1):153–178, 2010.

Conference Papers

2010

Ratti, J., Goel, R., Kim, S., Moon, J., Pappas, T., Vachtsevanos, G., Roemer. M., “Bio-Inspired Micro Air Vehicle: Design and Control Issues,” In AIAA Infotech@Aerospace, 2010.