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ABSTRACT: This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which 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 requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.
IEEE Transactions on Industrial Electronics 06/2011; · 5.16 Impact Factor
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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.
AUTOTESTCON, 2010 IEEE; 10/2010
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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.
IEEE Transactions on Instrumentation and Measurement 03/2009; · 1.21 Impact Factor
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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.
Autotestcon, 2007 IEEE; 10/2007
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ABSTRACT: This paper presents the implementation of an online particle-filtering-based framework for fault diagnosis and failure prognosis in a turbine engine. The methodology considers two autonomous modules, and assumes the existence of fault indicators (for monitoring purposes) and the availability of real-time measurements. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant, and a particle filtering algorithm to calculate the probability of a crack in one of the blades of the turbine; simultaneously computing the state probability density function (pdf) estimates that will be used as initial conditions in the prognosis module. The failure prognosis module, on the other hand, computes the remaining useful life (RUL) pdf of the faulty subsystem in real-time, using a particle-filtering-based algorithm that consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters), and predicts the evolution in time of the probability distribution for the crack length. The outcome of the prognosis module provides information about precision and accuracy of long-term predictions, RUL expectations and 95% confidence intervals for the failure condition under study. Data from a seeded fault test is used to validate the proposed approaches.
Control & Automation, 2007. MED '07. Mediterranean Conference on; 07/2007
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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.
Intelligent Vehicles Symposium, 2007 IEEE; 07/2007
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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.
Aerospace Conference, 2007 IEEE; 04/2007
