Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMM-based approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method

Aeronautical engineering: A continuing bibliography with indexes (supplement 217)

This bibliography lists 450 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1987

Degradation Modeling and Remaining Useful Life Estimation: From Statistical Signal Processing to Deep Learning Models

Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge, or the Gulf of Mexico oil spill disaster, call for an urgent quest to design advanced and innovative prognostic solutions, and efficiently incorporate multi-sensor streaming data sources for industrial development. Prognostic health management (PHM) is among the most critical disciplines that employs the advancement of the great interdependency between signal processing and machine learning techniques to form a key enabling technology to cope with maintenance development tasks of complex industrial and safety-critical systems. Recent advancements in predictive analytics have empowered the PHM paradigm to move from the traditional condition-based monitoring solutions and preventive maintenance programs to predictive maintenance to provide an early warning of failure, in several domains ranging from manufacturing and industrial systems to transportation and aerospace. The focus of the PHM is centered on two core dimensions; the first is taking into account the behavior and the evolution over time of a fault once it occurs, while the second one aims at estimating/predicting the remaining useful life (RUL) during which a device can perform its intended function. The first dimension is the degradation that is usually determined by a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a primary objective in prognosis and health management. Extensive research has been conducted to develop new theories and methodologies for degradation modeling and to accurately capture the degradation dynamics of a system. However, a unified degradation framework has yet not been developed due to: (i) structural uncertainties in the state dynamics of the system and (ii) the complex nature of the degradation process that is often non-linear and difficult to model statistically. Thus even for a single system, there is no consensus on the best degradation model. In this regard, this thesis tries to bridge this gap by proposing a general model that able to model the true degradation path without having any prior knowledge of the true degradation model of the system. Modeling and analysis of degradation behavior lead us to RUL estimation, which is the second dimension of the PHM and the second part of the thesis. The RUL is the main pillar of preventive maintenance, which is the time a machine is expected to work before requiring repair or replacement. Effective and accurate RUL estimation can avoid catastrophic failures, maximize operational availability, and consequently reduce maintenance costs. The RUL estimation is, therefore, of paramount importance and has gained significant attention for its importance to improve systems health management in complex fields including automotive, nuclear, chemical, and aerospace industries to name but a few. A vast number of researches related to different approaches to the concept of remaining useful life have been proposed, and they can be divided into three broad categories: (i) Physics-based; (ii) Data-driven, and; (iii) Hybrid approaches (multiple-model). Each category has its own limitations and issues, such as, hardly adapt to different prognostic applications, in the first one, and accuracy degradation issues, in the second one, because of the deviation of the learned models from the real behavior of the system. In addition to hardly sustain good generalization. Our thesis belongs to the third category, as it is the most promising category, in particular, the new hybrid models, on basis of two different architectures of deep neural networks, which have great potentials to tackle complex prognostic issues associated with systems with complex and unknown degradation processes

Aeronautical engineering: A continuing bibliography with indexes (supplement 211)

A continuing bibliography (NASA SP-7037) lists 519 reports, journal articles and other documents originally announced in February 1987 in Scientific and Technical Aerospace Reports (STAR) or in the International Aerospace Abstracts (IAA). The coverage includes documents on the engineering and theoretical aspect of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems. It also includes research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles. Each entry in the bibliography consists of a standard bibliographic citation accompanied in most cases by an abstract. The listing of the entries is arranged by the first nine STAR specific categories and the remaining STAR major categories. The arrangement offers the user the most advantageous breakdown for individual objectives. The citations include the original accession numbers from the respective announcement journals. The IAA items will precede the STAR items within each category. Seven indexes entitled subject, personal author, corporate source, foreign technology, contract number, report number, and accession number are included

Guaranteed methods based on constrained zonotopes for set-valued state estimation of nonlinear discrete-time systems

This paper presents new methods for set-valued state estimation of nonlinear discrete-time systems with unknown-but-bounded uncertainties. A single time step involves propagating an enclosure of the system states through the nonlinear dynamics (prediction), and then enclosing the intersection of this set with a bounded-error measurement (update). When these enclosures are represented by simple sets such as intervals, ellipsoids, parallelotopes, and zonotopes, certain set operations can be very conservative. Yet, using general convex polytopes is much more computationally demanding. To address this, this paper presents two new methods, a mean value extension and a first-order Taylor extension, for efficiently propagating constrained zonotopes through nonlinear mappings. These extend existing methods for zonotopes in a consistent way. Examples show that these extensions yield tighter prediction enclosures than zonotopic estimation methods, while largely retaining the computational benefits of zonotopes. Moreover, they enable tighter update enclosures because constrained zonotopes can represent intersections much more accurately than zonotopes.Comment: This includes the supplement "Supplementary material for: Guaranteed methods based on constrained zonotopes for set-valued state estimation of nonlinear discrete-time systems

Aeronautical engineering: A continuing bibliography with indexes (supplement 210)

This bibliography lists 409 reports, articles and other documents introduced into the NASA scientific and technical information system in January 1987