Optimization algorithms for prognostics of electrohydraulic on-board servomechanisms

This paper studies the response of an electrohydraulic actuator (EHA) subjected to three different progressive failures (demagnetization of the torque motor, increment of the jack static friction and presence of backlash); in particular, it is focused on the identification of failure precursors able to give an early identification of progressive failures affecting the system, in order to provide tools that can be used to predict its remaining useful life. This kind of analysis belongs to a new discipline, called Prognostics and Health Management (PHM), that focuses on predicting the time at which a system or a component will no longer perform its intended function, estimating its Remaining Useful Life (RUL) and, then, providing an effective diagnostic tool that allows them to exploit a component until it is safe, saving money. In order to conceive an effective prognostic algorithm authors studied the failures effects on the system behaviors, identifying some details in the monitored time-history signals that exclusively got evidence of a particular failure, avoiding confounding each other and allowing pointing out the fault level of the system. For this purpose, the authors developed a new EHA Monitor Model able to reproduce the dynamic response of the actual system in terms of position, speed and equivalent current, even with the presence of incipient faults. Starting from this Monitor Model, the authors propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms (GAs) optimization approach and parallelized calculations, investigating its ability to timely identify symptoms alerting that a component is degrading

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Aerospace Medicine and Biology. A continuing bibliography with indexes (supplement 225)

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1981

Fault Detection and Identification Method Based on Genetic Algorithms to Monitor Degradation of Electrohydraulic Servomechanisms

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 274)

This bibliography lists 128 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1985

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 144

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975

Study of Parameter Estimation and Model Calibration Using Bayesian Analysis of Noisy Data for a Virus Model

Numerous engineering problems are concerned with the challenge of representing real life systems through mathematical equations: modeling. Properly generated mathematical models can accurately predict the behavior of natural processes. The key objective of model development is to correctly build a set of equations or expressions that can reproduce the results observed from experimental measurements. By following this methodology, sources of error and uncertainty will arise as most natural processes have random factors that make the results stochastic, and therefore can never be exactly reproduced. A model can try to best approximate the actual outcome, but many times assumptions or simplifications are needed because either the problem becomes mathematically unfeasible, or there is not enough knowledge regarding the process. Additionally, even if models are correctly defined, they may require proper calibration of its parameters to make predictions.;In the study of virology, within host viral infections can be modeled by means of mathematical balances of target cell populations. A virus model will describe how a virus infects healthy cells and spreads by defining a set of depletion/replenishment rate parameters that will depend on each system. The focus of this study is to determine the posterior probability distributions of these parameters that will best approximate a given patient\u27s data, similar to data fitting. Using an inverse modeling approach to generate patient data using known reference values of the virus model parameters and adding random white noise, a virus model will be fitted to the generated noisy data using Bayesian methods for parameter estimation.;The main purpose of this study is to validate the use of Bayesian calibration techniques as an alternative to conventional gradient-based parameter estimation methods. The results of a calibrated virus model with a fixed virus generation rate are then used to make model predictions and extrapolate the dynamic behavior to different ranges of the fixed parameter. The results conclude that Bayesian methods can be successfully used for parameter estimation, especially for high-dimensional problems, however the practical identifiability of the parameters is limited by the model\u27s nonlinear terms, the experimental data variance, and the available data measurements. Although the results are encouraging, the excessive computation time needed for obtaining the empirical parameter Posterior distributions limit the practical use of these methods

Aerospace Medicine and Biology: A cumulative index to the 1981 issues

The aeromedical research reported considers the safety of the human component in manned space flight. The effects of spacecraft environment, radiation and weightlessness on human biological and psychological processes are covered