36,234 research outputs found
A distributed networked approach for fault detection of large-scale systems
Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique
Artificial Neural Network-based error compensation procedure for low-cost encoders
An Artificial Neural Network-based error compensation method is proposed for
improving the accuracy of resolver-based 16-bit encoders by compensating for
their respective systematic error profiles. The error compensation procedure,
for a particular encoder, involves obtaining its error profile by calibrating
it on a precision rotary table, training the neural network by using a part of
this data and then determining the corrected encoder angle by subtracting the
ANN-predicted error from the measured value of the encoder angle. Since it is
not guaranteed that all the resolvers will have exactly similar error profiles
because of the inherent differences in their construction on a micro scale, the
ANN has been trained on one error profile at a time and the corresponding
weight file is then used only for compensating the systematic error of this
particular encoder. The systematic nature of the error profile for each of the
encoders has also been validated by repeated calibration of the encoders over a
period of time and it was found that the error profiles of a particular encoder
recorded at different epochs show near reproducible behavior. The ANN-based
error compensation procedure has been implemented for 4 encoders by training
the ANN with their respective error profiles and the results indicate that the
accuracy of encoders can be improved by nearly an order of magnitude from
quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding
ANN-generated weight files are used for determining the corrected encoder
angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science
and Technology (MST
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever
increasing demand for computational resources for their simulation.
Neuromorphic devices offer a number of advantages over conventional computing
architectures, such as high emulation speed or low power consumption, but this
usually comes at the price of reduced configurability and precision. In this
article, we investigate the consequences of several such factors that are
common to neuromorphic devices, more specifically limited hardware resources,
limited parameter configurability and parameter variations. Our final aim is to
provide an array of methods for coping with such inevitable distortion
mechanisms. As a platform for testing our proposed strategies, we use an
executable system specification (ESS) of the BrainScaleS neuromorphic system,
which has been designed as a universal emulation back-end for neuroscientific
modeling. We address the most essential limitations of this device in detail
and study their effects on three prototypical benchmark network models within a
well-defined, systematic workflow. For each network model, we start by defining
quantifiable functionality measures by which we then assess the effects of
typical hardware-specific distortion mechanisms, both in idealized software
simulations and on the ESS. For those effects that cause unacceptable
deviations from the original network dynamics, we suggest generic compensation
mechanisms and demonstrate their effectiveness. Both the suggested workflow and
the investigated compensation mechanisms are largely back-end independent and
do not require additional hardware configurability beyond the one required to
emulate the benchmark networks in the first place. We hereby provide a generic
methodological environment for configurable neuromorphic devices that are
targeted at emulating large-scale, functional neural networks
Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem
The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design
Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems
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Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
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