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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2008

Sensitivity Study for Long Term Reliability

This paper illustrates using Markov models to establish system and maintenance requirements for small electronic controllers where the goal is a high probability of continuous service for a long period of time. The system and maintenance items considered are quality of components, various degrees of simple redundancy, redundancy with reconfiguration, diagnostic levels, periodic maintenance, and preventive maintenance. Markov models permit a quantitative investigation with comparison and contrast. An element of special interest is the use of conditional probability to study the combination of imperfect diagnostics and periodic maintenance.

White, Allan L.↗

NASA NTRS2020

Deep Learning Models for Planetary Seismicity Detection

Research in planetary seismology is fundamentally constrained by a lack of data. Seismo-logical science products of future missions can typically only be informed by theoretical signal/noise characteristics of the environment or likely Earth-analogues. Although objectives can be re-assessed after some initial data-collection upon lander arrival, transfer of high-resolution data back to Earth is costly on lander power usage. Over the last several years, development of GPU computing techniques and open-source high-level APIs have led to rapid advances in deep learning within the fields of computer vision, natural language processing, and collaborative filtering. These techniques are actively being adapted in seismology for a variety of tasks, including: earthquake detection, seismic phase discrimination, and ground-motion prediction. Until the recent detection of mars quakes during the Mars InSight mission, the only other measurements of seismicity recorded outside of Earth was on the Moon during the Apollo missions between 1969 to 1977. These unique data sets have been periodically revisited using new seismological methods, including ambient noise interferometry and Hidden Markov Models. Our objective is to develop a deep learning seismic detector and use it to catalog moonquakes from the Apollo 17 Lunar Seismic Profiling Experiment (LSPE) and compare the results with those obtained by other methods. Additionally, we will assess the accuracy tradeoff between using a training set of lunar data and one composed of Earth seismicity. In this document, we present preliminary results using a prototype classifier trained on a small set of earthquakes that was able to obtain detections for LSPE moonquakes with a greater accuracy than a recent study using Hidden Markov Models.

Civilini, F.↗

DOE OSTI2020

Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective

Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗

DOE OSTI2023

Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements

Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.

42 ENGINEERING↗

NASA NTRS1984

An approximation formula for a class of Markov reliability models

A way of considering a small but often used class of reliability model and approximating algebraically the systems reliability is shown. The models considered are appropriate for redundant reconfigurable digital control systems that operate for a short period of time without maintenance, and for such systems the method gives a formula in terms of component fault rates, system recovery rates, and system operating time.

White, A. L.↗

NASA NTRS1984

Optimal inventories for overhaul of repairable redundant systems - A Markov decision model

A Markovian decision model was developed to calculate the optimal inventory of repairable spare parts for an avionics control system for commercial aircraft. Total expected shortage costs, repair costs, and holding costs are minimized for a machine containing a single system of redundant parts. Transition probabilities are calculated for each repair state and repair rate, and optimal spare parts inventory and repair strategies are determined through linear programming. The linear programming solutions are given in a table.

Schaefer, M. K.↗

NASA NTRS1985

Synthetic bounds for semi-Markov reliability models

Upper and lower bounds are derived for the probability of failure for a class of highly reliable process control computers. The bounds are synthetic in the sense that the descriptions of component failure and system recovery are assumed to be obtained from different sources. The reliability model is constructed under the assumption that the processes are independent.

White, A. L.↗

NASA NTRS1985

Markov-Tree model of intrinsic transport in Hamiltonian systems

A particle in a chaotic region of phase space can spend a long time near the boundary of a regular region since transport there is slow. This 'stickiness' of regular regions is thought to be responsible for previous observations in numerical experiments of a long-time algebraic decay of the particle survival probability, i.e., survival probability approximately t to the (-z) power for large t. This paper presents a global model for transport in such systems and demonstrates the essential role of the infinite hierarchy of small islands interspersed in the chaotic region. Results for z are discussed.

Meiss, J. D.↗

NASA NTRS2010

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗

DOE OSTI2020

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

behind-the-meter solar generation↗

DOE OSTI2023

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source’s infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model—which make use of different information within a tree—suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1993

Markov Monitoring with Unknown States

Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is difficult if not impossible to model all the possible fault states of the system in advance.

Markov models↗

NASA NTRS2004

Statistical analysis of geodetic networks for detecting regional events

We present an application of hidden Markov models (HMMs) to analysis of geodetic time series in Southern California. Our model fitting method uses a regularized version of the deterministic annealing expectation-maximization algorithm to ensure that model solutions are both robust and of high quality.

geodetic networks↗

NASA NTRS1984

A user's view of CARE III

The present computerized reliability predictor for digital fault-tolerant systems whose sizes are of the order of one million Markovian equivalent states, employs advanced stochastic modeling techniques and implements a mixed Markov model that enables it to drastically reduce the state size of hitherto computationally unobtainable models. Attention is given to the concepts of failure, fault, and error, in the context of the novel system's fault/error-handling models. Examples are drawn from the system's user-friendly interface dialog.

Bavuso, S. J.↗

NASA NTRS2012

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

Barlow, Jonathan↗

NASA NTRS1988

Reliability analysis of large, complex systems using ASSIST

The SURE reliability analysis program is discussed as well as the ASSIST model generation program. It is found that semi-Markov modeling using model reduction strategies with the ASSIST program can be used to accurately solve problems at least as complex as other reliability analysis tools can solve. Moreover, semi-Markov analysis provides the flexibility needed for modeling realistic fault-tolerant systems.

Johnson, Sally C.↗