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1,855 results for “Framework”
An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis
Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents a novel inte- grated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automati- cally constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.
An Integrated Model-Based Distributed Diagnosis and Prognosis Framework
Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi- tion is used to perform the diagnosis and prog- nosis tasks in a distributed way. We show how different submodels can be automatically con- structed to solve the local diagnosis and prog- nosis problems. We illustrate our approach us- ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.
A Structural Model Decomposition Framework for Systems Health Management
Systems health management (SHM) is an impor- tant set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL predic- tion. We solve these model decomposition problems using a three-tank system as a case study.
Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions
Electrolytic capacitors are used in several applications rang- ing from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past expe- riences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress condi- tions during operations. This makes them good candidates for prognostics and health management. Model-based prognos- tics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation mod- els for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.
An Energy-Based Prognostic Framework to Predict Fatigue Damage Evolution in Composites
In this work, a prognostics framework to predict the evolution of damage in fiber-reinforced composites materials under fatigue loads is proposed. The assessment of internal damage thresholds is a challenge for fatigue prognostics in composites due to inherent uncertainties, existence of multiple damage modes, and their complex interactions. Our framework, considers predicting the balance of release strain energies from competing damage modes to establish a reference threshold for prognostics. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in carbon-fiber-reinforced polymer (CFRP) cross-ply laminates. Results are presented for the prediction of expected degradation by micro-cracks for a given panel with the associated uncertainty estimates.
Optimizing Battery Life for Electric UAVs using a Bayesian Framework
In summary, this paper lays a simple flight plan optimization strategy based on the particle filtering framework described in [5]. This is meant as a first step in formalizing computationally tractable stochastic programming techniques to optimally generate flight plans in response to battery life predictions. This approach takes advantage of the PF framework to simultaneously generate the optimal/sub-optimal flight plan simultaneously with predicting the RUL. Several steps lie ahead like a comparative analysis of alternative stochastic models in terms of optimality as well as computational cost. These options will need to be validated by flight tests where robustness to environmental conditions like air temperature and density as well as wind speed can be evaluated. The notion of risk-tolerance can be introduced via appropriate objective functions, thus allowing a non-zero risk of the dead stick condition in order to use more battery power.
A Framework for Systematic Benchmarking of Monitoring and Diagnostic Systems
In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13 benchmarking metric definitions. The calculation of metrics is illustrated on a probabilistic model-based diagnosis algorithm utilizing Bayesian reasoning techniques. The diagnosed system is a real-world electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The proposed benchmarking approach shows how to generate realistic diagnostic data sets for large-scale, complex engineering systems, and how the generated experimental data can be used to enable “apples to apples” assessments of the effectiveness of different diagnostic and monitoring algorithms.
ARC Code TI: CODE Software Framework
CODE is a software framework for control and observation in distributed environments. The basic functionality of the framework allows a user to observe a distributed set of resources, services, and applications. A user can also use the framework to manage distributed resources, services, and applications.
An Event-based Distributed Diagnosis Framework using Structural Model Decomposition
Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
DXC'10 Framework and Oracle
The DXC Framework is a collection of programs and APIs for running and evaluating diagnostic algorithms (DAs) under identical experimental conditions. It is complementary to system XML catalogs and data sets. The Oracle is new to DXC'10 and provides a querying capability to the DAs to determine the correct actions for given faults. Because the oracle uses proprietary NASA software, it is provided only in binary form. Thus the framework is available as a windows binary installer, linux source files + binary oracle, or windows source files + binary oracle. Please see the included READMEs and example DAs for more information.
A Bayesian Framework for Remaining Useful Life Estimation
The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. This is especially true for aerospace systems, where unanticipated subsystem downtime may lead to catastrophic failures. RUL prediction needs to contend with multiple sources of error like modeling inconsistencies, system noise and degraded sensor fidelity. Bayesian theory of uncertainty management provides a way to contain these problems by integrating out the nuisance variables. We use the Relevance Vector Machine (RVM), for model development. RVM is a Bayesian treatment of the well known Support Vector Machine (SVM), a kernel-based regression/classification technique. This model is next used in a Particle Filter (PF) framework. Statistical estimates of the noise in the system and anticipated operational conditions are processed to provide estimates of RUL in the form of a probability density function (PDF). Validation of this approach on experimental data collected from Li-ion batteries is presented.
Refined cellular activity expression signatures provide a targeted framework to quantify phenotypic intra-tumour heterogeneity in single-cell data II
GEO Series GSE310220. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
A phylogenetic framework to study the evolution of transcriptional regulatory networks [RNA-Seq]
GEO Series GSE94627. Saccharomyces cerevisiae; Schizosaccharomyces pombe; Candida albicans. 32 samples. Type: Expression profiling by high throughput sequencing.
Bioplatforms Australia: Antibiotic Resistant Sepsis Pathogens Framework Initiative - Streptococcus pneumoniae 180/2
GEO Series GSE152829. Streptococcus pneumoniae. 18 samples. Type: Expression profiling by high throughput sequencing.
An efficient framework to decipher microRNA regulatory programs in functionally divergent naïve T cell subsets [smallRNAseq_cleandirty]
GEO Series GSE280667. Mus musculus. 11 samples. Type: Non-coding RNA profiling by high throughput sequencing.
A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples
GEO Series GSE299799. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
An efficient framework to decipher microRNA regulatory programs in functionally divergent naïve T cell subsets [smallRNAseq_adultNeoTNVM]
GEO Series GSE280664. Mus musculus. 7 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Aptamer-modified 5-fluorouracil encapsulated metal-organic framework nanodrugs for enhanced therapy of colorectal cancer via peroxidative induced cell death
GEO Series GSE315569. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
A phylogenetic framework to study the evolution of transcriptional regulatory networks [Agilent microarray]
GEO Series GSE94625. Schizosaccharomyces pombe; Nakaseomyces glabratus; Kluyveromyces lactis; Naumovozyma castellii; Saccharomyces cerevisiae; Candida albicans. 103 samples. Type: Expression profiling by array.
Refined cellular activity expression signatures provide a targeted framework to quantify phenotypic intra-tumour heterogeneity in single-cell data III
GEO Series GSE310222. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.