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1,601 results for “Prognosis;”
Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison.
In-situ fatigue life prognosis for composite laminates based on stiffness degradation
In this paper, a real-time composite fatigue life prognosis framework is proposed. The proposed methodology combines Bayesian inference, piezoelectric sensor measurements, and a mechanical stiffness degradation model for in-situ fatigue life prediction. First, the composites stiffness degradation is introduced to account for the composites fatigue damage accumulation under cyclic loadings and a new growth rate-based stiffness degradation model is developed. Following this, the general Bayesian updating-based fatigue life prediction method is discussed. Several sources of uncertainties and the developed stiffness degradation model are included in the prognosis framework. Next, an in-situ composites fatigue testing with piezoelectric sensors is designed and performed to collected sensor signal and the global stiffness data. Signal processing techniques are implemented to extract damage diagnosis features. The detected stiffness degradation is integrated in the Bayesian inference framework for the remaining useful life (RUL) prediction. Prognosis performance on experimental data is validated using prognostics metric. Finally, some conclusions and future work are drawn based on the proposed study.
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.
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.
Integrated fatigue damage diagnosis and prognosis under uncertainties
An integrated fatigue damage diagnosis and prognosis framework is proposed in this paper. The proposed methodology integrates a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Following this, a small time scale model is introduced and used as the mechanism model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result for the fatigue crack growth prediction. Probability distributions of model parameters and final RUL are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.
Adaptive Load-Allocation for Prognosis-Based Risk Management
It is an inescapable truth that no matter how well a system is designed it will degrade, and if degrading parts are not repaired or replaced the system will fail. Avoiding the expense and safety risks associated with system failures is certainly a top priority in many systems; however, there is also a strong motivation not to be overly cautious in the design and maintenance of systems, due to the expense of maintenance and the undesirable sacrifices in performance and cost effectiveness incurred when systems are over designed for safety. This paper describes an analytical process that starts with the derivation of an expression to evaluate the desirability of future control outcomes, and eventually produces control routines that use uncertain prognostic information to optimize derived risk metrics. A case study on the design of fault-adaptive control for a skid-steered robot will illustrate some of the fundamental challenges of prognostics-based control design.
Cell-free DNA methylation markers in diagnosis and prognosis of common cancers (COAD)
GEO Series GSE97923. Homo sapiens. 773 samples. Type: Methylation profiling by high throughput sequencing.
Integration of ATAC-seq and RNA-seq identifies typing and prognosis evaluation target molecules of triple negative breast cancer
GEO Series GSE254212. Homo sapiens. 19 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
CD8+MTs+ Effector T Cells from Enlarged Tumor-Draining Lymph Nodes Drive Enhanced Tumor Immunogenicity and Improved Prognosis in Colorectal Cancer Patients
GEO Series GSE282542. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer
GEO Series GSE246486. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
Skeletal muscle transcriptional dysregulation of genes involved in senescence is associated with prognosis in severe heart failure
GEO Series GSE262824. Homo sapiens. 78 samples. Type: Expression profiling by high throughput sequencing.
RANK is an independent biomarker of poor prognosis in estrogen receptor-negative breast cancer and functionally contributes to increase tumor aggressiveness
GEO Series GSE185513. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Circulating PMN-MDSCs correlates with poor prognosis in metastatic hormone sensitive prostate cancer
GEO Series GSE207006. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Tumor Stemness Score to Estimate Epithelial-to-Mesenchymal Transition (EMT) and Cancer Stem Cells (CSCs) Characterization and to Predict the Prognosis and Immunotherapy Response in Bladder Urothelial
GEO Series GSE215947. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
The transcriptional signature for Hepatocyte Growth Factor-driven invasive growth predicts poor prognosis of human hepatocellular carcinoma.
GEO Series GSE43393. Mus musculus. 8 samples. Type: Expression profiling by array.
Elevated MTSS1 mRNA Expression Is Associated with Metastasis and Poor Prognosis of Residual Hepatitis B-related Hepatocellular Carcinoma
GEO Series GSE75965. Homo sapiens. 20 samples. Type: Expression profiling by array.
Recurrent Splicing Programs Define a Conserved Axis of Tumor Heterogeneity and Prognosis
GEO Series GSE301722. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
A small-cell lung cancer subtype with good prognosis found by a three miRNA signature
GEO Series GSE19945. Homo sapiens. 63 samples. Type: Non-coding RNA profiling by array.
Expression of genes associated with poor prognosis from hepatocellular carcinoma are increased in peripheral blood from Hmong with active hepatitis B infection
GEO Series GSE173897. Homo sapiens. 95 samples. Type: Expression profiling by high throughput sequencing.
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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.