Assessment of data and knowledge fusion strategies for prognostics and health management

Abstract. Recent advents in magnetic resonance spectroscopy (MRS) techniques permit subsequent microarray analysis over the entire human transcriptome in the same tissue biopsies. However, extracting information from such immense quantities of data is limited by difficulties in recognizing and evaluating the relevant patterns of apparent gene expression in the context of the existing knowledge of phenotypes by histopathology. Using a quantitative approach derived from a knowledge base of pathology findings, we present a novel methodology used to process genome-wide transcription and MRS data. This methodology was tested to examine metabolite and genomewide profiles in MRS and RNA in 55 biopsies from human subjects with brain tumors with ~100% certainty. With the guidance of histopathology and clinical outcome, 15 genes with the assistance of 15 MRS metabolites were able to be distinguished by tumor categories and the prediction of survival was better than when either method was used alone.This new method, combining MRS, genomics, statistics and biological content, improves the typing and understanding of the complexity of human brain tumors, and assists in the search for novel tumor biomarkers. It is an important step for novel drug development, it generates testable hypotheses regarding neoplasia and promises to guide human brain tumor therapy provided improved in vivo methods for monitoring response to therapy are developed.

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“…medicine (58,59). Some studies have previously described that classifiers have attempted to combine data from different sources (60)(61)(62)(63).…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone

Astrakas

Blekas²,

Constantinou³

et al. 2011

Int J Oncol

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“…Although much relevant recent work has been reported in the literature [1][2][3][4][5][6][7], no mention is found in recent literature of techniques providing intelligent prognosis for a large class of systems based on chemical or other non-electrical processes where the interactions of the various operating parameters are subtle, complex, and correlated in unknown or difficult to elicit ways. Examples of such systems are semiconductor manufacturing appliances, rocket engines, and chemical oxygen-iodine lasers (COILs), which depend on flow, pressures and temperatures for successful operation.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of System Failure in Complex Chemical or Non-Electrical Based Systems Using a Nerual Network

Stone

2006

2006 World Automation Congress

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Health monitoring systems have evolved into complex diagnostic systems. Researchers are attempting to include prediction, or prognostics, into such systems and are resorting to hybrid systems fusing statistics, data mining, expert systems, neural networks, and more into system that perform not only health monitoring and diagnostics but prognostics as well. However, no work has been reported on systems based on chemical or other non-electrical processes where the interactions of the various operating parameters are subtle, complex, and correlated in unknown or difficult to elicit ways. This paper describes the use of neural networks to provide early detection of the onset of operational failure in such devices and suggests ways to use it as part of a prognostic system.

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“…While the details on the probabilistic fusion process alluded to in Figure 9, are outside the scope of this paper (more detail can be found in [7]), fusion with the Dempster-Shafer method is provided here.…”

Section: Knowledge Fusionmentioning

confidence: 99%

Enhancement of Physics-of-Failure Prognostic Models with System Level Features

Kacprzynski

Roemer

Modgil

et al. 2002

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-To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches.The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-offailure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes.

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Assessment of data and knowledge fusion strategies for prognostics and health management

Cited by 60 publications

References 2 publications

Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone

Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone

Early Detection of System Failure in Complex Chemical or Non-Electrical Based Systems Using a Nerual Network

Enhancement of Physics-of-Failure Prognostic Models with System Level Features

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