Detection and Location of Mechanical System Degradation by Using Detector Signal Noise Data

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This report describes the investigation of a diagnostic method for detecting and locating the source of structural degradation in mechanical systems. The goal of this investigation was to determine whether the diagnostic method would be practically and successfully applied to detect and locate structural changes in a mechanical system. The diagnostic method uses a mathematical model of the mechanical system to define relationships between system parameters, such as spring rates and damping rates, and measurable spectral features, such as natural frequencies and mode shapes. These model-defined relationships are incorporated into a neural network, which is used to relate measured spectral ... continued below

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81 pages

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Damiano, B. January 1, 1994.

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Description

This report describes the investigation of a diagnostic method for detecting and locating the source of structural degradation in mechanical systems. The goal of this investigation was to determine whether the diagnostic method would be practically and successfully applied to detect and locate structural changes in a mechanical system. The diagnostic method uses a mathematical model of the mechanical system to define relationships between system parameters, such as spring rates and damping rates, and measurable spectral features, such as natural frequencies and mode shapes. These model-defined relationships are incorporated into a neural network, which is used to relate measured spectral features to system parameters. The diagnosis of the system's condition is performed by presenting the neural network with measured spectral features and comparing the system parameters estimated by the neural network to previously estimated values. Changes in the estimated system parameters indicate the location and severity of degradation in the mechanical system. The investigation involved applying the method by using computer-simulated data and data collected from a bench-top mechanical system. The effects of neural network training set size and composition on the accuracy of the model parameter estimates were investigated by using computer-simulated data. The measured data were used to demonstrate that the method can be applied to estimate the parameters of a ''real'' mechanical system. The results show that this diagnostic method can be applied to successfully locate and estimate the magnitude of structural changes in a mechanical system. The average error in the estimated spring rate values of the bench-top mechanical system was approximately 5 to 10%. This degree of accuracy is sufficient to permit the use of this method for detecting and locating structural degradation in mechanical systems. It is also shown that the neural network training sets required for this level of estimate accuracy are not impractically large and can consist of natural frequency and mode shape information that is sufficient to provide system parameter estimates.

Physical Description

81 pages

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  • Other Information: PBD: 1 Jan 1994

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  • Report No.: ORNL/TM-12695
  • Grant Number: AC05-00OR22725
  • DOI: 10.2172/814043 | External Link
  • Office of Scientific & Technical Information Report Number: 814043
  • Archival Resource Key: ark:/67531/metadc740470

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • January 1, 1994

Added to The UNT Digital Library

  • Oct. 18, 2015, 6:40 p.m.

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  • March 31, 2016, 12:50 p.m.

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Damiano, B. Detection and Location of Mechanical System Degradation by Using Detector Signal Noise Data, report, January 1, 1994; United States. (digital.library.unt.edu/ark:/67531/metadc740470/: accessed November 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.