Condition-based monitoring system for machinery and associated methods
First Claim
1. A system for performing real-time condition-based analysis on a machine for providing diagnostic and prognostic outputs indicative of machine status comprising:
- a signal processor for receiving signals from a plurality of sensors positioned and adapted for measuring a plurality of machine performance parameters, for conditioning and shaping at least some of the received signals into a form for inputting into a neural network, for identifying a current operational mode of a plurality of operational modes of the machine, and for selecting a particular neural network of a plurality of neural networks to which the conditioned and shaped signals are to be provided based at least in part on the current operational mode;
the plurality of neural networks including the particular neural network, the plurality of neural networks each adapted to receive at least some of the conditioned and shaped signals, each neural network associated with a different operational mode of the plurality of operational modes and configured to detect and classify a state of the machine based upon the received conditioned and shaped signals and upon a predetermined ontology of machine states, diagnostics, and prognostics, to determine from the machine state a relative health status thereof, and to output a signal representative of the determined relative health status; and
a Bayesian intelligence network adapted to receive the machine state from the neural network and to determine therefrom and output a probability of a fault at a predetermined future time.
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Abstract
Real-time condition-based analysis is performed on a machine for providing diagnostic and prognostic outputs indicative of machine status includes a signal processor for receiving signals from sensors adapted for measuring machine performance parameters. The signal processor conditions and shapes at least some of the received signals into an input form for a neural network. A fuzzy adaptive resonance theory neural network receives at least some of the conditioned and shaped signals, and detects and classifies a state of the machine based upon the received conditioned and shaped signals, and upon a predetermined ontology of machine states, diagnostics, and prognostics. The neural network can also determine from the machine state a health status thereof, which can comprise an anomaly, and output a signal representative of the determined health status. A Bayesian intelligence network receives the machine state from the neural network and determines a fault probability at a future time.
18 Citations
24 Claims
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1. A system for performing real-time condition-based analysis on a machine for providing diagnostic and prognostic outputs indicative of machine status comprising:
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a signal processor for receiving signals from a plurality of sensors positioned and adapted for measuring a plurality of machine performance parameters, for conditioning and shaping at least some of the received signals into a form for inputting into a neural network, for identifying a current operational mode of a plurality of operational modes of the machine, and for selecting a particular neural network of a plurality of neural networks to which the conditioned and shaped signals are to be provided based at least in part on the current operational mode; the plurality of neural networks including the particular neural network, the plurality of neural networks each adapted to receive at least some of the conditioned and shaped signals, each neural network associated with a different operational mode of the plurality of operational modes and configured to detect and classify a state of the machine based upon the received conditioned and shaped signals and upon a predetermined ontology of machine states, diagnostics, and prognostics, to determine from the machine state a relative health status thereof, and to output a signal representative of the determined relative health status; and a Bayesian intelligence network adapted to receive the machine state from the neural network and to determine therefrom and output a probability of a fault at a predetermined future time. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for performing real-time condition-based analysis on a machine for providing diagnostic and prognostic outputs indicative of machine status comprising:
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conditioning and shaping signals received from a plurality of sensors positioned and adapted for measuring a plurality of machine performance parameters into a form for inputting into a neural network; identifying a current operational mode of a plurality of operational modes of the machine; selecting a particular neural network of a plurality of neural networks to which the conditioned and shaped signals are to be provided based at least in part on the current operational mode, each neural network associated with a different operational mode of the plurality of operational modes; inputting at least some of the conditioned and shaped signals into the particular neural network, the particular neural network adapted to detect and classify a state of the machine based upon the received conditioned and shaped signals and upon a predetermined ontology of machine states, diagnostics, and prognostics; using the particular neural network to determine from the machine state a relative health status of the machine; outputting a signal representative of the determined relative health status; inputting the machine state into a Bayesian intelligence network adapted to determine therefrom a probability of a fault at a predetermined future time; and outputting the determined fault probability. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification