Mechanical health monitor apparatus and method of operation therefor
First Claim
1. An apparatus for estimation of state of mechanical health of a mechanical element, comprising:
- a probabilistic data signal processor embedded in a computer, said probabilistic data signal processor comprising;
a dynamic state-space model comprising;
a process model; and
an observation model;
a probabilistic updater configured to generate a posterior probability distribution function using both;
(1) a prior probability distribution function output from said dynamic state-space model and (2) readings from a load sensor, wherein the mechanical element comprises a valve bearing of a valve, said valve comprising a pipe fitting configured to regulate flow of a substance through a pipe, said load sensor configured to provide time dependent measurements related to load on the valve bearing of the valve; and
a probabilistic sample module configured to operate on said posterior probability distribution function to generate an output in the form of at least one probability of state of health of said valve.
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Abstract
A probabilistic data signal processor used to determine health of a system is described. Initial probability distribution functions are input to a dynamic state-space model, which iteratively operates on probability distribution functions, such as state and model probability distribution functions, to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled and used as an input to the dynamic state-space model in the iterative algorithm. In various embodiments, the probabilistic data signal processor is used to filter output from any mechanical device using appropriate physical models, which optionally include chemical, electrical, optical, mechanical, or fluid based models. Examples to valve bearings and pipe systems are provided.
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Citations
16 Claims
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1. An apparatus for estimation of state of mechanical health of a mechanical element, comprising:
a probabilistic data signal processor embedded in a computer, said probabilistic data signal processor comprising; a dynamic state-space model comprising; a process model; and an observation model; a probabilistic updater configured to generate a posterior probability distribution function using both;
(1) a prior probability distribution function output from said dynamic state-space model and (2) readings from a load sensor, wherein the mechanical element comprises a valve bearing of a valve, said valve comprising a pipe fitting configured to regulate flow of a substance through a pipe, said load sensor configured to provide time dependent measurements related to load on the valve bearing of the valve; anda probabilistic sample module configured to operate on said posterior probability distribution function to generate an output in the form of at least one probability of state of health of said valve. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for estimating state of mechanical health of a mechanical element, comprising the steps of:
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collecting with a sensor time dependent readings related to a mechanical state of the mechanical element, wherein the mechanical element comprises a valve bearing of a valve, said valve comprising a pipe fitting configured to regulate flow of a substance through a pipe, said sensor configured to measure load on said valve bearing; calculating a prior probability distribution function with a probabilistic data signal processor, said probabilistic data signal processor comprising; a dynamic state-space model comprising; a process model; and an observation model using the readings from said sensor, determining a posterior probability distribution function through combination of;
(1) said prior probability distribution function output from said dynamic state-space model and (2) the readings from said sensor; andusing said posterior probability distribution function, generating an output comprising an estimate of state of health of the mechanical element. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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Specification