US 2003/0195718 A1 · Method and device for machinery diagnostics and prognostics

Abstract

Machinery diagnostics and prognostics is an emerging engineering field that seeks to accurately determine the operational health of a machine without waiting for the machine to fail, performing maintenance that may not yet be required or, in the worst case, performing unnecessary maintenance that inadvertently causes other problems and hastens machine health deterioration. Accurate prediction of machine health (operability) enables operators to base machine maintenance on the machine's actual condition, in contrast to the common practice of time-based maintenance (e.g., perform maintenance every 100 hours). The “just-in-time” methodology of the machine health monitoring system (MHMS) of the present invention translates into significant cost savings by providing early warning of impending failures and thus reducing unanticipated catastrophic machine failures through preventative maintenance techniques (but no more or no less than is required) while simultaneously keeping false alarm rates low. The MHMS couples proprietary analysis, modeling and pattern recognition techniques to provide accurate machine health predictions. The MHMS's combination of technologies provides accurate, easy-to-understand, early indication of potential problems, enabling efficient and timely repair. In addition, MHMS technology can continuously “learn” from its own machine health monitoring experience, so that system accuracy and false alarm rate improve over time.

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Claims1

Dependent claims

A computer readable medium having instructions encoded thereon that cause a computer system to perform an accurate machine health prediction upon receipt of raw monitored machine operating variables data from said machine, the instructions on the computer readable medium comprising:

(a) a data filter comprising a filtering algorithm operable for eliminating “spikes” in said raw monitored machine operating variables data while retaining legitimate “ups and downs” in the raw monitored machine operating variables data, thereby improving the predictive performance of transfer function models (TFMs);
(b) a TFM estimator comprising a multivariate non-linear transfer function estimation algorithm that uses historical or real-time calibration data comprised of values of machine operating variables from one or more normally operating machines to define normal monitored machine operating variables and to construct statistical multivariate non-linear TFMs of normal monitored machine operating variables;
(c) a TFM modelbase comprising (i) a plurality of TFMs;
and (ii) a set of sampled normal monitored machine operating variable values and operational condition values employed to develop said plurality of TFMs;
(d) a predictor operable for identifying the best TFMs in the TFM modelbase for the monitored machine operating variables data and the current set of real-time machine operational conditions, and uses said monitored machine operating variables to predict one-step-ahead values for said monitored machine operating variables;
(e) a comparator that enables the computer to compare current monitored machine operating variable values to the value of the monitored machine operating variables predicted by the appropriate TFMs for the same variables, and producing the algebraic differences therebetween, said differences defining residuals;
(f) a prognosticator component that receives the residuals for each monitored machine operating variable from said comparator and conducts statistical tests on said residuals to categorize each monitored machine operating variable as normal or abnormal, and employ the results of said statistical tests on each monitored machine operating variable to calculate an output, the value of the output being a measure of the overall probability of machine abnormality. 2. a method for predicting machine failure comprising the steps of:
(a) presenting a machine wherein operability of the machine is dependent on the values of a set of machine variables;
(b) presenting a computer means comprising the computer readable medium of claim 1;
(c) measuring the set of machine variables to provide a set of measured machine variables, the set of measured machine variables comprising input to said computer means;
and (d) observing the value of the PMA output of the computer means, the value of the PMA being predictive of machine failure.

8 more elements

Description5,076 words

Summary1,298 words

#1. Field of the Invention

#A method and device for machinery diagnostics and prognostics.

#2. Prior Art

Brief description of the drawings2 figures

#FIG. 1 is a diagrammatic representation of a Transfer Function Model.

#FIG. 2 is a diagrammatic representation illustrating the operation of the machinemonitoring device of the present invention.

Detailed description3,750 words

#Artisans skilled in art will appreciate the value of illustrating the present invention by means of an example. Consider early detection of “abnormalities” (prognostics) and classification of those abnormalities (diagnostics) based on historical Digital Electronic Engine Controller data from many normally operating F-16 fighter aircraft F100-PW-229 gas turbine (jet) engines (GTEs) to learn what is “normal” and to automatically construct statistical multivariate non-linear transfer function models (TFMs) of normal GTE variable values and relationships over the entire range of normal GTE operation.

#Pre-Processing Filters

#The present invention can be thought of as a machine health monitoring system (MHMS). FIG. 2 illustrates the process of automatically developing TFMs for machine health monitoring (MHM). The TFM estimation process automatically takes raw data for machine variables of interest (generally those believed to be the most important to the particular machine's health by experts, operators or maintenance), filters the data to eliminate anomalous data “spikes” that are typically present in machine data produced by analog or digital sensors, estimates appropriate TFMs for each variable of interest relating that variable to all other variables of interest, and places the TFMs so developed into the Transfer Function Modelbase (TFMB). If historical, recorded data is used to develop the TFMs, this process is not real-time. If the MHMS is provided data feeds from an operating machine or machines, then the TFM estimation process occurs in real-time as the machine is operating.

Method and device for machinery diagnostics and prognostics

Life of this patent

Method and device for machinery diagnostics and prognostics

Life of this patent

Life of this patent

Abstract

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Claims1

Description5,076 words

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Classification2 codes

Life of this patent

Description5,076 words