Surveillance system and method having an adaptive sequential probability fault detection test
AssigneeIntellectual Assets LLC
InventorsJames P. Herzog, Randall L. Bickford
Life of this patent
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Abstract
System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.
Claims32
A method for performing surveillance of an asset, the steps including:
- acquiring residual data correlative to expected asset operation;
- fitting an equation to said acquired residual data and storing said fitted equation in a memory means;
- collecting a current set of observed signal data values from the asset;
- using said fitted equation in a sequential hypotheses test to determine if said current set of observed signal data indicates unexpected operation of the asset that is indicative of a fault condition;
- outputting a signal correlative to a detected fault condition for providing asset surveillance.
A surveillance system for monitoring an asset, said system comprising in combination:
- a data acquisition means for acquiring a current set of signals engendered from said asset correlative to asset status;
- a digitizing means for digitizing said current set of signals for defining a current set of digitized signals;
- a parameter estimation means for producing a set of estimated signal values as a function of said current set of digitized signals;
- a fault detection means for detecting the presence of a fault as a function of said set of estimated signal values and said current set of digitized signals;
- said fault detection means including a stored fault detection model comprised of an empirically derived probability density function utilized in a sequential probability test of at least one result of a mathematical operation between said set of estimated signal values and said current set of digitized signals;
- a communication means for communicating detected faults to a remote location for providing asset surveillance.
A method for training a plurality of process models wherein each process model represents a set of parameter estimation models and a set of fault detection models of an asset to be monitored by a surveillance system, the steps including:
- acquiring at least one set of signal values associated with a current status of said asset;
- forming a training data set comprised of at least one said set of signal values;
- providing a memory means for storing and retrieving said training data set;
- processing said training data set comprised of at least one said set of signal values associated with said current status of said asset for training at least one parameter estimation model;
- estimating at least one set of process parameters by using at least one said parameter estimation model for processing said training data set;
- empirically fitting a general probability density function to at least one said set of signals values in said training data set as a function of at least one said estimated set of process parameters and said training data set for obtaining at least one empirically derived fault detection model;
- and
- storing within said memory means said at least one said parameter estimation model and at least one said empirically derived fault detection model for subsequent use in surveillance of said asset.
A method for performing surveillance of an asset, the steps including:
- numerically fitting a probability density function to a distribution correlative to normal operation of the asset;
- acquiring a current set of observed signal data values from the asset;
- calculating a current set of estimated signal data values from a stored model for said asset;
- transforming said calculated current set of estimated signal data values with said current set of observed signal data values for defining residual data values;
- utilizing said fitted probability density function in a statistical hypothesis test performed on said residual data values for detecting a fault condition;
- and
- outputting a signal correlative to each detected fault condition for providing asset surveillance.
Dependencies are inferred from the claim text.