Data processing: generic control systems or specific application – Generic control system – apparatus or process – Digital positioning
Reexamination Certificate
2001-04-26
2003-12-23
Khatri, Anil (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Digital positioning
C700S027000, C702S182000, C702S188000, C703S017000, C703S022000, C705S007380, C705S014270, C717S104000
Reexamination Certificate
active
06668203
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention is directed to the analysis of raw sensor data from dynamic processes at a facility for the purpose of remote facility monitoring and inspection, and more particularly to a method and apparatus for identifying the actual processes that were performed at the facility during a period of interest using a state machine model and comparing the actual processes identified to the expected processes declared by an inspector.
The use of sensor systems for monitoring and tracking the status of high value assets and processes has proven to be less costly and less intrusive than the on-site human inspections that they are intended to replace. Such systems can help to minimize the need for costly material inventories and human exposure to hazardous materials. In general, such a remote monitoring system may be of benefit to any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information of any kind.
Typical government applications for such sensor systems include nuclear material handling sites with sensored operations (for example, weapons facilities that fabricate, transport, or store nuclear material), facilities secured with detection sensors where controlled, expensive, or classified materials must be accessed and stored, and facilities that must safely handle expensive, dangerous, or controlled materials of any kind (conventional weapons, high explosives, experimental reactors, etc.)
Industrial applications for such sensor systems include facilities with sensored operations (for example, nuclear power plants, pharmaceutical companies, and chemical manufacturers), sites that must monitor access to expensive or one-of-a-kind objects (satellite components, fine-line lithography equipment, etc.), and plants that must electronically monitor complicated, human-error prone operations for safety or efficiency (power plants, computer chip producers, etc.)
In the realm of international inspections, such a sensor system can save inspectors from numerous trips to foreign facilities. In addition, facility inspectors can automate the current data inspection/verification process, allowing them to concentrate on process abnormalities, and saving them from unnecessary attention to normal processing.
Such monitoring systems, however, present a classic information overload problem to an inspector trying to analyze the resulting sensor data. These data are typically so voluminous and contain information at such a low level that the significance of any single reading (e.g., a door open event) is not obvious. Sophisticated, automated techniques are needed to identify and extract expected processes in the data and isolate and characterize the remaining patterns that may be due to undeclared or abnormal activities. A key issue from an operational perspective is that it is not feasible to expect a human inspector to manually perform all of the required analysis reliably.
The data gathered by monitoring systems come from a wide variety of sensors including discrete state sensors (e.g., breakbeams), analog sensors that measure continuous physical quantities (e.g., tank levels or temperatures), and sensors that measure spectra (e.g., chemical photo analyzers and gamma radiation spectra). Analysis of this data requires extracting, correlating, and classifying patterns in the sensor data and interpreting them in terms of the allowed activities at the monitored facility. In most situations, it is not obvious how to combine the discrete, analog, and spectrum sensor data in ways to draw useful conclusions about the dynamic processes being monitored.
There are many factors that make sensor data analysis both difficult and labor intensive:
the processes being monitored can have a tremendous degree of variability (e.g., activities in the process may not always be performed in the same order);
many of the sensors provide only minimal information, indicating activity but not conveying sufficient information to reliably classify the nature of the activity;
interpretation of the raw sensor data is facility- and process-specific, requiring a high degree of human training;
the data may be incomplete (e.g., two objects may pass through a breakbeam with only one resultant trigger);
there may be “noise” from background activities, either expected or unexpected (e.g., two activities occurring simultaneously);
the interpretation of the raw sensor data may depend on the current or past states of multiple sensors or subtle timing differences between events coming from multiple sensors;
sensors tend to drift away from their calibration points; and
measurements of continuous physical quantities have inherent uncertainty.
All of these factors must be accounted for in assessing how well the events from many sensors correlate with expected, normal behavior.
Traditional systems typically monitor static situations. For a storage facility, for example, a simple monitoring system that checks sensor values against static set points is quite adequate. However, for facilities with dynamic processes, it is no longer sufficient simply to check periodically the readings of each sensor against a fixed threshold value. In particular, analysis of data from dynamic processes differs from static facilities in important ways:
interpretation of the data from a sensor that is within threshold often depends on knowing the current status (state) of other objects or processes in the facility;
verification of correct operation of a facility often requires knowledge about correct sequencing of processes and sub-processes;
correct identification of what processes have occurred can require knowledge of the relative timing between sensor events; and
correct assessment of the current status of a facility can require knowledge of the status of the facility at any time in the past.
There remains a need for facilities with dynamic processes for an inspector to be able to detect situations where knowledge about the combined states of multiple sensors is required to make judgements about possible diversion, safety, security, sensor system integrity, sensor data quality, or other, more abstract concepts.
SUMMARY OF THE INVENTION
The present invention, hereinafter referred to as Knowledge Generation (KG), solves the problem of information overload at facilities having multiple dynamic processes with an automated data analysis engine that runs a state machine model of the processes and sensors at the facility.
The invention comprises a method that analyzes the raw sensor data, advancing the state machine through a series of object state transitions, thereby converting and combining the outputs from many sensors into operator domain level information, or actual processes, that occurred at the facility during the period of interest. The method further compares the actual processes identified against a set of expected processes declared by an inspector (these would normally be the legal or allowed operations), and then presents the differences between processes that actually occurred and those that were declared by the inspector in the process level domain.
The present invention also comprises an apparatus for performing the KG analysis. A processor connects with an input/output system and storage. A sensor array monitors the dynamic processes at the facility. The apparatus also comprises means for inputting the raw sensor data and the facility characteristics into the processor to define event rules in the storage, converting the raw sensor data into events with the event rules, advancing the state machine through a series of object state transitions using with the events, and grouping the transitions into the actual processes. The apparatus further comprises means for inputting expected operations into the processor to construct a process declarations file and comparing the declared processes to the actual processes to identify undeclared processes that occurred at the facility during the period of interest. This information is output to the inspector t
Brabson John M.
Cook William R.
Deland Sharon M.
Barnes Crystal J.
Bieg Kevin W.
Khatri Anil
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