State machine analysis of sensor data from dynamic processes

• US 6,668,203 B1
• Filed: 04/26/2001
• Issued: 12/23/2003
• Est. Priority Date: 04/26/2001
• Status: Active Grant

• Current Assignee: National Technology & Engineering Solutions Of Sandia LLC (Honeywell International Inc.)
• Original Assignee: Sandia Corporation (Martin Marietta Corporation)
• Inventors: Cook, William R., Deland, Sharon M., Brabson, John M.
• Primary Examiner(s): Khatri, Anil
• Assistant Examiner(s): BARNES-BULLOCK, CRYSTAL JOY
• Application Number:
  US09/844,028
• Time in Patent Office: 971 Days
• Field of Search: 700/27, 700/65, 700/9, 700/86-88, 700/103, 703/17, 703/22, 707/103, 717/104, 702/182, 702/188, 705/7, 705/11
• US Class Current: 700/65
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms

Abstract

A state machine model analyzes sensor data from dynamic processes at a facility to identify the actual processes that were performed at the facility during a period of interest for the purpose of remote facility inspection. An inspector can further input the expected operations into the state machine model and compare the expected, or declared, processes to the actual processes to identify undeclared processes at the facility. The state machine analysis enables the generation of knowledge about the state of the facility at all levels, from location of physical objects to complex operational concepts. Therefore, the state machine method and apparatus may benefit any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information.

Claims

1. A method for remote facility inspection, comprising the steps of:

a) gathering raw sensor data from a plurality of dynamic processes with a plurality of sensors during a period of interest;

b) converting the raw sensor data into a plurality of events;

c) advancing a state machine with the plurality of events from an initial state through a plurality of object state transitions to a final state, Wwerein the object state transitions comprise a plurality of transitioning steps whereby a destination at the intersection of each of a plurality of object states with each of the plurality of events within the state machine instructs an object to transition from a first state to a second state when an event happens and the object is in the object state defined by the destination;

d) grouping the plurality of object state transitions into a plurality of actual processes performed at the facility during the period of interest;

e) declaring a plurality of declared processes that an inspector expects to happen at the facility during the period of interest; and

f) comparing the plurality of actual processes to the plurality of declared processes, thereby identifying undeclared processes performed at the facility during the period of interest.

2. The method of claim 1, wherein the converting step further comprises identifying a significant transition point in the raw sensor data as an event indicating a change in a process state of the facility.

3. An apparatus for remote facility inspection, comprising:

a) a processor;

b) storage accessible from the processor;

c) an input system accessible from the processor;

d) an output system accessible from the processor;

e) a plurality of sensors for gathering raw sensor data from a plurality of dynamic processes;

f) means for using facility characteristics from the input system and the processor to define a plurality of event rules in the storage;

g) means for using facility characteristics from the input system and the processor to construct a state machine in the storage;

h) means for converting the raw sensor data into a plurality of events according to the event rules;

i) means for advancing the state machine with the plurality of events from an initial state through a plurality of object state transitions to a final state, the advancing means comprising a matrix further comprising a plurality of object states and the plurality of events and having a destination at the intersection of each of the plurality of object states with each of the plurality of events, wherein the destination instructs an object to transition from a first state to a second state when the an event happens and the object is in the object state defined by the destination;

j) means for grouping the object state transitions into a plurality of actual processes performed at the facility during the period of interest;

k) means for using the processor and the output system to communicate the actual processes;

l) means for using expected operations from the input system and the processor to construct a plurality of declared processes in the storage; and

m) means for comparing the plurality of actual processes to the plurality of declared processes to identify undeclared processes performed at the facility during the period of interest.

4. The apparatus of claim 3, wherein the destination is modeled with a destination description language.

5. The apparatus of claim 3, wherein the event rules identify significant transition points in the raw sensor data as a change in a process state of the facility.