George Mason University Sensor Management in a Sensor Rich Environment Carl G. Schaefer, Jr. Kenneth J. Hintz Department of Electrical and Computer Engineering

George Mason University

Sensor Management ina Sensor Rich Environment

Carl G. Schaefer, Jr.Kenneth J. Hintz

Department of Electrical and Computer Engineeringand

Center of Excellence in C3IGeorge Mason University, Fairfax, VA 22030

SPIE AeroSense24 – 28 April 2000

Orlando, FL

April 25, 2000 SPIE Aerosense 2000 2


The Sensor Rich Environment

• Sensor rich environment (SRE) – large network of inexpensive, unimodal, passive sensors with the ability to capture data in parallel.

• Contrast with C3I sensor environments – limited number of expensive, multimodal, and active sensors.

• Examples:– U.S. Navy DD-21 destroyer -- ~ 200,000 sensors.

– Aircraft health and usage monitoring systems (HUMS) – ~ 2,000 sensors.

– Others include networked building energy systems, autonomous space probes, satellite constellations, power grids, and others.



The Sensor Rich Environment (cont’d)

• Overwhelming volume of data– One example: SH-60 HUMS

– 450 healthy aircraft 3 – 9 terabytes of data per month, 400 terabytes of data in 10 years

– Overabundance of data compounded when components fail.

• This is a “data rich, information poor (DRIP)” environment, especially at the human interface.

• In an SRE, system state estimation not constrained by sensors, but by network bandwidth, processor performance, human-machine interface.



The Sensor Rich Environment (cont’d)

• Paradigm must change:– From: merely collecting vast amounts of data to

be analyzed later– To: determining information needs first,

followed by collection of only that data that can satisfy those needs.



Characteristics of Sensor Rich Environments

• Large numbers of sensors and pseudosensors.• Massively distributed.• Tightly coupled.• Unimodal sensors (typically).• Stationary, perhaps overlapping, sensing or surveillance

volumes.• Fixed (non-agile and stationary) sensors.• (Relatively) inexpensive sensors.• Passive, rather than active, sensors.• Sensor type diversity.• Continuous and concurrent data acquisition.



Helicopter HUMS Sensor Distribution

Aircraft Subsystem No. of Sensors or Pseudosensors

Aircraft States and Controls 158 Engines 344 Auxiliary Power Unit (APU) 7 Main Gearbox 29

Auxiliary Gearbox 59 Auxiliary Systems 12 Intermediate Gearbox 37 Tail Rotor Gearbox 31 Drive shafts/Hanger Bearings 84 Flight Regimes 202 Rotor Track and Balance 179 Hydraulics and Lubricants 134 External Loads 26 Fuel System 47 HUMS Status and Self-Test 304 Engine Nose Gearbox 26 Swashplate 13



Helicopter HUMS Sensors

• Highly distributed sensor network– 1,700 sensors spread over 17 aircraft subsystems

• Single sensors covering multiple subsystems or performing multiple functions.– Accelerometer(s) at aircraft center of gravity

– Flight regime recognition, flight control system, load limiting device, aircraft vibration control, rotor track and balance.

• Tightly coupled sensor network. Significant data sharing and interaction between subsystems.

• Overlapping sensor volumes for gearbox fault monitoring.



Distributed Sensing on Helicopter Tail Rotor Driveshaft

Individual Sensor

Pseudosensor



GMU Comprehensive Sensor Manager

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Sensor Management for SRE’s• The GMU Comprehensive Sensor Management model can

be adapted to SRE without major architectural or functional changes.

• Architectural options;– Fully distributed.

• Issues relative to coordination of mission goals amongst distributed sensor managers.

– Fully centralized.• Issues concerning the immense size of the Applicable Function

Tables and Applicable Sensor Tables.

– Hybrid system• Distributed sensor schedulers partitioned along functional or

subsystem boundaries.• Single Mission Manager.



SRE Sensor Manager



HUMS Partial Goal Lattice



Implications of Hybrid SM Architecture

• Difficulty in modeling faults and coupled failure modes may require local intelligence at each sensor.

• This implies an “interrupt driven” process, however, this is still consistent with the architecture of the original SM model.

• Meta-scheduler to coordinate subsystem sensor schedulers.• Use of intelligent sensors must be balanced against global

context in which system is situated.– True system or component failure?, or– Combat maneuvering?

• Information span – abstracting view of local data by placing it within the global context until it “makes sense”.



Conclusions

• The GMU Comprehensive Sensor Management Model can be adapted to a sensor rich environment with only minor implementation changes.

• Research areas:– Mission Manager – adaptive goal lattices to compensate for real-time

reprioritization of mission needs.– Information Instantiator/Sensor Scheduler – methods to efficiently map

information needs to multiple and overlapping functional needs and to map these functional needs to sensors allocated to distributed sensor schedulers.

– Sensor Scheduler – development of an efficient meta-scheduler for coordinating multiple distributed sensor schedulers.

– Mission Manager – methods for abstracting local information and placing that information within the global context; the “information span”.

– Fusion Space – failure state estimators, data association techniques for the hybrid SM model.

Documents

George Mason University Sensor Management in a Sensor Rich Environment Carl G. Schaefer, Jr. Kenneth J. Hintz Department of Electrical and Computer Engineering