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Opportunity: Modern LADAR systems have evolved to point where they are capable of making long-range non-contact surface vibration measurements. Etegent is working to develop algorithms to reveal useful and actionable information from surface vibration data.
LADAR vibration signatures contain valuable information about both the excitation mechanism and the structure on which the vibration is being measured. This rich “hidden” information can be “decoded” utilizing a sound, physics-based understanding of vibration excitation mechanisms and structural dynamic response characteristics.
Etegent has a strong physics-based understanding of piston engine vibration signatures generated by different engine configurations. Under prior work with AFRL/RYJM an engine vibration modeling program, eVISM, has been developed which predicts engine vibration spectra based on engine configuration, cylinder combustion pressure pulses (and associated torsional acceleration of the drivetrain) and inertial acceleration of the drivetrain (pistons, rods, crank). Effects of component imbalance and imbalanced combustion pressures (misfires) can be modeled.
Misfire vibration can dominate an engine signature with major implications. First, misfire variation across different serial numbers can render “black box” pattern matching approaches to engine vibration signature ID useless. Second, a physics based understanding of engine vibration mechanisms can not only be used to effectively ID engine types based on remote vibration measurements, but may also be used to fingerprint different vehicles based on severity of misfire and other characteristics.
Etegent has utilized this physics-based understanding of the vibration characteristics of reciprocating engines to develop remote vibrometry measurement techniques to robustly classify salient engine configuration and operating features without requiring any signature data training sets.
Under Phase I and Phase II SBIRs sponsored by AFRL/RYJM, techniques are being developed to identify two salient, physics-based engine features: engine speed and number of cylinders. Beyond these engine vibration features, there is also potential to detect third order vibration sources such as engine accessories, valve train components and drive train components for engine ID.