No abstract

Military missiles are exposed to many sources of mechanical vibration that can affect system reliability, safety, and mission effectiveness. The US Army Aviation and Missile Research Development and Engineering Center has been developing missile health monitoring systems to assess and improve reliability, reduce life cycle costs, and increase system readiness. One of the most significant exposures to vibration occurs when the missile is being carried by a helicopter or other aviation platform, which is a condition known as captive carry. Recording the duration of captive carry exposure during the missile's service life can enable the implementation of predictive maintenance and resource management programs. Since the vibration imparted by each class of helicopter varies in frequency and amplitude, tracking the vibration exposure from each helicopter separately can help quantify the severity and harmonic content of the exposure. To help address these needs, the authors have developed a captive carry health monitor for the Hellfire II missile. The captive carry health monitor is an embedded usage monitoring device installed on the outer skin of the Hellfire II missile to record the cumulative hours the host missile has been in captive carry mode. To classify the vibration by class of helicopter, the captive carry health monitor analyzes the amplitude and frequency content of the vibration with the Goertzel algorithm to detect the presence of distinctive rotor harmonics. This article provides an overview of the captive carry health monitor, presents vibration data collected on missiles during captive carry, describes data analysis techniques used to monitor captive carry and identify the class of helicopter, and discusses the potential application of missile health and usage data for real-time reliability analysis. More broadly, this article illuminates the challenges of developing a structural health monitor to classify transportation modes in an unstructured environment.

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their em• ployees, makes any w.Jrranty, expressed or Implied, or Dsumes any legal IIDility or responsibility for the accuracy, completeness, or usefulness of any information, .tpparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government of any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

No abstract

Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty.