Structural Health Monitoring using Index Based Reasoning for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. ^ Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. ^ The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. ^ The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. ^ The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles. ^

Independent Monitoring the Cuban Economic Zone Oil Development

The extensive impact and consequences of the 2010 Deep Water Horizon oil drilling rig failure in the Gulf of Mexico, together with expanding drilling activities in the Cuban Exclusive Economic zone, have cast a spotlight on Cuban oil development. The threat of a drilling rig failure has evolved from being only hypothetical to a potential reality with the commencement of active drilling in Cuban waters. The disastrous consequences of a drilling rig failure in Cuban waters will spread over a number of vital interests of the US and of nations in the Caribbean in the general environs of Cuba. The US fishing and tourist industries will take major blows from a significant oil spill in Cuban waters. Substantial ecological damage and damage to beaches could occur for the US, Mexico, Haiti and other countries as well. The need exists for the US to have the ability to independently monitor the reality of Cuban oceanic oil development. The advantages of having an independent US early warning system providing essential real-time data on the possible failure of a drilling rig in Cuban waters are numerous. An ideal early warning system would timely inform the US that an event has occurred or is likely to occur in, essentially, real-time. Presently operating monitoring systems that could provide early warning information are satellite-based. Such systems can indicate the locations of both drilling rigs and operational drilling platforms. The system discussed/proposed in this paper relies upon low-frequency underwater sound. The proposed system can complement existing monitoring systems, which offer ocean-surface information, by providing sub-ocean surface, near-real time, information. This “integrated system” utilizes and combines (integrates) many different forms of information, some gathered through sub-ocean surface systems, and some through electromagnetic-based remote sensing (satellites, aircraft, unmanned arial vehicles), and other methods as well. Although the proposed integrated system is in the developmental stage, it is based upon well-established technologies.