Barry G. Grossman

Composite damage assessment employing an optical neural network processor and an embedded fiber-optic sensor array

This paper discusses a novel approach for composite damage assessment with potential for DoD, NASA, and commercial applications. We have analyzed and modeled a two-dimensional composite damage assessment system for real-time monitoring and determination of damage location in a composite structure. The system combines two techniques: a fiberoptic strain sensor array and an optical neural network processor. A two-dimensional fiberoptic sensor array embedded in the composite structure during the manufacturing process can be used to detect changes in the mechanical strain distribution caused by subsequent damage to the structure. The optical processor, a pre-trained Kohonen neural network, has the capability to indicate the location of the damage due to its positionally associative architecture. Because of the parallel, all optical architecture of the system, the system has the advantages of having high resolution, a simple architecture, and almost instantaneous processor output. Results of the modeling and simulation predict a highly robust system with accurate determination of damage location. We are currently beginning work on a breadboard demonstration model of the system.

Fiber-Optic Sensor And Smart Structures Research At Florida Institute Of Technology

This paper discusses the fundamental issues being investigated by Florida Institute of Technology (F.I.T.) to implement the technology of smart structural systems for DoD, NASA, and commercial applications. Embedded sensors and actuators controlled by processors can provide a modification of the mechanical characteristics of composite structures to produce smart structures1-3. Recent advances in material science have spurred the development and use of composite materials in a wide range of applications from rotocraft blades and advanced tactical fighter aircraft to undersea and aerospace structures. Along with the advantages of an increased strength-to-weight ratio, the use of these materials has raised a number of questions related to understanding their failure mechanisms. Also, being able to predict structural failures far enough in advance to prevent them and to provide real-time structural health and damage monitoring has become a realistic possibility. Unfortunately, conventional sensors, actuators, and digital processors, although highly developed and well proven for other systems, may not be best suited for most smart structure applications. Our research has concentrated on few-mode and polarimetric single-fiber strain sensors4-7 and optically activated shape memory alloy (SMA) actuators controlled by artificial neural processors. We have constructed and characterized both few-mode and polarimetric sensors for a variety of fiber types, including standard single-mode, high-birefringence polarization preserving, and low-birefringence polarization insensitive fibers. We have investigated signal processing techniques for these sensors and have demonstrated active phase tracking for the high- and low-birefringence polarimetric sensors through the incorporation into the system of an electrooptic modulator designed and fabricated at F.I.T.. We have also started the design and testing of neural network architectures for processing the sensor signal outputs to calculate strain magnitude and actuator control signals for simple structures.

Smart structures and fiber optic sensor research at Florida Institute of Technology: 1990

This paper discusses several novel concepts being investigated in the Center for Fiberoptic Sensor Systems and Smart Structures at Florida Institute of Technology associated with fiberoptic sensors, actuators and processor technology, and efforts to integrate these components into distributed smart systems. Projects include: a polarimetric sensor with active phase tracking test set, a combination polarimetric/two mode sensor, an N-mode sensor with neural processor, damage assessment using embedded fiber-optic arrays and a neural processor, a pulsed interferometric sensor, neural network-processed polarimetric sensor signals, and optically-energized shape-memory alloy actuators.

THREE-DIMENSIONAL STRUCTURAL STRAIN MEASUREMENT WITH THE USE OF FIBER-OPTIC SENSORS

Three-dimensional strain sensing inside a structure is not feasible with conventional strain sensing techniques such as electrical strain gauges, which are limited to surface measurements. Three-dimensional strain measurement inside a structure would find uses in a variety of new applications: enhanced understanding and detection of composite failure modes, such as delamination; sensing for adaptive structural control; intelligent vehicle highway systems; and structural health monitoring systems for civil structures. The latter application could involve remotely monitoring structural integrity during and after an earthquake, for example. A fiber-optic strain sensor array (FOSSA) in a planar, patch-like configuration was developed, and accurate measurement of the three principal strains inside a simple structure was demonstrated. The planar configuration was chosen to avoid the difficulty and structural degradation of embedding optical sensors in three planes. Two extrinsic Fabry-Perot interferometric (EFPI) sensors and one polari-metric sensor form the planar sensor array. The two EFPI sensors were placed perpendicular to each other in the sensor plane to extract the two normal strain components along the x and y axes. The polarimetric sensor embedded in the plane was used to extract the third normal strain acting on the z axis. The sensor array was embedded in an epoxy resin cube and loaded to 454 kg (1,000 1b) with a loading machine. The strains that were measured correlated well with the external strains measured with surface-bonded electrical strain gauges. The variation in measured strain between the two sensor systems was less than 4 percent for all three principal axes.

Fiber optic pore water pressure sensor for civil engineering applications

Low cost, rugged and reliable fiberoptic sensors are being developed to meet the needs of geotechnical engineers. The primary emphasis has been on load and pressure sensors, including pore water pressure sensors. The microbend sensors developed have been tested in the laboratory up to water pressures of 100 psi and loads of 50 lb. Accuracy of sensor measurements are within 5% and is being improved upon. Sensors with larger range or more sensitivity can easily be built without changing the basic sensor design. A semi-automated calibration and testing system was developed to characterize the sensors. In this paper we describe some of the applications for the sensors, their construction, characterization system, and experimental performance.

Optical processors for smart structures

An all-optical processor for sensor inputs capable of computing the resultant strain and optical control signals for such applications as underwater fiber-optic sensor arrays and smart aerospace structures is presented. Attention is given to computer simulation and experimental results obtained to date, which indicate that neural network architectures can perform the very high speed calculations with the requisite accuracy. The performance thus far obtainable in the available bistable optical gate arrays is the basic performance limitation identified for this all-optical implementation.

Rotationally insensitive circular-core two-mode fiber-optic strain sensor.

Conventional two-mode fiber-optic strain sensors measure strain by inducing a path difference between the two propagating modes and spatially interfering the modal output pattern. At high strain values, the output mode pattern changes (rotates), limiting the range of measured strain. We have applied a mode separation/recombination technique and demonstrated it with a two-mode strain sensor, resulting in a rotationally invariant/stable output mode pattern and extended range of measured strain. The sensor was designed to measure strain, but with very little modification, it can measure temperature, pressure, electric and magnetic fields, etc. The improved rotationally invariant two-mode fiber-optic strain sensor performs to within 2% of standard electrical strain gauges.

Development of microbend sensors for pressure, load, and displacement measurements in civil engineering

We are developing low cost, rugged, and reliable fiberoptic sensors to meet current and future needs in civil engineering, including those of smart civil structures. Our work has concentrated on load, pressure, and displacement sensors, including pore water pressure sensors. We have built and demonstrated sensors in the laboratory with loads up to 50 lb., water pressures of 100 psi, and displacements up to 1 mm. Repeatability of sensor measurements are within 5% and are being improved with continued development. The range and sensitivity of the sensors can be easily changed without changing the basic sensor design. We also have multiplexed two water pressure sensors on a single fiber. We describe the sensor construction and experimental performance.

Smart electromagnetic structures: a neural network antenna

Artificial neural networks(ANN5) and their ability to model and control dynamical systems for smart structures including sensors actuators and plants are being considered in our lab. Both linear and non-linear systems have been successfully modeled. We are presently working on two diverse regimes smart mechanical systems and smart electromagnetic systems. In order to better understand neural controllers as used in the smart electromagnetic structures we have directed our study of ANNs toward understanding the ability of the network to approximate system responses. We are training networks to mimic the desired output of the system. The damped sinusoid was chosen as the model and was approximated using a Jordan-like 8 iterative network. The results to date indicate that the ANNs can easily mimic these systemsthe question is whether the mechanism that the network applies can be related to the mechanisms that we understand for classical analysis. Sensor preprocessing represents a significant element in the smart material and structure concept. We are looking at certain network architectures as sensor preprocessors. Results from both these areas will be presented in this paper.

Neural network processing of fiber optic sensors and sensor arrays

For sophisticated smart structures where sensing and actuation is distributed over large areas or consists of dozens to thousands of discrete elements, the processing task is computationally intensive. Artificial neural networks offer an opportunity to implement a massively parallel architecture with near real time processing speed and the ability to learn the desired response. This overview of applied neural network processing projects at Florida Institute of Technology includes: processing polarimetric and N-mode strain sensor signals, damage assessment using embedded sensor arrays, and development of electrooptic neural networks