Parameter measurement systems and methods having a neural network comprising parameter output means

A system for measuring the value of a parameter, e.g., structural strain, includes an optical waveguide, a laser or equivalent light source for launching coherent light into the waveguide to propagate therein as multi modes, an array of a plurality of spaced apart photodetectors each comprising a light receptor surface and signal output, said array being arranged to have light emitted from said waveguide output portion irradiate said light receptor surfaces, an artificial neural network formed of a plurality of spaced apart neurons, connectors to impose weighted portions of signal outputs from the photodetectors upon the neurons which register the parameter value on a meter or like output device.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This application relates to systems and methods for measurement of a 
variety of parameters, e.g., strain, temperature, pressure, etc. More 
particularly, it concerns such systems and methods that comprise unique 
combinations of optical waveguides, e.g., optical fibers, operated in 
multi-mode to generate modal interference patterns and neural networks to 
interpret such patterns to obtain a value for the parameter being 
measured. 
2. Description of the Prior Art 
The use of optical fibers for the sensing of strain is well-known and this 
has been done in a variety of applications, e.g., see U.S. Pat. Nos. 
4,295,738; 4,611,378; 4,653,906 and 4,947,693. 
Several different classes of optical sensors have been investigated, each 
having particular advantages and disadvantages. One class of particular 
interest is the "few-mode" sensor in which an optical fiber is used that 
will support two or more lower order propagation modes. As the fiber is 
subjected to strain, the effective propagation constant for each of the 
modes is altered in such a way that the relative phase between each mode 
is shifted a different amount in proportion to the strain. Thus, at the 
output end of the fiber, all propagating modes interfere, producing an 
intensity pattern in space which varies with the induced strain. If the 
fiber is constructed so only the two lowest order modes propagate therein, 
the interference of these results in two intensity lobes at the output. 
The light intensity is measured by a photodetector within the spatial 
illumination area of one of the lobes. As the applied strain is increased, 
the intensity pattern alternates through successive light/dark transitions 
(fringes) producing a sinusoidal output signal from the photodetector. To 
process the detected signal, the modes must be constrained not to rotate 
with respect to each other. If this is not done, for example in the case 
of a sensor made from standard circular core fiber, at higher strain 
levels the intensity pattern rotates so an elliptical core fiber is used 
to prevent such pattern rotation. Thus, the usual two mode sensor has 
limited dynamic range and requires special and expensive optical fibers to 
operate. 
If one looks at the other extreme, i.e., the use of standard, low cost, 
multimode optical fiber where hundreds or thousands of modes propagate, 
the interference pattern, usually referred to as a speckle pattern, is 
composed of a very intricate and complicated intensity distribution. In 
this case the pattern is more sensitive to strain and other parameters 
(e.g., temperature) as compared with the case when only two modes 
propagate producing patterns that do not repeat over a large range of 
strain values. However, it is not easy to process the latter output signal 
to make full use of the sensitivity and dynamic range possible with the 
N-mode optical fiber sensors. One possibility would be laboriously to 
store the intensity distribution data for each strain value. To determine 
an unknown strain value one could then use image processing techniques to 
correlate stored images with the one produced from the unknown amount of 
strain thus identifying the unknown strain value. A serious limitation of 
this technique is that very high resolution imaging systems are required 
consequently demanding increased computer processing time to obtain a 
result. This might require a very expensive system where only static (vs. 
dynamic strain) might be able to be processed due to the long processing 
time required for segments of the dynamic waveform. The present invention 
provides new practical systems and methods for making full use of the 
sensitivity and dynamic range possible with the N-mode optical fiber 
sensors in measurement of strain, temperature and other parameter values. 
OBJECTS 
A principal object of the invention is the provision of new systems and 
methods for measurement of a variety of parameters, e.g., strain, 
temperature, pressure, etc. 
Further objects include the provision of: 
1. Such systems and methods that comprise the unique combination of an 
optical waveguide operated in a multi-mode manner to produce spatial 
intensity output patterns and neural network signal processing 
architecture to interpret such patterns to provide a value for a parameter 
being measured with such system and method. 
2. New strain measurement systems and methods that employ low cost, 
standard circular core optical fibers in contrast to expensive, elliptical 
core optical fibers typically used in some known fiber optic strain 
sensors. 
3. Such strain measurement systems and methods that can be used to monitor 
strain in bridges, buildings, aircraft, space structures and vehicles, 
satellites, ocean vehicles and offshore drilling platforms and other ocean 
structures. 
4. New parameter measurement systems and methods that can be used to 
measure strain, pressure, compression, temperature, electrical fields, 
magnetic fields and chemical features. 
Other objects and further scope of applicability of the present invention 
will become apparent from the detailed descriptions given herein; it 
should be understood, however, that the detailed descriptions, while 
indicating preferred embodiments of the invention, are given by way of 
illustration only, since various changes and modifications within the 
spirit and scope of the invention will become apparent from such 
descriptions. 
SUMMARY OF THE INVENTION 
The objects are accomplished, in part, in accordance with the invention by 
the provision of a system for measuring the value of a parameter which 
comprises (a) an optical waveguide having an input portion and an output 
portion, (b) means for launching coherent light into the input portion to 
propagate as multi modes in the waveguide, (c) an array of a plurality of 
spaced apart photodetectors each comprising a light receptor surface and 
signal output, the array being arranged to have light emitted from the 
waveguide output portion irradiate the light receptor surfaces of the 
photodetectors, (d) an artificial neural network comprising at least one 
layer of a plurality of spaced apart neurons, (e) connector means to 
operatively impose weighted portions of the photodetector signal outputs 
upon the neurons and the neural network further comprising (f) parameter 
value output means. 
In preferred embodiments, the neurons are equal at least in number to the 
numbers of the photodetectors, the artificial neural network comprises a 
succession of separate layers comprising a first layer of spaced apart 
neurons and at least second and third layers of spaced apart neurons. 
Also, the neurons of the first layer neurons connect with the 
photodetectors, the second layer neurons connect with the first layer 
neurons and the third layer neurons connect with the second layers 
neurons. 
Neural networks useable in accordance with the invention may take a variety 
of forms, for example, see Neurocomputing: picking the human brain, by R. 
Hecht-Nielsen, IEEE Spectrum Magazine, March 1988, ps. 36-41 and An 
Introduction to Computing with Neural Nets, by R. Lippmann, IEEE ASSP 
Magazine, April, 1987, ps. 4-26, the disclosures of which are incorporated 
herein by reference. 
The advantage of neural networks in the systems of this invention lies both 
in their capability to analyze complex sensor signal patterns and in their 
speed in calculating the appropriate paraameter value. For three-layer 
perceptron networks, such as discussed infra, the processing speed is just 
three gate delays after off-line training, regardless of the number of 
inputs and outputs. This results in a total processing time that can be as 
small as a few tens of nanoseconds or less. The neural network learns the 
correct "algorithms" by example during training and is able to generalize 
to untrained inputs after training is completed. The inputs to the neural 
network are the sampled values of the fiber-optic sensor output intensity 
distribution, and the neural network output is the parameter value. 
Neural nets have been primarily implemented using software simulation 
programs on conventional computers, but have now been developed in silicon 
parallel processing chips, optical systems, and hybrid electrooptic 
systems that use electrooptic components with optical interconnects. 
Neural networks are processors that possess, to a very limited extent, some 
of the capabilities of the brain in learning, memorizing, and reacting. 
They therefore posses a capability to solve problems that are not easily 
defined algorithmically and to process parallel inputs in real time. 
In a preferred embodiment, the system of the invention is for measuring 
strain and comprises (A) a longitudinal optical fiber having a core of 
substantially uniform circular cross-section throughout its length having 
an input end and an output end, (B) means to launch a beam of coherent 
light into the input end to propagate though the optical fiber in multi 
mode and exit the output end as modal spatial intensity patterns, (C) an 
array of a plurality of spaced apart photodetectors each comprising a 
light receptor surface and signal output, such array being arranged to 
permit light emitted from the output end to irradiate the light receptor 
surfaces to produce an electric signal at the signal output, (D) an 
artificial neural network comprising a plurality of spaced apart neurons, 
(E) connector means to operatively impose weighted portions of the 
photodetector signal outputs upon the neurons and (F) the neural network 
further comprises strain value output means. 
The objects are further accomplished in accordance with the invention by 
the provision of methods of measuring the value of a parameter which 
comprises (1) providing an optical waveguide having an input portion and 
an output portion, (2) imposing some intensity value of the parameter on 
the waveguide, (3) launching a beam of coherent light into the input 
portion of the waveguide so that such light propagates as multi modes in 
the waveguide and exits the output portion as a modal spatial intensity 
pattern related to the intensity of the parameter, (4) providing an array 
of a plurality of spaced apart photodetectors each comprising a light 
receptor surface and signal output arranged to permit light emitted from 
the waveguide to irradiate the light receptor surfaces, (5) providing an 
artificial neural network comprising a plurality of spaced apart neurons, 
(6) imposing weighted portions of the photodector signal outputs upon the 
neurons and (7) obtaining the value of the parameter from the neural 
network. 
Advantageously, the neural network is subjected to a learning operation 
before the parameter value is obtained. 
A preferred method of measuring the value of strain imposed on a structure 
comprises (P1) providing a longitudinal optical fiber of substantially 
uniform circular cross-section throughout its length having an input end 
and an output end, (P2) imposing some intensity of strain on the optical 
fiber, (P3) launching a beam of coherent light into the input end to 
propagate in multi mode along the fiber and exit the output end as modal 
spatial intensity patterns, (P4) providing an array of a plurality of 
spaced apart photodetectors each comprising a light receptor surface and 
signal output arranged to permit light emitted from the fiber output end 
to irradiate the light receptor surfaces and produce electrical signals at 
the signal outputs, (P5) providing an artificial neural network comprising 
a plurality of spaced apart neurons, (P6) imposing weighted portions of 
the electrical signals upon the neurons via connections between the 
photodetector signal outputs and the neurons, and (P7) obtaining the value 
of the strain from the neural network.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
In order to relate the systems and methods of the invention to the state of 
the prior art, a typical response from a two-mode elliptical core optical 
fiber sensor (LPOI and LPII) is shown in FIGS. 6 and 7 where 250 
microstrain is sufficient to cause a complete transition from minimum to 
maximum detected intensity. The two graphs show that the dynamic range is 
limited to the unambiguous strain range of one half of a fringe. This is 
because the intensity varies sinusoidally with strain and thus there are 
an infinite number of strain values, both positive and negative with the 
same intensity value. By limiting the strain range to one half of a 
period, this ambiguity is eliminated. Hence, the optical fiber strain 
sensors that employ two-mode elliptical core fibers suffer from limited 
dynamic range in addition to requiring special and expensive optical 
fibers for operation. The present invention now makes it possible to use 
inexpensive optical fibers made with cores of circular cross-section and 
at the same time take advantage of the broad dynamic evaluation ranges 
associated with such fibers. 
With reference to FIG. 1, the system 2 for measuring the value of a 
parameter, e.g., comprises optical fiber 4 containing a core (not shown) 
of circular cross-section enclosed by cladding (not shown) having an input 
end 6 and an output end 8. 
Means 10 for launching coherent light into the input end to propagate as 
multi modes in the optical fiber 4 comprises the laser 12 and focusing 
lens 14. 
The light output 16 from fiber end 8 falls on the array 18 of a plurality 
of spaced apart photodetectors 20 each comprising a light receptor surface 
22 and signal output 24. In the embodiment of FIG. 1, the array 18 is 
positioned adjacent fiber end 8 so light emitted from the end 8 irradiates 
the light receptor surfaces 22, but a variety of other arrangements for 
the array 18 for irradiation of the surfaces 22 (not shown) are possible, 
e.g., individual optic fibers from output end 8 to each of the surfaces 
22, prisms or lens between the fiber end 8 and the surfaces 22, etc. 
FIGS. 8 and 9 show magnified interference patterns typical of those that 
will radiate from the end 8 of fiber 4. The three negative photographs in 
each of FIG. 8 and 9 were obtained by viewing the end 8 of fiber 4 through 
a microscope and recording the pattern on film in a camera positioned at 
the eyepiece. The photographs show the variation in the patterns with 
change in microstains imposed on the optical fiber as indicated on the 
right-hand side of the sheet. 
The system 2 further includes an artificial neural network 26, connector 
means 28 to operatively impose weighted portions of the electric signals 
from photodetector outputs 24 upon neurons 30 and strain value output 
means 31. 
FIG. 2 illustrates one embodiment of a neural network for use in accordance 
with the invention. This network 26 has a first layer of neurons 30 each 
connected to all the outputs 24 of array 18 by connectors 32, a second 
layer of neurons 34 each connected to all the neurons 30 by connectors 36, 
and a third layer of neurons 38 each connected to all the neurons 34 by 
connectors 40. The value output means 31 comprises a terminal from which 
an analog or digital signal emits to give an statement of the value of the 
strain imposed under the test conditions on the optical fiber 4. 
Connectors 32 useable in systems of the invention can be of various forms. 
For example, they may be direct wire connections. In more improved systems 
of the invention, they can be inline resistors of various resistive 
values, each being adjusted through programmed evaluation of a given 
system to the resistive value needed to give the highest degree of 
reliability of the system reported strain or other reported parameter 
value. In alternative systems, the connectors 32 can be any known form of 
signal antenuator, signal amplifier, or equivalent connector between an 
electrical signal output source and a receptive input. 
In a typical embodiment of the invention, the number of connectors 32 
between each output 24 and neurons 30 will be equal to number of neurons 
30, between neurons 30 and each neuron 34 will be equal to the number of 
neurons 34, etc. However, it is possible in accordance with the invention, 
that some applications of the teachings of the invention will lead those 
skilled in the art to find that elimination of some of these connections 
do not reduce reliability of the values reported by such systems thereby 
reducing cost of their production and repair, or reduction or redundancy 
of connectors 32 have other beneficial results within the scope of this 
invention. Also, the number of neurons 30 need not be equal to the number 
of outputs 24 and the number of neurons 34 need not be equal to the number 
of neurons 30. 
The value output means 31 of the neural network 26 may take a variety of 
forms. In the network 26, means 31 comprises six neurons 38 whose output 
signals feed into the meter 42 to register the magnitude of the strain 
imposed on the optical fiber 4. In other networks (not shown), some of the 
neurons 38, e.g., three, could feed the meter 42 while others could feed a 
second meter (not shown) to register polarity of the strain. 
In use of the system 2 to measure strain in a structure, e.g., a wing 
strut, the fiber 4 may be coupled in any suitable fashion to such 
structure. When so assembled, some intensity of strain on will be imposed 
on the structure (not shown) and this, in turn, will impose a 
corresponding strain on the optical fiber 4. A beam of coherent light is 
then launched by laser 12 via the lens 14 into the input end 6 of fiber 4 
to propagate in multi mode along the fiber 4 and exit the output end 8 as 
modal spatial intensity patterns, e.g., as shown in FIGS. 8 and 9. These 
irradiate the light receptor surfaces 22 of the photodetectors 20 and 
produce electrical signals at the signal outputs 24 which are transmitted 
as weighted signals in turn to the neurons 30, 34 and 36 of the artificial 
neural network 26 thereby displaying the value of the strain being imposed 
on the structure on the meter 42. 
The multilayer perceptron architecture shown in FIG. 2 is a well-proven 
structure, and its use with back propagation for learning is a 
well-documented system. The multilayer perceptron training is based on 
back-propagation algorithms. The network is trained by providing sets of 
training values to the input, letting the network calculate a set of 
outputs, and comparing the calculated and desired outputs. The error 
between them is used to modify the internodal weights using an appropriate 
learning algoritlun. Another set of training data is then input to the 
network. This continues until the weight values converge to values that 
result in an output of sufficient accuracy for the complete set of 
training data. Once the network is trained, the weights are fixed, and it 
can be used to calculate the appropriate outputs with any similar input 
data, not only the training set.