Aerospace (Aircrafts, Engines)
There is a strong interest in the aerospace community to increase the safety and reliability of aircraft while reducing operating and maintenance costs by replacing conventional wire-sensing systems with fiber-optics sensors. Research on structural health monitoring of aerospace vehicles and aircrafts have been reported in 2000 . Fiber-optic sensors have remarkable advantages in meeting the miniature size and weight requirements because of their improved multiplexing capability and their benefits including low cost and immunity to electromagnetic interference and lightning. One important type of sensor is a binary switch, which is actuated when the pilot presses a button . The signal from the switch is “0” when no force is applied, and “1” when the operator-induced strain in the sensing element exceeds a given threshold. As shown in Figure 6.6, the white-light interferometry (WLI) monitoring setup consists of two bulk-scanned Michelson interferometers sharing a common mirror translation stage. One interferometer uses a super-luminescent diode
Figure 6.6 Setup of WLI monitoring system.
(SLD) operating at central wavelength of 1325 nm as the light source for monitoring a sensing FFPI and a reference FFPI, while the second interferometer uses a distributed feedback (DFB) laser light source at a 1540 nm peak emission wavelength. The output light from the DFB interferometer is detected, and the resulting fringe pattern is processed to determine the position of the stage with high precision. One complete fringe corresponds to a displacement of АУ2 at the DFB laser wavelength of 1540 nm.
The FFPI contains two dielectric internal mirrors forming an FP cavity of length L within a single-mode fiber, so that the round-trip OPD within the cavity equals 2nL, with n the refractive index of the fiber mode. As the position of the translation stage is scanned, an interference pattern will be observed when |2nL - L < Lc, where Ls is the OPD of the SLD Michelson interferometer and Lc is the coherence length of the SLD. The maximum amplitude of the fringe pattern (center of the central fringe) is observed when Ls = 2nL. Fringe patterns from the sensing FFPI, the reference FFPI, and the DFB interferometer are collected by a data acquisition board and processed by a personal computer.
In 2010, an extrinsic FPI-based high-temperature pressure sensor was invented for avionics application . On the other hand, the development of instruments for detecting is also important for universe exploration. A new tunable FPI spectral camera has been reported in 2011, which makes it possible to collect spectrometric image blocks with stereoscopic overlaps using lightweight unmanned airborne vehicles (UAVs) platforms. It is an important sensing application, because this technology is increasingly needed in various environmental measurement and monitoring applications.
The FPI-based spectral camera developed by the VTT provides a new way to collect spectrometric image blocks. The imager is based on the use of multiple orders of FPI together with the different spectral sensitivities of red, green, and blue pixels of the image sensor. With this arrangement, it is possible to capture three wavelength bands with a single exposure. When the FPI is placed in front of the sensor, the spectral sensitivity of each pixel is a function of the interferometer air gap. By changing the air gap, it is possible to acquire new set of wavelengths. With smaller air gaps, it is also possible to capture only one or two wavelengths in each image. Separate short- pass and long-pass filters are needed to cut out unwanted transmissions at unused orders of the FPI. During a flight, a predefined sequence of air-gap values is applied using the FPI camera to reconstruct the spectrum for each pixel in the image. The principles of the FPI spectral camera have been described by Saari et al.  and Makynen et al. .
The general data processing chain for FPI spectral image data has been shown in Figure 6.7. The processing chain includes data collection, FPI spectral data cube generation, image orientation, digital surface model (DSM) calculations, radiometric model calculations,
and output product generation. When developing the data processing chain for the FPI spectral camera, our objective is to integrate the sensor-specific processing steps into our existing processing line based on commercially available photogrammetric and remote sensing software. The FPI spectral data cube is a crucial step in the data processing chain, which includes three major phases: radiometric image corrections based on laboratory calibrations, spectral smile corrections, and band matching. The challenging part of processing the FPI data is that the bands in the spectral data cube are collected with a small time delay. The approach mentioned above was to select a few reference bands and determine their exterior orientations. The FPI sensor provides many alternative ways for processing the data.
Besides, an FFPI was also used as an optical resonator in a dualfrequency optical source system for navigation application in 2015 . In this system, an optical reference source provides output signals at frequencies, where output difference frequency is in a specific range.