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We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications
(2019)
We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.