A novel fiber optic distributed temperature and strain sensor for building applications

This paper introduces the use of a Rayleigh backscatter-based distributed fiber optic sensor to map the temperature field in air flow for a thermal fatigue application. The experiment involves a pair of air jets at 22 and 70 °C discharging from 136 mm hexagonal channels into a 1 × 1 × 1.7 m tank at atmospheric pressure. A 40 m-long, ϕ155 µm fiber optic sensor was wound back and forth across the tank midplane to form 16 horizontal measurement sections with a vertical spacing of 51 mm. This configuration generated a 2D temperature map with 2800 data points over a 0.76 × 1.7 m plane. Fiber optic sensor readings were combined with PIV and infrared measurements to relate flow field characteristics to the thermal signature of the tank lid. The paper includes sensor stability data and notes issues encountered using the distributed temperature sensor in a flow field. Sensors are sensitive to strain and humidity, and so accuracy relies upon strict control of both.

In a distributed fiber optic temperature sensing system, the intensity of Raman Stokes backscattering light serving as reference light increases with the increase of temperature, leading to measurement errors in the system. A novel method of dynamically calibrating Raman Stokes backscattering light intensity is proposed to improve temperature accuracy for distributed fiber optic temperature sensors. According to the real-time Stokes intensity distribution in the reference fiber, Stokes intensity curve of the whole fiber at a reference temperature is simulated, and the temperature response of Stokes light is corrected. The ratio of Raman anti-Stokes light intensity to the calculated Stokes light intensity is used to demodulate temperature along the fiber. The experimental results indicate that the temperature accuracy of the distributed optical fiber temperature sensor system after making the Stokes optical dynamic calibration is increased up to 4.3 ℃ compared with that from the conventional method. And the accuracy of temperature measurement is improved by 8.9 ℃ when combined with Rayleigh noise suppression method. This study provides a new solution for a distributed fiber optic temperature sensor system to monitor high temperature environment temperature.

We report on an approach for reducing the effects of temperature in a fiber optic distributed sensor. This technique employs a sensing fiber and a Brillouin optical time domain reflectometer (BOTDR). The BOTDR has been proposed for measuring both strain and optical loss distribution along optical fibers by accessing only one end of the fiber. The BOTDR analyzes changes in the Brillouin frequency shift caused by strain. This device can measure distributed strain with an accuracy of better than plus or minus 60 X 10-6 and a high spatial resolution of up to 1 m over a 10 km long fiber. However, temperature fluctuations have an adverse effect on the accuracy with which the Brillouin frequency shift can be measured because the shift changes with temperature as well as with strain. This has meant that both spatial and temporal fluctuations in temperature must be compensated for when a fiber optic distributed sensor is used for continuous strain measurements in massive civil structures. We describe a method for the simultaneous determination of distributed strain and temperature which separates strain and temperature in a fiber optic sensor.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Distributed Brillouin scattering sensor was employed to measure the hoop strain in an internally pressurized steel pipe with wall thinning due to erosion. The difference of Brillouin frequency shift from regions that have different degree of wall thickness lost can be observed on strain distribution. The strain from thin wall region is higher than that from the thick wall region. Therefore, the inner wall thinning can be discriminated from the corresponding strain measurements, which shows that fiber optic sensor technology based on distributed Brillouin scattering offers the great potential as a nervous system for infrastructure elements that allow high performance, cost effective health and damage assessment systems to be achieved.

One of the problems that arise during the design of a fiber-optic distributed temperature sensor is signal filtering. A high noise level occurs due to the optoelectronic conversion and the low power of the initial signal. A low signal-to-noise ratio leads to degradation characteristics of the fiber-optic temperature sensor. Averaging of multiple signals is often used to solve this problem. It significantly reduces device performance. Therefore, it is necessary to investigate and develop effective methods of signal filtering. In this paper, a filtering method based on the wavelet transform is considered. The method consists in splitting the signal into frequency bands by discrete wavelet transform and restoring the signal from the selected frequency bands. But at the same time, the choice of a wavelet plays a significant role. This paper discusses Bi-orthogonal wavelets, Daubechies, Coiflets, and Symlets. As a result, optimal wavelets were found for filtering the signal from the fiber-optic temperature sensor. An important advantage of this method is a slight decrease in spatial resolution with a significant reduction of noise level.

Raman scattering in optical fibres provides a means of measuring continuous temperature distributions over extended distances. However, in many applications it has been found that the background transmission properties of the optical fibre either change after deployment of the fibre or vary over time due to ageing effects. Changes in the fibre transmission can be distinguished from thermal effects by measuring the Rayleigh backscatter, which is relatively insensitive to temperature. The combined use of Raman and Rayleigh data is discussed here in the context of a particularly simple and cost-effective sensor design, where a single, fixed optical filter and a single light source are used for both measurement modes. The Rayleigh backscatter measurement allows the accurate correction of background transmission changes in a probe arrangement that includes several splices between different fibres with different attenuations. In this way, the temperature in a 290 °C test region is accurately recovered from the anti-Stokes Raman signal. However, preliminary measurements of the fibre attenuation as a function of temperature and exposure to an accelerated ageing environment show that these two effects will be difficult to separate in practice. Therefore it remains challenging to perform accurate, unambiguous background corrections in situations where high-temperature ageing occurs.

In this study, we developed a health monitoring system using a fiber optic distributed strain sensor for International America's Cup Class (IACC) yachts. Most structural components of an IACC yacht consist of an aluminum honeycomb core sandwiched between carbon fiber reinforced plastic (CFRP) laminates. In such structures, delamination, skin/core debonding and debonding between adhered members will be result in serious fracture of the structure. We equipped two IACC yachts with fiber optic strain sensors designed to measured the distributed strain using a Brillouin optical time domain reflectometer (BOTDR) and to detect any deterioration or damage to the yacht's structures caused by such failures. And based on laboratory test results, we proposed a structural health monitoring technique for IACC yachts that involves analyzing their strain distribution. Some important information about structural conditions of the IACC yachts could be obtained from this system through the periodical strain measurements in the field.

A 52 well heavy oil field development targeting shallow (3,400-4,000 ft TVD) sands on the north slope of Alaska was initiated in 2008. Horizontal wells of 11,000 to 13,000 ft MD were drilled early in the program. These initial wells served as "data-gathering and technology-proving" opportunities leading up to the eventual 23,000 ft MD wells. Key technical challenges include equivalent circulating density (ECD) and drag management. ECD management became essential in the 8½" production hole section of the longer wells. A relatively narrow (<600 psi) mud weight window necessitated changes to casing, drill-string, drilling fluids, and operational parameters. Lighter weight production casing allowed drilling a larger production hole (8¾" vs. 8½"). A tapered drill-string, reduced mud rheology, and reduced flow rate all became a necessary part of the ECD management solution. Advanced drag management techniques are required to install the 9⅝" production casing, 5½" production liner, and 4½"x3½" intelligent inner-string completion. The 9⅝" casing is installed using the "buoyancy assist" method (ie, "floatation") so it may be "pushed" and reamed in the hole beyond the point of negative weight. The lower completion consists of a 5½" slotted liner with swell packers. Centralizers on the liner were changed from non-rotating to fixed, which allowed breaking axial drag while reaming the liners to depth. Extensive torque and drag modeling was used to plan intelligent inner-string completions on the injector wells, which included injection control devices, swell packers, and a fiberoptic distributed temperature sensor (DTS) to monitor injectivity. The full length paper will discuss the technical challenges, well design solutions, and operational practices that were trialed and implemented to enable extended reach wells to be successfully drilled on the edge of the industry experience envelope with all wells meeting targeted objectives.

The signal quality of a fiber-optic distributed temperature sensor based on optical time domain reflectometry (OTDR) is improved using a pre-filtering technique in the iterative deconvolution method. Investigation of the performance is proposed using iteration conditions in which recovery rate of the signal-component is equal to that of the unfiltering technique. The recovered signal in the pre-filtering technique gives a good signal-to-noise ratio (SNR), 3 times of the unfiltered signal. Consequently, improvements in the OTDR signal with SNR of from 10 to 100 are obtained under the iterations for both distance and temperature resolutions.

Raman distributed temperature sensor (RDTS) measurements were performed during γ-radiation on three different classes of standard multimode fibers (pure, Ge-doped and F-doped). The sensor response is affected by the radiation induced attenuation phenomena leading to errors in the temperature measurements. The amplitude of this error strongly depends on the fiber type and the irradiation conditions. These results are promising in view of the integration of these RDTS into the deep geological repository for radioactive waste.

This article presents the structural assessment of an existing old steel railway bridge in Malaysia using fibre optic sensors. The steel bridge was built more than a century ago and the bridge structural drawings and other documentations were lost over the years. Keretapi Tanah Melayu Berhad (KTM), the Malaysian rail operator was upgrading the railway line to cater for heavier and faster locomotives. The bridge is subjected to a potential risk of structural health deterioration or fatigue due to corrosion and long service period in such a high humidity country. Full scale static and dynamic tests using locomotives were carried out to assess the structural integrity of the steel bridge. Two types of optical sensors were used. Discrete and distributed fibre optic sensors were mounted on critical steel panels to monitor the change in strain of these members under dynamic and static loads. The discrete sensor, Fibre Bragg Grating (FBG) strain sensors were able to measure the change in strain at 1000 Hz frequency, and were used during the dynamic test where a locomotive travelled through the bridge at maximum speed of 40 km/hour. Distributed fibre optic (DFOS) sensors were mounted on the bottom chord of the steel truss to measure the maximum tensile strain when the locomotive was placed at the centre of the bridge. The results from the two different fibre optic sensors (FBG and DFOS sensors) were then compared and the results showed good agreement to strain measurements. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making.

With the increase worldwide demand for hydrocarbon fuels and the vast development of new fuel production and delivery infrastructure installations around the world, there is a growing need for reliable hydrocarbon fuel leak detection technologies to provide safety and reduce environmental risks. Hydrocarbon leaks (gas or liquid) pose an extreme danger and need to be detected very quickly to avoid potential disasters. Gas leaks have the greatest potential for causing damage due to the explosion risk from the dispersion of gas clouds. This paper describes progress towards the development of a fast response, high sensitivity, distributed fiber optic fuel leak detection (HySense™) system based on the use of an optical fiber that uses a hydrocarbon sensitive fluorescent coating to detect the presence of fuel leaks present in close proximity along the length of the sensor fiber. The HySense™ system operates in two modes, leak detection and leak localization, and will trigger an alarm within seconds of exposure contact. The fast and accurate response of the sensor provides reliable fluid leak detection for pipelines, storage tanks, airports, pumps, and valves to detect and minimize any potential catastrophic damage.

Traditional ground sensors can be used as security systems to detect third party intrusion, but are costly and cumbersome to scale up to cover a large area. Fiber optic distributed acoustic sensing (DAS) offers a solution that economically and instantaneously monitors up to 50 linear kilometers of a border or perimeter with a single system. The output of the OptaSense ODH-3 fiber interrogation unit is spatially dense, coherent and exhibits characteristics expected of traditional strain sensors. In this paper, DAS data is shown to indicate a walker's directionality and support beamforming algorithms for localization.

We aim to classify acoustic events recorded by a fiber optic distributed acoustic sensor (DAS). We derived the information from probing the fiber with light pulses and analyzing the Rayleigh backscatter. Then, we processed this data by a pipeline of processing algorithms to form the input for our machine learning classification model. We put random matrix theory to the test to distinguish the acoustic event of interest from the noise. We conditioned the raw trace using moving average and wavelet-based filtering algorithms to improve the signal-to-noise ratio. For raw, low pass, and wavelet-based filtered signals that we inject into a convolutional neural network, we rely on the magnitude of their complex coefficients to categorize the nature of the event. We also investigate Mel-Frequency Cepstral coefficients specific to the event as an input for the classifier and compare their performance to other signal representations. We run the experiments on the CNN for two-class and three-class classification using datasets from a DAS that is deployed for perimeter security and pipeline monitoring. We obtained the best results when using the MFCCs paired with wavelet denoising, achieving accuracies of 96.4% for the “event” class and 99.7% for the “no event” class when it comes to the two-class process. The three-class process yielded optimal accuracies of 83.3%, 81.3%, and 96.7% for the “digging,” “walking,” and “excavation” classes, respectively. Finally, the training execution time is exceptionally long because the extensive dataset and the model’s architecture are complex. As a result, we make efficient use of the CPU and GPU to maximize our machine’s power using the Keras API’s sequence data generator. Compared with the serial implementation, we report an improvement of up to 4.87 times.

We report about a laser Doppler velocity profile sensor for precision measurements of microfluidic flows. The sensor measures the velocity and position of a tracer particle in the measurement volume, which consists of two superposed interference fringe systems of different wavelength, one with convergent, one with divergent interference fringes. This arrangement was achieved with a special combination of a diffractive and achromatic lenses. Due to fiber-optic beam delivery the sensor can be build compact and offers potential for miniaturization. A spatial resolution in the sub-micrometer range and an uncertainty of the velocity of about 10-4 could be achieved. The sensor offers potential for a variety of applications, like the investigation of shear flows or precise flow rate measurments.