Raman-based Distributed Temperature Research Articles

Calibration and Uncertainty Quantification for Single-Ended Raman-Based Distributed Temperature Sensing: Case Study in a 800 m Deep Coaxial Borehole Heat Exchanger

Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation.

Distributed temperature sensors operating at 840 nm for short-range sensing applications.

Raman-based distributed temperature sensor (RDTS) devices have grown dramatically over the past two decades, partially driving the optical sensor industry. Over nearly four decades, most academic investigations about RDTS have focused on developing distributed sensor devices operating at the wavelength of 1550nm, given the low loss of standard single-mode fibers in this spectral region. Certainly, the wavelength of 1550nm is ideal for long-range sensing applications. However, at this wavelength, the signal-to-noise ratio (SNR) of RDTS systems is degraded, given the low intensity of the measured signals. Looking for simple solutions to improve the SNR of this sensing technology, we discuss in this paper an RDTS operating at the spectral region of 840nm as an alternative for short-range distributed temperature sensing applications delivering an improved SNR.

A novel Deep-Learning model for RDTS signal denoising based on graph neural networks

Focusing on the problem of increasing the deviation of temperature measurement for Raman-based distributed temperature sensor (RDTS) caused by random noise, a new method of applying a three-layer GraphSAGE-based graph neural network (3L-GraphSAGE) to noise reduction is proposed, where the spatial relationship between each signal is first constructed, and then the effective denoised results are obtained from the developed 3L-GraphSAGE model. First, an experimental setup is built for collecting fiber signals. Then, the datasets are input into the 3L-GraphSAGE to train the model. Finally, the test datasets are input into the well-trained 3L-GraphSAGE model to obtain effective denoised signals. To evaluate the performance of 3L-GraphSAGE, three evaluation indexes are calculated, including maximum deviation (MD), root mean square error (RMSE) and smoothness. The experimental results show that it can efficiently suppress the random noise and reduce the temperature measurement deviation in RDTS compared with direct demodulation of the raw data, and significantly improve the curve smoothness compared with wavelet transform by soft threshold function (WT-soft) and fast waveform type (FWT). Therefore, 3L-GraphSAGE model can provide an available method for improving the performance of RDTS.

Raman scattering-based distributed temperature sensors: A comprehensive literature review over the past 37 years and towards new avenues

Over the last 37 years, particular interest has been directed towards exploring the characteristics of the optical environment for sensing, giving rise to what would now be one of the largest applications of well-known optical fibers, typically employed to transmit data at high rates. Sensing temperature, pressure, liquid level, deformation, and other physical parameters utilizing optical fibers has become a growing branch of research and a business competing with well-established electrical sensors in the industry. Optical fiber sensors have all the inherent characteristics of a fiber optic cable, such as electromagnetic immunity, small size and weight, multiplexing, and so on. These exclusive features have made fiber sensors so versatile as to become a transformative technology by enabling several industrial processes to be carried out with higher reliability. Nowadays, there are several optical sensors, including fiber Bragg grating, interferometric, polarimetric, polymer fiber, distributed, and several others. Specifically, Raman-based distributed temperature sensor (RDTS) is a class of fiber optic sensors broadly employed in temperature measurement of large structures such as oil and gas wells, tunnels, and pipelines. Since 1985, many techniques have been proposed to break through the barriers of exploring Raman scattering as a distributed temperature measurement method. Range, spatial and temperature resolutions have been the most investigated parameters. In this perspective, this paper presents a comprehensive review focused on the progress of the RDTS technology over the past 37 years (1985–2022), covering an analysis of over 500 journal papers. First, a brief introduction to fiber optic sensor technology is presented as a theoretical basis, discussing the emergence of distributed sensors. Subsequently, Raman scattering in optical fibers is introduced, as well as how this nonlinear effect can be used to build temperature sensors. Next, RDTS technology is detailed, followed by a discussion of its applications and evolution over nearly four decades of development. Lastly, future perspectives are addressed in this review for the advancements in distributed temperature sensor technologies.

Quench recovery time of an optical fiber encapsulated resistance-type DC superconducting fault current limiter coil determined by a Raman-based distributed temperature sensing system

The superconducting fault current limiter (SFCL), as a very important power facility for high voltage DC system, can consume fault current energy and make DC breakers cut off fault transmission lines successfully. For the moment of reclosing DC breakers, the quench recovery time of the SFCL must be determined in advance. In this article, according to the design requirement of a 160 kV/1 kA resistance type DC SFCL in the Nan’ao multi-terminal flexible HVDC transmission demonstration project, the quench recovery time of a SCFL coil wound by two optical fiber encapsulated high temperature superconducting (OFE-HTS) tapes was checked by our optimized Raman-based distributed temperature sensing (Rm-DTS) system. The experimental results show that the used OFE-HTS tapes can withstand a 1500 A/100 ms AC short circuit current impact and are qualified with the SFCL. Furthermore, the quench recovery time of the SFCL coil measured by the Rm-DTS was 2930 ms. During the quench, the highest temperature zone occurred near the center of the SFCL coil.

RDTS noise reduction: A fast method study based on signal waveform type

In order to reduce the measurement error of Raman-based distributed temperature sensor (RDTS) caused by random noise, we have proposed an effective noise reduction method, which can reduce the time consumption of data processing due to the low time complexity of the method. We classified the signal as five models based on the waveform types, thus to filter the raw data according to the waveform types. Then, we established an experimental device to test the effects of different signal noise reduction methods, including maximum deviation (MD), root mean square error (RMSE), smoothness, and time consumption. Experimental results show that it can efficiently suppress random noise and reduce temperature measurement errors in RDTS compared with direct demodulation of the raw data, and significantly improve the curve smoothness compared with D-SVD and WT-Soft. The time consumption test based on three different devices shows that the time consumption of the proposed method is approximately 10% of the D-SVD method, and about 30% of the WT-Soft method. It proves that this method is an effective and fast noise reduction method in RDTS systems.

Analytical investigation of the receiver for Raman-based distributed temperature sensors

The performance of Raman-based distributed temperature sensors is strongly dependent on the receiver system employed since the signal carrying the temperature information has extremely low intensity. Therefore, a noisy receiver can easily generate noise levels that overlap the signal strength, unable to detect changes in this signal due to temperature variations. The present study will address a theoretical analysis based on the sensitivity of the signal of interest with temperature in order to characterize the receiver under performance requirements. The main contribution of this work is to introduce in the mathematical formalism for the first time, to the best of our knowledge, the term that considers the sensitivity of the signal employed to measure the temperature. This provides a more accurate way of determining how to improve the sensor’s accuracy, range, and resolution. Moreover, our analytical model allows us to relate the components specifications of the electronic circuit of the receiver to the detected signal magnitude and the desired performance of the sensor.

High-Performance Raman Distributed Temperature Sensing Powered by Deep Learning

Raman-based distributed temperature sensing (RDTS) can achieve temperature measurement of tens of kilometers and is widely used in temperature monitoring of crucial facilities. The accuracy of RDTS is highly determined by the signal-to-noise ratio (SNR) of the acquired spontaneous Raman scattering (SpRS) signals, which is about 60 dB weaker than the pump pulse. Therefore, the performance of single-mode fiber (SMF) based conventional RDTS is poor. To improve its performance, many methods have been proposed, including the use of special optical fibers, pulse coding techniques, and denoising algorithms. However, these methods have their limitations. Here, we propose and experimentally demonstrate a deep 1-D denoising convolutional neural network (1DDCNN) to enhance the performance of RDTS. A simplified RDTS model is built to train and optimize the 1DDCNN. To verify the performance of the 1DDCNN on actual data, we experimentally measure the SpRS data of a 10-km SMF with an average time of 1 s and a spatial resolution of 3 m. The well-trained 1DDCNN reduces the temperature uncertainty from 6.4 °C to 0.7 °C with high fidelity. As a comparison, the commonly used wavelet denoising algorithm can only reduce it to 3.7 °C. Besides, the 1DDCNN does not require manual parameter adjustment and is not affected by the sampling rate, giving it significant advantages in practical applications compared with conventional denoising algorithms. Moreover, we believe that the proposed 1DDCNN can be applied to other distributed optical fiber sensing systems by fine-tuning the network with appropriate training data.

Detection of Multiple Small Temperature Events Simultaneously on a Distributed Temperature Map

Measuring temperature is essential in many industrial applications not only to preserve equipment lifespan but also for security reasons. One solution that has grown exponentially in recent years due to its distributed measurement capability is the Raman-based distributed temperature sensors (RDTSs) that enable the temperature to be mapped at several points along tens of kilometers of fiber optic cables. In spite of being a technology already present in the industry, it shows no sign of abating with progress being achieved in basic interrogation, accuracy improvements, and novel applications. Currently, the scientific literature in the area of distributed temperature sensing exhibits a consistent body of knowledge based on the solutions that allow temperature measurements with high precision and resolution. However, these approaches fail in situations when the temperature event is less than the equipment's spatial resolution, so that it cannot be detected correctly. In this perspective, we propose and experimentally demonstrate in this study an image processing technique applied to the temperature profile generated by these sensors that will allow circumventing this bottleneck of RTDS technology. As a result, the simultaneous detection and correction of hot spots of sizes ranging from 15 m to 3 cm are achieved experimentally with 98.22% accuracy. These results obtained here place the RDTS technology at a new level of applicability, in which new industrial applications can be developed.

Experimental research on a Raman-based distributed temperature sensor assisted by PCA for locating the temperature abnormal event of nuclear waste drums.

Aimed at locating the temperature abnormal event of nuclear waste drums in a nuclear waste temporary storage repository by a Raman-based distributed temperature sensor, a principal component analysis (PCA)-based method for application is proposed. The effectiveness of the proposed method is verified in the physical simulation device of the nuclear waste drums. First, some samples of the temperature abnormal event with known location are taken as the reference samples, and their features are extracted by PCA. Then, the features of the test sample data to be located are also extracted by PCA. The Euclidean distance is used to measure the similarity between the features of the test sample and the feature of each reference sample. Finally, we find the reference sample that is most similar to a test sample, the location of which is considered the location of the temperature anomaly event for the test sample. The experimental results show that the proposed method can accurately locate the temperature abnormal event of the nuclear waste drums, and the accuracy rate can reach 96%. The method that is proposed in this paper can reliably locate the temperature abnormal event generated by the nuclear waste temporary storage repository induced by external factors such as landslides or earthquakes, and provide technical support for nuclear safety.

Monitoring a Heatsink Temperature Field Using Raman-Based Distributed Temperature Sensor in a Vacuum and −173 °C Environment

A heatsink is a large experimental device which is used to simulate the outer space environment. In this paper, a Raman-based distributed temperature sensor was used for real-time and continuous heatsink temperature monitoring, and a special Raman-based distributed temperature sensing method and system have been proposed. This method takes advantage of three calibration parameters ( ) to calculate the temperature. These three parameters are related to the attenuation of the optical fiber, the Raman translation, and the difference of optoelectronic conversion, respectively. Optical time domain reflectometry was used to calculate the location. A series of heatsink temperature measurement experiments were performed in a vacuum and −173 °C environment. When the temperature dropped to −100 °C, the parameter was found to vary. A method was proposed to recalculate and modify the traditional Raman fiber temperature equation. The results of the experiments confirmed the validity of this modified Raman fiber temperature equation. Based on this modified equation, the temperature field in the heatsink was calculated. The Raman-based distributed temperature sensor has potential applications in temperature measurement and judging the occurrence of faults in space exploration.

Spatial and Temporal Resolution Optimization on Raman Distributed Temperature Sensor System for Quench Detection in a No-Insulated Coil

Quench detection and protection technologies are extremely important for high-temperature superconducting (HTS) magnet applications. In a previous work, we have verified the feasibility of using a Raman-based distributed temperature sensor (RmDTS) system to measure a no insulation (NI) HTS coil temperature changes in an overcurrent induced quench event. However, rough and slow temperature measurement is unsuitable for actual application on quench detection and protection. Therefore, we designed a new RmDTS system consisting of an RmDTS device whose temporal resolution is 15 ms and a thinner polyimide-coated optical fiber with a diameter of 150 μ m. Then, the optimized RmDTS system and an original one were, respectively, applied to detect the NI coil temperature during quenches. The comparative results show that the highest heat was generated on the 22nd turn of the NI coil, and the temperature response time of the original DTS system is 2 s slower than that of the optimized one. By using an optimization method, the spatial and temporal resolutions of the RmDTS system have been enhanced triple and 66 times than before.

Simultaneous measurements of distributed temperature and discrete strain based on Hybrid Raman/FBG system

A hybrid optic fiber system is proposed and experimentally demonstrated, which combines the Raman-based distributed temperature sensing (DTS) and high-reflective fiber Bragg grating (FBG) sensors for discrete static strain measurement along the same sensing fiber. A distributed feedback laser (DFB) is adopted to generate stimulated emission (STE) light pulses for demodulation of optical time domain reflectometry (OTDR) technology, and an erbium-doped optical fiber amplifier (EDFA) is utilized to increase the power of laser and produce spontaneous emission (SPE) lights. Thus the STE light is employed as pump light for temperature demodulation through Raman-based DTS, as well the SPE lights can be used for discrete strain demodulation through wavelength division multiplexing of FBGs. Since the central wavelengths of FBGs are far away from the lasing wavelength of the laser, the FBG-induced insertion loss can be negligible, therefore the lights reflected by FBGs have little influence on the performance of DTS system. The strain information can be obtained through deducting the temperature induced wavelength shifts of FBGs, since the temperature information can be achieved by DTS system in advance. The experimental results show the maximum measurement error of temperature and strain are 0.48 °C and 10.47 με, respectively. This hybrid system indicates an effective improved multiplexing scheme based on the Raman-based DTS for simultaneous measurements of distributed temperature and discrete static strain, and a better solution of cross sensitivity problem in FBGs.

Bend Limitation of a Polyimide-Coated Optical Fiber at Cryogenic Temperature of 77 K

A Raman-based distributed temperature sensor (RmDTS) system is a promising method for quench detection in high-temperature superconducting (HTS) magnets. To sense the temperature distribution of HTS magnets, optical fibers need to be wound with HTS wires synchronously. Accordingly, the bend limitation of the sensing optical fibers at 77 K is very important for an HTS coil temperature measurement. In this study, a segment with a 2.5-m-long polyimide-coated multimode optical fiber was bent as the curvature radii of 3, 5, 10, 15 and 25 mm, respectively, and applied to sense a room and a liquid nitrogen temperature. The experimental results of bending and without bending optical fiber show that when the curvature radius of the optical fiber is smaller than 10 mm, the temperature measurement is unreliable. The recommended minimal curvature radius of the used optical fiber for applying the RmDTS system to monitor the HTS coil temperature is 10 mm.

Research on noise reduction method of RDTS using D-SVD

Abstract Focusing on the problem of increasing the deviation of temperature measurement for Raman-based distributed temperature sensor (RDTS) caused by random noise, a method of applying dichotomize singular value decomposition (D-SVD) to noise reduction for RDTS is proposed, and a method for determining the number of D-SVD iterations by the result’s quality coefficient (RQC) is proposed to obtain effective denoised signals, thereby reducing the deviation of temperature measurement. Experiments were carried out at 45.2 °C, 50.0 °C, 55.0 °C, and 60.0 °C. Compared with the results obtained by direct demodulation of the observed signals, the maximum deviation (MD) of the temperature obtained by D-SVD noise reduction is reduced by 1.59 °C and 2.49 °C, 1.22 °C, and 0.87 °C, respectively. The root means square error (RMSE) is reduced by 0.79, 0.95, 0.67, and 0.40, respectively. And the smoothness of the temperature curve is significantly improved. At the same time, compared with the results obtained by wavelet transform by hard threshold function (WT-Hard) method and wavelet transform by soft threshold function (WT-Soft) method, the proposed method is better in these three indicators. Therefore, the proposed method can provide an available method for improving the performance of RDTS by using data processing methods.

Performance improvement in double-ended RDTS by suppressing the local external physics perturbation and intermodal dispersion

We propose and experimentally demonstrate a novel Raman-based distributed fiber-optics temperature sensor (RDTS) for improving the temperature measurement accuracy and engineering applicability. The proposed method is based on double-ended demodulation with a reference temperature and dynamic dispersion difference compensation method, which can suppress the effect of local external physics perturbation and intermodal dispersion on temperature demodulation results. Moreover, the system can omit the pre-calibration process by using the reference temperature before the temperature measurement. The experimental results of dispersion compensation indicate that the temperature accuracy optimizes from 5.6°C to 1.2°C, and the temperature uncertainty decreases from 16.8°C to 2.4°C. Moreover, the double-ended configuration can automatically compensate the local external physics perturbation of the sensing fiber, which exhibits a distinctive improvement.

Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1°C to 1.2°C at 40.0°C, 50.0°C and 60.0°C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

Abstract. Optical temperature sensors offer unique features which make them indispensable for key industries such as the energy sector. However, commercially available systems are usually designed to perform either distributed or distinct hot spot temperature measurements since they are restricted to one measurement principle. We have combined two concepts, fiber Bragg grating (FBG) temperature sensors and Raman-based distributed temperature sensing (DTS), to overcome these limitations. Using a technique called incoherent optical frequency domain reflectometry (IOFDR), it is possible to cascade several FBGs with the same Bragg wavelength in one fiber and simultaneously perform truly distributed Raman temperature measurements. In our lab we have achieved a standard deviation of 2.5 K or better at a spatial resolution in the order of 1 m with the Raman DTS. We have also carried out a field test in a high-voltage environment with strong magnetic fields where we performed simultaneous Raman and FBG temperature measurements using a single sensor fiber only.

A De-Noising Algorithm Based on EEMD in Raman-Based Distributed Temperature Sensor

A de-noising algorithm based on ensemble empirical mode decomposition (EEMD) method is employed in this paper for Raman-based distributed temperature sensor (RDTS). We first decompose the noisy signal using the EEMD and find local maxima on each intrinsic mode function (IMF) whose zero contour line is used to determine noise interval. The signal is then de-noised and reconstructed by removing the noise components of each IMF. The experimental results demonstrated that the proposed de-noising algorithm can enhance the signal-to-noise ratio by 8.8 dB while maintaining spatial resolution. The temperature error reduction of 3.2 °C can be achieved at 10 km using conventional RDTS without losing any detail.

Advanced Coding Techniques for Long-Range Raman/BOTDA Distributed Strain and Temperature Measurements

We provide an overview of the use of optical pulse coding to enhance the performances of long-range distributed optical fiber sensors for strain and temperature measurements. First, pulse coding techniques are introduced for distributed sensing, emphasizing in particular the advantages of advanced cyclic coding for fast distributed strain and simultaneous temperature measurements. Pulse coding techniques in Raman-based distributed temperature sensors employing multimode graded index fibers are introduced, also outlining their potential to achieve meter-scale long-distance sensing in standard single-mode fibers (SMF). The use of Simplex coding in Brillouin optical time-domain analysis (BOTDA) sensors is then described, showing accurate ultralong-distance sensing with meter-scale spatial resolution. Finally, the implementation of advanced cyclic coding in a hybrid Raman/BOTDA distributed sensor is presented, allowing fast long-distance strain and simultaneous temperature measurements over the same SMF, using a single narrowband laser with meter-scale spatial resolution. We show that advanced cyclic Simplex coding enables long-distance strain sensing with subsecond measurement times, also overcoming the typical temperature and strain cross-sensitivity issue affecting standard BOTDA sensors.