Quan Chai

Low-speed diesel engines are the primary propulsion systems for modern large vessels, and their level of intelligence is closely related to ship performance and operational safety. In conventional monitoring systems, the number of electrical sensors is constrained by the capacity of the data-acquisition hardware, which limits scalability for large-structure measurements. In contrast, fiber-optic sensing offers a clear advantage due to its strong multiplexing capability. This study presents a first attempt to apply an embedded fiber Bragg grating sensor array for real-time dynamic strain monitoring of a large low-speed marine diesel engine cylinder liner. An integrated fiber-optic sensor network was designed and fabricated for application to a 920-mm-bore cylinder liner, and engine tests were conducted under 25%~100% rated power steady operating conditions. The system achieved distributed measurement of the cylinder liner's dynamic strain response, revealed the spatial distribution of deformation, and further reconstructed the dynamic trajectory of the piston reciprocating motion.

In recent years, distributed optical fiber acoustic sensing (DAS) technology has revolutionized the monitoring of long-distance pipelines, offering full-line, real-time, and high-precision velocity measurements. Nonetheless, achieving high resolution at low flow rate remains a pivotal challenge confronting researchers in this field. This paper delves into the mechanics of vortex generation and shedding on the internal fluid surface within pipelines, uncovering the intricate relationship between flow rate and the variance characteristics of pipeline surface displacement. We propose time-shift statistical feature enhancement (TSSFE) combined with deep learning techniques. This method enables us to not only accurately identify but also predict flow rates using DAS data. Remarkably, our non-invasive detection method achieves an identification accuracy of 94.31% and a prediction accuracy of 92.3%, while maintaining an exceptional average resolution of 0.015 m/s.This breakthrough paves a novel path for enhancing the resolution of low-flow rate detection, offering significant potential for applications in various industries where precise monitoring of pipeline flow is crucial.

Regenerated fiber Bragg gratings (RFBGs) have garnered widespread attention owing to their fascinating high-temperature resistance. The flexible modulating refractive index of fiber Bragg gratings (FBGs) is conducive to boost the versatility of RFBG fabrication. Nevertheless, the impact of different refractive index modulation (RIM) amplitudes on regeneration rate remains ambiguous. Given this, an optical low-coherence reflectometry (OLCR) is employed for real-time monitoring of RIM during the regeneration process. Experimental results illustrate the regeneration rate remains consistent at different RIM amplitudes. Furthermore, a functional relationship between regeneration ratio and regeneration time is proposed. To enhance the OLCR signal intensity, a promising optimized strategy is raised by introducing a phase shift at the edges of FBG. This work deepens our understanding of the FBG regeneration process and immensely improves the manufacturing flexibility of RFBG.

The first embedded marine optoelectronic composite shaped cable with 30 m long and 120 mm diameter has been designed and manufactured. It is produced by sequentially preparing shape sensing units, shape sensing optical cables, and optoelectronic composite cables. The shape sensing units are prepared using three FBG sensors and shape memory alloy. Metal fixtures and matrix correction methods are used to reduce layout errors and improve accuracy. Shape sensing optical cables are created by embedding shape sensing units into the cable, which the long-term stability of 0.012 m. Optoelectronic composite shaped cables are produced by embedding shaped optical cables into optoelectronic composite cable and the curvature measurement accuracy of 0.05 m-1. Real-time shape measurement is achieved in both vertical and horizontal directions within a 4. 5 m range, and the shape reconstruction errors are 3.0% and 1.7%, respectively. Optoelectronic composite shaped cables can be used for shape monitoring in areas such as power transmission cables between islands and flexible pipeline cables for offshore oil and gas resource extraction, improving the safety of marine resource exploitation.

Residual stress in a part is a key factor in its fatigue life, structural stability, corrosion resistance and other properties. Therefore, accurate measurement of residual stress is essential. The mechanical release method is widely used in residual stress measurement for its high accuracy and mature technology. However, the method assumes that the material is homogeneous, and its accuracy is greatly affected in non-uniform materials. Aiming at the problem of inaccurate residual stress measurement in non-uniform materials, a fiber-optic ring array residual stress measurement method based on high spatial resolution distributed optical fiber measurement technology is proposed. The drilling method is taken as an example. In this method, we arrange the fiber-optic ring array around the hole in the same center to measure the residual stress on the circumference with high spatial resolution. The finite element simulation is used to analyze the influence of the spatial resolution of the fiber sensor, the non-uniform size, position and Young's modulus on the strain distribution and residual stress measurement. It is proved that the method can accurately measure the residual stress and obtain the information of the non-uniform state of the material.

The bonded composite laser crystals are common solid active media in the field of high-power lasers and amplifiers, used to improve thermal effect and laser output. There is a potential risk of affecting optical transmission due to the imperfect bonding interface. To characterize the interface for crystal fabrication and laser design, we propose a distributed interferometer-based interfacial loss characterizing method. Based on the stable broad-band light source and photodetector, our Optical Low-Coherence Reflectometry (OLCR) is easier to obtain the low noise background and realize the high-accuracy measurement, which is important for detecting the weak reflection of the interface. Using a fiber probe, any local area on the interface can be detected individually. With scanning the optical delay line, the surficial reflection and interfacial reflection of one light spot can be monitored separately to enable the lateral and vertical distributed measurement. Specifically, a Yb:YAG/YAG crystal was measured and demonstrated as an example. To avoid absorption and realize high spatial resolution(40μm), a source located at 1535nm with a bandwidth of 58nm is used. Meanwhile, the reflection coupling loss (RCL) of the fiber probe is self-calibrated and eliminated by the simulation and calculation model. Moving the fiber probe, the vertical and lateral interfaces are scanned. The point cloud distribution testing of vertical bonding interfaces is accomplished, and a total internal reflection method is employed to detect the lateral interface. At last, the resolution (~0.5μm) of the probe scanning is validated and discussed using a photomask.

In order to improve the response time of fiber optical temperature sensors, a fast response temperature sensor based on the Venturi structure is proposed. This sensor takes advantage of the Venturi structure to increase fluid flow velocity, thereby increasing thermal conductivity and reducing the temperature response time. Simulation analysis of the Venturi temperature sensor based on fiber Bragg grating (VFBGT) showed that it has a shorter response time compared to a bare FBG at different flow velocities. The structure of the VFBGT was optimized to achieve the minimum response time. Experimental results confirmed that the VFBGT can reduce the temperature response time of the bare FBG sensor from 420ms to 210ms at a flow velocity of 1m/s, providing a new approach for dynamic temperature measurement and real-time fast temperature compensation in high-precision systems.

The integration of artificial intelligence and distributed fiber optic acoustic sensing technology (DAS) has yielded remarkable results in recent years; however, some application scenarios face the challenge of acquiring an adequate amount of data for higher network accuracy. To address this issue, we propose a decoupling parallel convolutional neural network (DPCNN) that relies on multiple feature inputs to achieve higher accuracy while using smaller databases. Our model offers excellent recognition of five events, including background noise, footstep, digging, car passing, and climbing fence, with an accuracy rate of up to 94.9%. The DPCNN is a parallel and lightweight convolutional neural network (CNN) that boasts a short training time of only 3.76 s per epoch and a test time of 0.1175 s, with superior network convergence. In comparison to a mature single-branch CNN based on mixed images of time-frequency and time-space, the DPCNN accuracy is 6.4% higher. Our model demonstrates excellent performance across various databases and can achieve recognition accuracy of up to 98.7% with larger databases. Finally, we show the broad range of applications available for DPCNN based on multiple feature inputs when using a mature single-branch replacement in each branch of a two-branch network.