This paper presents the fabrication of a fiber Bragg grating (FBG)-based Fabry–Perot (FP) structure (7 mm total length) in an adiabatic fiber taper, investigates its strain and temperature characteristics, and compares the sensing characteristics with a standard polyimide coated FBG sensor. Firstly, a simulation of the said structure is presented, followed by the fabrication of an adiabatic fiber taper having the outer diameter reduced to 70 $\mathsf{\mu }$ m (core diameter to 4.7 $\mathsf{\mu }$ m). Next, the sensing structure, composed of two identical uniform FBG spaced apart by a small gap, is directly inscribed point-by-point using infrared femtosecond laser (fs-laser) micromachining. Lastly, the strain and temperature behavior for a range up to 3400 $\mathsf{\mu }\mathsf{\epsilon }$ and 225 ${}^{°}$ C, respectively, are investigated for the fabricated sensor and the FBG, and compared. The fabricated sensor attains a higher strain sensitivity (2.32 pm/ $\mathsf{\mu }\mathsf{\epsilon }$ ) than the FBG (0.73 pm/ $\mathsf{\mu }\mathsf{\epsilon }$ ), while both the sensors experience similar sensitivity to temperature (8.85 pm/ ${}^{°}$ C). The potential applications of such sensors include continuous health monitoring where precise strain detection is required.

Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many unpredictable factors. Consequently, the FBG signals have more fluctuations and are polluted by noises. Hence, the signals no longer directly follow the assumed physical laws and their proposed thinning detection mechanisms no longer work. Targeting at this, this article develops a data-driven model for FBG signal feature extraction and tube wall thickness monitoring using data analytic techniques. In particular, we develop a spatiotemporal model to describe dynamic FBG signals and extract features related to thickness. By taking physical law as guideline, we trace the relationship between the extracted features and the tube wall thickness, based on which we construct an online statistical monitoring scheme for tube wall thinning process. We use both laboratory test and field trial experiment to demonstrate the efficacy and efficiency of the proposed scheme. Note to Practitioners —This article is motivated by the real industrial needs of inner erosion detection of tubes in harsh environment. Most of the current research works focus on designing various sensing apparatuses based on fiber Bragg grating (FBG) sensors for nondestructive erosion detection. These apparatuses prove to be able to collect signals reflecting tube wall thickness in static and controllable laboratory environment qualitatively. However, in reality, the industrial environment, which is impacted by many changing factors, is dynamic and uncontrollable. Consequently, the signals collected by these FBG apparatuses would have larger variations that mask the signals related to thickness. Furthermore, current methods have neither mentioned how to process their collected data to capture the unnoticeably slow but accumulative erosion information efficiently nor constructed online monitoring algorithms to detect the tube wall thinning process based on the collected signals quantitatively. Built upon their apparatuses but targeting at their unsolved challenges, we propose a novel data-driven approach for FBG signal analysis that can remove the environmental influence and extract features only related to tube wall thickness, and using the extracted features, we construct a statistical process control scheme to monitor tube wall thickness and detect erosion in real time efficiently.

Monitoring the health of vehicle-track system using deep learning and distributed fiber optic sensing presents a significant challenge due to the vast volume of real-time data and the difficulty of directly assessing the system’s condition. This often results in a severe imbalance in the distribution of extreme samples within the dataset, as large-scale signal collection typically lacks manual labeling. Consequently, supervised deep learning models face limitations due to insufficient labeled training data, while unsupervised deep learning models struggle with contamination from ambiguous samples whose health status remains unclear, hindering the development of robust and accurate models. To address this challenge, we propose SemAnoDiffusion, a semi-supervised model based on blur diffusion and an enhanced contrastive loss training approach. SemAnoDiffusion leverages a small set of labeled data alongside a large amount of unlabeled samples to accurately differentiate between anomalous data, normal data, and ambiguous samples that fall between these categories. In a case study of a metro system in Singapore, Distributed Acoustic Sensing and accelerometer arrays were used to collect track vibration responses as trains passed, with wheel flats occurring in a small subset of the trains. SemAnoDiffusion achieved 100% accuracy in classifying manually labeled normal and anomalous samples and effectively identified semi-damaged samples with unclear damage levels from the labeled data, successfully detecting all trains with wheel flats.

In this paper, various types of non-destructive inspection techniques have been discussed. We also experimentally investigate the usage of optical sensors as a non-destructive approach towards tube boiler inspection. The experiment has been carried out onto a carbon steel U-tube with sensors being installed at critical positions of the tubes. It was observed that different thicknesses along the curved section of the U-tube could be distinguished at room temperature after processing the sensed signal which gives an idea of monitoring the thinning of the u-tubes. This technology is supposed to have an upper edge over existing NDT/NDI technologies regarding its form-factor, detection speed and accuracy, cost-effectiveness, and tube diameter requirement.