Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and heavy-load handling applications, where rope failure can result in catastrophic safety incidents and costly downtime. Despite growing research interest in data-driven condition monitoring, there is no publicly available image dataset that captures the complete degradation lifecycle of SFRs under controlled cyclic fatigue loading. To address this gap, we present a novel image dataset comprising approximately 34,700 high-resolution images of eleven Dyneema SK75/78 high-modulus polyethylene (HMPE) rope samples subjected to cyclic fatigue on a sheave-bend test stand at seven distinct axial load levels ranging from 60 kN to 280 kN. Ropes were loaded until mechanical failure, with fatigue lifetimes ranging from 695 cycles to 8,340 cycles. After every fixed number of sheave cycles (an inspection burst), ten images were captured at different cross-sectional positions along the rope, providing spatially representative sampling of surface degradation throughout the rope's entire service life. The images obtained from each load are annotated with the corresponding elapsed cycle count, enabling a direct computation of RUL for any rope in the sequence. This dataset aims to support a broad range of machine learning (ML) tasks including RUL regression, damage progression modelling, anomaly detection, and load-conditioned prognostics. The dataset is intended to serve as a benchmark resource for the development and comparison of vision-based condition monitoring (CM) and prognostics algorithms for SFRs.

The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspectors need continuous severity estimates, maintenance recommendations, anomaly flags, deterioration timelines, and automated reports, all from a single inspection image. We present DART (Damage Assessment via Rope Transformer), a vision-language foundation model that addresses the full rope inspection workflow through a unified multi-task architecture. DART extends the Joint-Embedding Predictive Architecture (JEPA) to the cross-modal domain by coupling a Vision Transformer (ViT-H/14) with Llama-3.2-3B-Instruct via a Severity-Conditioned Cross-Modal Fusion (SC-CMF) module. Three architectural innovations drive the model's versatility: (1) HD-MASK, a saliency-guided masking strategy that focuses self-supervised reconstruction on damage-dense patches; (2) per-class learnable severity gates that adaptively weight language grounding by damage category; and (3) a Contrastive Damage Disentanglement (CDD) loss that shapes the embedding space to simultaneously encode damage type, severity ordering, and cross-modal semantics. Trained once on 4,270 images spanning 14 fine-grained rope damage classes, the frozen DART backbone supports downstream tasks without any task-specific fine-tuning: damage classification (93.22

This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.

InfraRed Thermography (IRT) is an effective tool for Non-Destructive Testing and Evaluation (NDT E) of structural components consisting of fiber reinforced polymers (FRP). Active thermography is a safe and reliable technique for health monitoring of these materials during in-service applications. The present work focuses on the defect depth resolvability in a carbon fiber reinforced polymer (CFRP) sample using frequency modulated thermal wave imaging (FMTWI) technique. The performance of the FMTWI has been explored using correlation-based pulse compression approach and compared with the conventional data processing approaches. Results shows the high sensitivity and resolution to resolve deeper defects of varying depths and diameters in CFRP sample using correlation approach.

InfraRed Thermography (IRT) is a widely used Non-Destructive Testing (NDT) method for inspection of various structures and components in the fields of renewable energy, aerospace, automobiles industry, etc. The paper presents Barker Coded Thermal Wave Imaging (BCTWI) technique based on pulse compression approach to obtain enhanced probing depth and resolution for testing of Carbon Fiber Reinforced Polymers (CFRP) sample. The time domain and frequency domain analysis has been carried out for quantitative assessment of the defects in the CFRP sample. Results shows that BCTWI with time domain approach significantly improves the defect depth resolution in comparison to the conventional frequency domain analysis approach.

In this paper, we present two years of high-resolution nitrous oxide (N_2O) measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The dataset comprises frequent, real-time measurements from a full-scale WWTP, with a sample interval of 2 minutes, making it ideal for developing models for real-time operation and control. This comprehensive bio-chemical dataset includes detailed influent and effluent parameters, operational conditions, and environmental factors. Unlike existing datasets, it addresses the unique challenges of modeling N_2O, a potent greenhouse gas, providing a valuable resource for researchers to enhance predictive accuracy and control strategies in wastewater treatment processes. Additionally, this dataset significantly contributes to the fields of machine learning and deep learning time series forecasting by serving as a benchmark that mirrors the complexities of real-world processes, thus facilitating advancements in these domains. We provide a detailed description of the dataset along with a statistical analysis to highlight its characteristics, such as nonstationarity, nonnormality, seasonality, heteroscedasticity, structural breaks, asymmetric distributions, and intermittency, which are common in many real-world time series datasets and pose challenges for forecasting models.

Frequency modulated thermal wave imaging (FMTWI) is an efficient and affective thermal nondestructive testing and evaluation (NDT E) technique for qualitative and quantitative analysis for defects in test materials. FMTWI utilizes low peak power heat sources modulated within a frequency sweep to excite the material under test. The paper demonstrates a novel analytical approach for heat diffusion in isotropic material using FMTWI technique to distinguish defects located at different depths inside the test sample. The frequency modulated thermal excitation has been illuminated over carbon fibre reinforced polymer (CFRP) material to compute thermal response over the object under test. The mapped temperature response is analysed further for defect detectability in terms of correlation coefficient and time delay. Further, the presented analytical approach for defects located at different depths are comparted with the simulation model of CFRP test material. Lastly, different frequency and time domain data analysis schemes have been applied to detect defects in terms of thermal contrast. Results demonstrates the detection capability of FMTWI technique using correlation based matched filter method to provide better test resolution and sensitivity.

Infrared thermography (IRT) has been extensively used in the field of non-destructive testing and evaluation (NDT&E) as a condition monitoring tool in commercial as well as industrial applications. It is a fast, non-contact, whole field and quantitative technique for NDT&E applications by mapping the thermal profile over the test sample. This paper presents a novel three-dimensional analytical model for characterization of glass fibre reinforced polymer (GFRP) sample having flat bottom hole defects. The capability of the proposed method is highlighted with recently introduced pulse compression favorable thermal wave imaging modalities . Further, the analytical results have been validated with simulated and experimental studies on the proposed frequency modulated thermal wave imaging (FMTWI), Barker coded thermal wave imaging (BCTWI) and digitized frequency modulated thermal wave imaging (DFMTWI) techniques to analyze the performance of the defect detectability by taking normalized correlation coefficient as a figure of merit. Obtained results clearly shows the capabilities of the DFMTWI over the FMTWI and BCTWI using correlation based matched filter post-processing approach.

Frequency-modulated thermal wave imaging (FMTWI) is an emerging thermal non-destructive testing and evaluation (TNDT&E) technique widely used for damage monitoring of various structural materials. The present paper discusses the theoretical aspects of a three-dimensional simulated mild steel model for FMTWI to predict the detection of air defects. In this paper, a comparative evaluation of the results has been carried out by the conventional frequency-domain-based post-processing scheme and is compared with the recently proposed correlation-based time-domain data processing approach. Results show the merits of the time-domain-based post-processing scheme over the frequency-domain-based analysis schemes for improved defect detection capability.

Defect detectability of structures and components is one of the significant parameters for proper functioning of industries. Active thermography is a safe and reliable technique for non-destructive testing and evaluation (NDT&E) of these materials during in-service applications. This paper investigates the defect resolvability in a carbon fiber reinforced polymer (CFRP) sample using frequency modulated thermal wave imaging (FMTWI) technique. FMTWI performance has been examined using correlation-based pulse compression (PC) approach and compared with the conventional data processing approaches. Results shows the high sensitivity and resolution to resolve deeper defects of varying depths and diameters in CFRP sample using correlation approach.

The estimation of defect depth is a crucial parameter in the field of non-destructive testing. The paper presents the simulation aspects for three-dimensional heat transfer equation for a finite thickness steel sample having six blind holes as defects located at different locations using a promising Barker-coded thermal wave imaging technique (BCTWI). The surface of the test sample is subjected to Barker-coded modulated thermal excitation, and the analysis has been carried out using the time domain phase and correlation analysis post-processing approach on the captured data during the active heating. Finally, various adopted processing approaches have been compared to evaluate defect detection capabilities.

In recent years, non-invasive imaging methodologies have been demonstrated as reliable, quantitative and remote inspection methods for the characterization of biological samples. The present work incorporates frequency modulated thermal wave imaging (FMTWI) followed by matched filtering-based post-processing analysis for bone diagnostics, especially bone with tissue, skin and muscle over layers. In order to find the characterization capabilities of the proposed method to detect the bone density variations, a multilayer skin-fat-muscle-bone structure is considered. The results obtained from the proposed scheme clearly show improved evaluation capabilities in terms of the test resolution and sensitivity.

The active thermal non-destructive testing and evaluation technique plays a vital role in health monitoring of various solid materials. Present manuscript demonstrates the applicability of pulse compression favorable Digitized version of linear Frequency Modulated Thermal Wave Imaging (DFMTWI) approach to identify flaws having different geometrical shapes in a Glass Fibre Reinforced Polymer (GFRP) sample. A novel Thermal Image Correlation (TIC) data-processing approach is proposed to obtain the isothermal patterns from the reconstructed pulse compressed data through matched filter scheme to identify sub-surface anomalies. The detection capabilities of the presented approach are compared on various adopted data processing approaches.

The active thermal non-destructive testing and evaluation technique plays a vital role in health monitoring of various solid materials. Present manuscript demonstrates the applicability of pulse compression favorable Digitized version of linear Frequency Modulated Thermal Wave Imaging (DFMTWI) approach to identify flaws having different geometrical shapes in a Glass Fibre Reinforced Polymer (GFRP) sample. A novel Thermal Image Correlation (TIC) data-processing approach is proposed to obtain the isothermal patterns from the reconstructed pulse compressed data through matched filter scheme to identify sub-surface anomalies. The detection capabilities of the presented approach are compared on various adopted data processing approaches.

Frequency modulated thermal wave imaging (FMTWI) has been successfully applied in the field of infrared thermography to estimate defects in various structural materials. The paper presents theoretical aspects of three-dimensional mild steel sample for FMTWI to detect flat bottom holes as defects. Furthermore, performance of FMTWI for analytical and simulated approach has been discussed and comparison has been performed based on time domain matched filtering pulse compression approach. The results demonstrate enhanced depth resolvability of the time domain-based pulse compression approach for FMTWI scheme.

Infrared non-destructive testing and evaluation is one of the promising inspection methods for characterization of wide verity of materials due to its merits and applicability to test materials irrespective of their electrical, mechanical, acoustical and magnetic properties. Among the various infrared non-destructive evaluation modalities such as pulse based thermography and mono frequency excited modulated lock-in thermography, recently proposed matched filter based non-periodic infrared thermographic approaches gained their importance due to their superior sub-surface defect detection in terms of resolution and sensitivity. The present work demonstrates the merits of frequency modulated thermal wave imaging for identification of concrete in concrete structure. Obtained results shows the matched filter based post-processing schemes exhibits better depth scanning capabilities compared with the conventional frequency domain phase approach to detect corrosion in the concrete structures.

Infrared non-destructive testing and evaluation is one of the most promising inspection methods for evaluating variety of materials due to its merits such as remote based, whole field, safe and quantitative inspection capabilities. Among the various infrared non-destructive evaluation methods, pulse based thermography and mono frequency excited modulated lock-in thermography gained importance due to their simple experimentation procedure and data processing approaches involved. However, recently proposed matched filter based non-periodic infrared thermographic approaches gained importance due to their superior sub-surface defect identification capabilities in terms of detection resolution and sensitivity. The present work demonstrates the merits of pulse compression favorable thermal wave imaging approach for identification of flat bottom holes in a carbon fiber reinforced polymer material.

InfraRed Thermography (IRT) is a safe, remote, whole field and quantitative Non-Destructive Testing and Evaluation (NDT&E) technique to obtain subsurface information. In this paper, depth resolvability using matched filtering approach has been explored for Frequency Modulated Thermal Wave Imaging (FMTWI) technique. Further, the spectral reshaping has been performed using Gaussian windowing function to study and compare the variations obtained in the thermal response. The present paper explores proposed techniques on a mild steel sample having six Flat Bottom Hole (FBH) defects using finite element modeling and analysis technique. Results shows high defect detection performance of frequency modulated approach by considering correlation coefficient and time delay as a figure of merit.

InfraRed Thermography (IRT) is one of the non-destructive testing and evaluation (NDT&E) approach widely used for testing and evaluation of wide verity of materials such as metals, semiconductors and composites. Among the widely used Thermal NDT&E (TNDT&E) approaches for better depth resolution and sensitivity for detection of defects located at different depths inside the test specimen recently proposed correlation based approach gained importance due to its enhanced defect detection capabilities. The present paper introduces a novel one-dimensional analytical solution for the frequency modulated excitation scheme under adiabatic boundary conditions for detection of flat bottom holes as defects in a mild steel sample. The performance of the Pulse Thermography (PT), Lock-in Thermography (LT) and Frequency Modulated Thermal Wave Imaging (FMTWI) methods are highlighted their defect detection capabilities have been compared by adopting the recently introduced correlation based post-processing approach. Finally, the proposed analytical method has been validated with the results obtained from the commercially available finite element based software.