Non-intrusive Technique Research Articles

Challenges on prognostics and health management for wind turbine components

This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

Advancements in Drowsiness Detection: A Review

This review study looks at current advancements in sleepiness detection technologies to address the urgent problem of driving while sleepy leading to accidents. In order to prevent future accidents, it analyzes several approaches and technology and emphasizes how crucial it is to correctly recognize drowsy conditions in drivers. Important results highlight the effectiveness of methods for real-time drowsiness detection such as convolutional neural networks (CNNs), eye aspect ratio (EAR) analysis, and deep learning integration. Additionally, improvements in computer vision algorithms make it possible to precisely analyze facial landmarks and eye blinking patterns, which improves the accuracy of detection systems. In order to protect driver comfort and system reliability, the evaluation emphasizes the importance of non-intrusive fatigue detection techniques while also emphasizing the necessity to reduce false warnings. Remarkably, certain models detect drowsy-eye with an accuracy of above 95%. All things considered, the review emphasizes how important cutting-edge technology is in reducing the risks related to drunk driving, especially deep learning and computer vision algorithms. It promotes the deployment of reliable sleepiness detection technologies in order to increase road safety and reduce accidents brought on by fatigued drivers. Key Words: Drowsiness detection, Eye blink duration, Facial landmark detection, Deep learning algorithms, Computer vision, Eye aspect ratio, Road safety.

Non-intrusive temperature measurements for transient freezing in laminar internal flow using laser induced fluorescence

This work presents two color LIF temperature measurements for the transient freezing in a square channel under laminar flow conditions. This is the first time non-intrusive temperature measurements were performed within the thermal boundary layer during the transient growth of an ice layer in internal flow. A combination of a local outlier factor algorithm and a smoothing operation was used to remove the top to bottom striations and reduce the other measurement noise. The temperature uncertainty in our measurements was between σ=0.3∘C and σ=0.5∘C. For the largest temperature difference between the bulk and the melting point of 14.6 °C, good results were obtained. As such, the current campaign demonstrates the potential of LIF as a non-intrusive temperature measurement technique for solid–liquid phase change experiments. However, some artefacts were present within the vicinity of the ice-layer due to the scattering of the laser light, especially near the inlet of the channel where the ice-layer is curved instead of flat. LIF measurements taken within a short time span prior to the onset of ice freezing showed approximately 2 °C of subcooling, consistent with previous findings. In addition, an anomalous behavior within the thermal boundary layer was observed, with a much smaller temperature gradient within the first few mm above the cold plate and a point of inflection in the temperature profile. The anomalous temperature behavior is possibly attributed to enhanced natural convection as a result of the subcooling at the cold plate surface.

A non-invasive, low-cost and easy-to-operate underwater terrain monitoring method

A fast, reliable, through water and non-intrusive bed-profile measurement technique that is economical and easy to use in a wide range of laboratory experiments involving sediment transport is proposed. A consumer-grade 3-D range sensing technology which by its low cost, easy operation and relatively good accuracy is becoming an attractive alternative to expensive 3-D laser scanners. The Microsoft XBOX 360 kinect sensor 2.0 is a low-cost and highly popular depth camera, which can be used to scan 3D surface terrain of dynamic objects and has the potential to scan terrain below and above water. Through the software matching of kinect 2.0, a data storage and analysis system are formed, which makes it has the ability to obtain underwater terrain data. In addition, the correction model of water refraction error is established, which can eliminate the measurement error caused by water refraction. Up to four different water conditions, kinect 2.0 is tested. Finally, the influences of water depth, suspend load volume concentration and moving flow velocity on the measurement accuracy of kinect 2.0 are explored, and the compensation methods of measurement error caused by the above factors are given. The results of this paper can provide reference for underwater topographic survey.

Velocity measurements in particle-laden high-enthalpy flow using non-intrusive techniques

This research aims at analysing the particle-laden flow of the hypersonic high-enthalpy wind tunnel L2K, situated in Köln at the German Aerospace Center (DLR). In the L2K wind tunnel, Martian atmosphere can be created, and the facility can simulate heat load conditions encountered during atmospheric entry of Martian missions. In the tests, a simplified Martian atmosphere (97% CO2 and 3% N2) was used. The high-enthalpy flow was loaded with micrometric particles of magnesium oxide. The particles’ mean velocity was measured with a 2D–2C particle image velocimetry (PIV) system, in the region right downstream the nozzle expansion of the wind tunnel. The work proves the possibility of creating a high-enthalpy particle-laden flow for thermal protection systems (TPS) testing with simulated Martian atmosphere. Average particle velocities of around 2000 m/s are measured and compared with the numerical simulation of the wind tunnel’s particle-free flow, and with the flow velocity measured with tunable diode laser absorption spectroscopy (TDLAS). The study also highlights some unexpected results and features of the high-enthalpy particle-laden flow and proposes some theories for the causes of such effects, which include agglomeration due to melting, and gravitational effect.

Non-Intrusive Online Time Domain Reflectometry Technique for Power Cables

Non-Intrusive Online Time Domain Reflectometry Technique for Power Cables

A novel non-intrusive mental workload evaluation concept in human-robot collaboration

The integration of Human-Robot Collaboration (HRC) in industrial robotics introduces challenges, particularly in adapting manufacturing environments to work seamlessly with collaborative robots. A key objective in HRC system optimization is enhancing human acceptance of these robots and improving productivity. Traditionally, the assessment of human mental workload in these settings relies on methods like EEG, fNIRS, and heart rate monitoring, which require direct physical contact and can be impractical in manufacturing environments. To address these issues, we propose an innovative and non-intrusive method that employs cameras to measure mental workload. This technique involves capturing video footage of human operators on the shop floor, focusing specifically on facial expressions. Advanced AI algorithms analyse these videos to predict heart rate ranges, which are then used to estimate mental workload levels in real time. This approach not only circumvents the need for direct contact with measurement devices but also enhances privacy and data security through privacy computing measures. Our proposed method was tested in an HRC experiment to provide preliminary validation. This pioneering use of non-intrusive AI-based vision techniques for real-time mental workload assessment represents a significant advancement in managing human factors in industrial HRC settings.

Infrared measurements of fluid temperature in a polymeric Pulsating Heat Pipe

Pulsating heat pipes are two-phase passive heat transfer devices partially filled with a working fluid in saturation conditions. During operation, supplying heat to one end of the system (named evaporator) results in a local increase in temperature and pressure, which drives the fluid through a transport section (named adiabatic section) towards the cooled, opposite end (named condenser) for effective heat dissipation. The local thermo-fluid dynamic state of the working fluid is sometimes assessed by means of non-intrusive techniques, such as infrared thermography. In this case, the radiative properties of the systems in the infrared spectrum must be known a priori. Nevertheless, since pulsating heat pipes may be manufactured with different materials, wall thicknesses and channel geometries, the radiative properties of the walls and the confined flow are not always known or assessable by means of the available literature. Hence, the work proposes to design a straightforward calibration procedure for quantitative infrared fluid temperature measurements in a polymeric pulsating heat pipe charged with FC-72 and having unknown radiative properties. The emissivity and transmissivity of the walls and confined fluid are estimated with good accuracy. The results will allow repeatable and reliable fluid temperature measurements in future experimentations on the mentioned device.

From Raw Data to Practical Application: EEG Parameters for Human Performance Studies in Air Traffic Control

The use of electroencephalography (EEG) techniques has many advantages in the study of human performance in air traffic control (ATC). At present, these are non-intrusive techniques that allow large volumes of data to be recorded on a continuous basis using wireless equipment. To achieve the most with these techniques, it is essential to establish appropriate EEG parameters with a clear understanding of the process followed to obtain them and their practical application. This study explains, step by step, the approach adopted to obtain six EEG parameters: excitement, stress, boredom, relaxation, engagement, and attention. It then explains all the steps involved in analysing the relationship between these parameters and two other parameters that characterise the state of the air traffic control sector during the development of real-time simulations (RTS): taskload and number of simultaneous aircraft. For this case study, the results showed the highest relationships for the engagement and attention parameters. In general, the results confirmed the potential of using these EEG parameters.

Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities (RECs) involves maximizing the self-consumption within a community, particularly in regulatory contexts in which shared energy is incentivized. In many countries, the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users (e.g., residential and commercial users) makes the design of a new energy community a challenging task. This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data (i.e., billed energy), with the aim of estimating the energy shared by RECs. The proposed methodology involves three phases: first, identifying the typical load patterns of residential users through k-Means clustering, then implementing a Random Forest algorithm, based on monthly energy bills, to identify typical load patterns and, finally, reconstructing the hourly electrical load profile through a data-driven rescaling procedure. The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant. The Normalized Mean Absolute Error (NMAE) and the Normalized Root Mean Squared Error (NRMSE) were evaluated over an entire year and whenever the energy was shared within the REC. The Relative Absolute Error was also measured when estimating the shared energy at both a monthly (MRAE) and at an annual basis. (RAE). A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04 %, an NRMSE of 26.17 %, and errors of 18.34 % and 23.87 % during shared energy timeframes, respectively. Furthermore, our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31 % and 0.12 %, respectively.

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Practical Challenges in Nanofluid Convective Heat Transfer Inside Silicon Microchannels

Despite numerous studies on nanofluids in microchannel heat sinks (MCHSs), they are not yet commercialized due to long-term stability issues and high maintenance costs. Therefore, this study explores the impact of nanofluids and nanoparticle clustering on single-phase convective heat transfer inside microchannels under laminar conditions. Water and commercially available water-based nanosuspensions, including Al2O3-water (30–60 nm), TiO2-water (5–30 nm), and polystyrene-water (50 nm), are circulated through silicon MCHS having rectangular channels integrated into a closed flow loop. To assess the in situ and real-time nanoparticle clustering during heat transfer experiments, Light Extinction Spectroscopy (LES) is applied as a non-intrusive measurement technique on nanofluids without any fluid sampling. Our findings reveal the appearance of nanofluid discoloration with no measurable increase in heat transfer coefficient. This unexpected change is attributed to the interplay of abrasion, erosion, and corrosion phenomena, likely triggered by the clustering of nanoparticles within the silicon microchannels—a novel insight into the complex dynamics of nanofluid behavior (an increase in the De Brouckere mean diameter from 11 nm to 107.3 nm over a 2.5 h period for TiO2 nanoparticles). The resulting material loss could not be mitigated by altering the nanoparticle material, which may impede heat transfer enhancement under tested conditions.

District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images

This paper describes a dataset collected by infrared thermography, a non-contact, non-intrusive technique to acquire data and analyze the built environment in various aspects. While most studies focus on the city and building scales, an observatory installed on a rooftop provides high temporal and spatial resolution observations with dynamic interactions on the district scale. The rooftop infrared thermography observatory with a multi-modal platform capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore. It was placed on the top of two buildings that overlook the outdoor context of the National University of Singapore campus. The platform collects remote sensing data from tropical areas on a temporal scale, allowing users to determine the temperature trend of individual features such as buildings, roads, and vegetation. The dataset includes 1,365,921 thermal images collected on average at approximately 10-second intervals from two locations during ten months.

Effect of dopants addition on the displacing phase properties in a polymer flooding process using computed tomography scanning

This study evaluated the impact of the four most used dopants in core-flooding tests, supported by computed tomography, on the behavior of polymer viscosity. Initially, the minimum concentration of each dopant required to achieve an adequate contrast between phases was estimated by varying the amount of dopant in solution and comparing the CT number of polymer solution and oil to guarantee a differential highest than 300 CT. Afterward, the effect of adding the different dopants on the polymer solution viscosity was determined through Brookfield viscosimeter. Subsequently, the polymer concentration to counteract the viscosity reduction was quantified by adding different polymer amounts. The results showed that sodium iodide achieved the highest attenuating effect between phases with the lowest dopant concentration. Moreover, this dopant caused the lowest polymer viscosity diminution and required the smallest increase in polymer concentration to reach the base case viscosity, making this dopant the most suitable agent to be used in polymer injection experiments at the conditions evaluated. Finally, the impact of crude oil density on the lowest amount of dopant required to achieve sufficient differentiation between phases was analyzed, and as a result, a correlation was found that could be used in future experiments at similar conditions to evaluate polymer flooding in a porous medium by means computed tomography scan as a visual and non-intrusive technique, reaching the best contrast between crude oil and HPAM.

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems

Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning techniques developed especially for time-series forecasts, such as LSTM and TCN, or for spatial-feature extraction such as CNN, are employed to model the system dynamics for advection-dominated problems. This paper proposes a Convolutional Autoencoder(CAE) model for compression and a CNN future-step predictor for forecasting. These models take as input a sequence of high-fidelity vector solutions for consecutive time steps obtained from the PDEs and forecast the solutions for the subsequent time steps using auto-regression; thereby reducing the computation time and power needed to obtain such high-fidelity solutions. Non-intrusive reduced-order modeling techniques such as deep auto-encoder networks are utilized to compress the high-fidelity snapshots before feeding them as input to the forecasting models in order to reduce the complexity and the required computations in the online and offline stages. The models are tested on numerical benchmarks (1D Burgers’ equation and Stoker’s dam-break problem) to assess the long-term prediction accuracy, even outside the training domain (i.e. extrapolation). The most accurate model is then used to model a hypothetical dam break in a river with complex 2D bathymetry. The proposed CNN future-step predictor revealed much more accurate forecasting than LSTM and TCN in the considered spatiotemporal problems.

Impact of the Undersaturation Level on the Longevity of Superhydrophobic Surfaces in Stationary Liquids.

Although the longevity of superhydrophobic surface (SHS) induced by diffusive gas transfer has been extensively studied, the scaling relation between SHS longevity and undersaturation level of the liquid is still an open question. In this study, we address this question by performing experiments where the plastron decay is visualized by a nonintrusive optical technique based on light reflection, the gas diffusion is introduced by using liquid with low dissolved gas concentrations, and the SHS longevity is measured based on the status of gas on the entire surface. We find that the SHS longevity (tf) follows a scaling relation: tf ∼ (1 - s)-2, where s is the ratio of the gas concentration in liquid to that in the plastron. This scaling relation implies that as the gas is dissolving into the liquid, mass flux J reduces with time as J ∼ t-0.5. Furthermore, we find that the diffusion length LD reduces as the undersaturation level increases, following the scaling relation of LD ∼ (1 - s)-1. Lastly, we show that an SHS with a greater texture depth has a longer longevity and a larger LD. Our results provide a better understanding of SHS longevity in undersaturated liquid.

Mach Number and Heating Effects on Turbulent Supersonic Jets

Modern experimental techniques were used to obtain detailed aerodynamic and thermodynamic data for turbulent supersonic jets. Operating points from the work of Seiner et al. for heated Mach 2 jets were used as a starting point for the matrix of cases examined here using a geometrically similar Mach 2 nozzle. Two additional nozzles were examined having design Mach numbers of 1.36 and 1.63. For each Mach number, multiple jet temperatures were investigated. Jet measurements were first made with stagnation pressure and temperature probes for comparison to the previous experiments. The experiments focused on modern nonintrusive measurement techniques, including particle image velocimetry for mean and root-mean-square (rms) velocities, and rotationally resolved Raman scattering spectroscopy for mean and rms temperatures. Experimental results showed that as jet Mach number increases, the overall jet mixing rate decreases, as indicated by the potential core length, downstream mean velocities, and turbulence levels. Similar behavior was observed in the mean temperatures and rms temperatures. The suppression of turbulence as the jet Mach number increases is due to compressibility. For a fixed jet Mach number, increasing the jet temperature resulted in faster mixing by all measures. Peak turbulent stress levels, located along the lipline of the jet, demonstrated trends in individual stress components that differed depending on whether jet Mach number or temperature was varied, but indicated that the turbulence structure parameter was very similar when varying either jet temperature or Mach number.

Intrusive effects of dual-tip conductivity probes on bubble measurements in a wide velocity range

High-velocity air-water flows in hydraulic structures, such as spillways and low-level outlets, are characterised by high turbulence levels and strong self-aeration. In air-water flows, the void fraction varies from values close to zero at the invert up to near unity in the upper spray region. Because of the strong aeration, non-intrusive optical measurement techniques can only be applied to a limited extent, and intrusive phase-detection probes are widely used to estimate air-water flow properties, including void fraction, interfacial velocity, and chord sizes. However, these probes are often used without independent validation. Herein, this study systematically investigated the uncertainties of phase-detection probe measurements of individual bubbles traveling at a wide range of velocities (up to 7.5 m/s) in a vertically upward bubbly pipe flow. Velocities and chord times of air bubbles were simultaneously measured with an intrusive dual-tip conductivity probe and stereo-view high-speed videos, and a comparative analysis identified various effects of bubble-probe interactions on the measurement accuracy of the bubble velocities. The bubble deceleration due to these bubble-probe interactions were quantified. For standard interactions (i.e., bubbles undergoing no significant deformation and slowing down), the velocity estimates compared well with a theoretical model (errors less than 2 %), while larger errors were found for non-standard interactions such as those characterised by crawling, drifting, and waking mechanisms. In addition, a novel correction scheme for chord times was proposed, improving the accuracy of chord length, bubble size and void fraction estimations in air-water flows.

Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for.

Surrogate modeling in irreversible electroporation towards real-time treatment planning

In this paper, we develop surrogate models that can replace expensive predictive models and account for uncertainties in real-time treatment planning for irreversible electroporation of liver tumors. Standard non-intrusive surrogate modeling techniques that account for the model uncertainty and reduce the computational cost, such as polynomial chaos expansion and Gaussian process regression with conventional kernels, often do not capture the true physical behavior of the treatment outcome as required in the context of treatment planning. We improve the Gaussian process regression model by modifying the kernel function to a non-stationary Gibbs kernel with a support vector machine-based classifier in its length scale definition. This proposed model is compared with the standard surrogates in terms of their performance and accuracy. Our model is able to accurately replicate the behavior of the biophysics-based predictive model. There is a decrease of at least 81% in the overall root-mean-square error for treatment outcome when compared to the Gaussian process regression model with conventional kernels. Furthermore, we illustrate the application of the proposed surrogate model in treatment planning to address a voltage optimization problem for complete tumor ablation. Surrogate-assisted treatment planning exhibited good performance while maintaining similar levels of accuracy in comparison to treatment planning based on biophysical models. Finally, the effect of uncertainty in tissue electrical conductivities on the optimal voltage value is discussed.