Ice Detection Research Articles

Recent Developments on Dielectric Barrier Discharge (DBD) Plasma Actuators for Icing Mitigation

Ice accretion is a common issue on aircraft flying in cold climate conditions. The ice accumulation on aircraft surfaces disturbs the adjacent airflow field, increases the drag, and significantly reduces the aircraft’s aerodynamic performance. It also increases the weight of the aircraft and causes the failure of critical components in some situations, leading to premature aerodynamic stall and loss of control and lift. With this in mind, several authors have begun to study the thermal effects of plasma actuators for icing control and mitigation, considering both aeronautical and wind energy applications. Although this is a recent topic, several studies have already been performed, and it is clear this topic has attracted the attention of several research groups. Considering the importance and potential of using dielectric barrier discharge (DBD) plasma actuators for ice mitigation, we aim to present in this paper the first review on this topic, summarizing all the information reported in the literature about three major subtopics: thermal effects induced by DBD plasma actuators, plasma actuators’ ability in deicing and ice formation prevention, and ice detection capability of DBD plasma actuators. An overview of the characteristics of these devices is performed and conclusions are drawn regarding recent developments in the application of plasma actuators for icing mitigation purposes.

Sea ice image classification based on ResFPG network and heterogeneous data fusion

ABSTRACT Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%).

A Class-Imbalanced Heterogeneous Federated Learning Model for Detecting Icing on Wind Turbine Blades

Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection, but they rely on a considerable amount of data. Data exchange between multiple wind farms would improve the performance of detection models, due to the spatio-temporal dependencies capable of reflecting different meteorological conditions. The traditional centralized approach for icing detection faces many challenges, including the requirement of high storage and computational capacity of the server, vulnerability to cyberattacks, and operators’ reluctance of sharing data for commercial reasons. To address these challenges, this article proposes a heterogeneous federated learning (FL) model for wind turbine blade icing detection. The structures of the server and client models in the presented method are different, in contrast to the traditional FL of sharing the same structure. In addition, this article addresses the class imbalance problem in the training data. Last, this article conducts comprehensive experiments to evaluate the proposed method using real-world data from 20 turbines in two wind farms, and compares it with two state-of-the-art FL models and five well-known class imbalance methods. The experimental results verify the effectiveness and superiority of the proposed method.

Icing Detection Method of Transmission Line Based on Machine Vision

The conventional icing detection method is mainly based on the manual detection of icing status. The texture correlation characteristics of icing are variables, which lead to large errors in the icing edge detection and affect the subsequent maintenance of transmission lines. Therefore, the transmission line icing detection method based on machine vision is designed in this paper. The method comprises the following steps of extracting the icing texture feature of the transmission line, namely extracting the correlation feature of an icing image, identifying an icing area by using machine vision technology, carrying out edge detection on the transmission line and finally obtaining the icing thickness of the transmission line to realize the accurate detection on the icing of the power transmission line. The comparative experimental results show that the detection error of this method is small, the detection accuracy is high, and it can be applied to real life.

A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

Wind energy is a fast-growing renewable energy but faces blade icing. Data-driven methods provide talented solutions for blade icing detection, but a considerable amount of Internet of Things data needs to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BLADE</i> , a Blockchain-empowered imbalanced federated learning (FL) model for blade icing detection. With the help of the Blockchain, the conventional FL is improved without worrying about the failure of the single centralized server and boosts the privacy preserving. A validation mechanism is introduced into the Blockchain to enhance the defense against poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the class imbalance problem in the sensor data. BLADE is evaluated on ten wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of the proposed BLADE.

Dual-functional communication and sensing antenna system

In this article, a dual functional antenna system for communication and sensing applications is designed on a single substrate having a common input port. The main motivation behind the proposed design is to build an antenna system, which can work as an antenna sensor and also can communicate over a fixed band not related to the resonance frequency shifts of the antenna sensor. The dual functionality of the system has been achieved by designing a frequency selective multipath filter (FSMF). The FSMF has an input port and two output ports. Both the communicating and sensing antennas are integrated on the output ports of the FSMF. The FSMF ensures minimum effect of the antennas on each other’s performance. The performance of the antenna sensor is first shown by characterizing different standard substrates with {varepsilon} _r=2.2 to 6.15 and then demonstrated for ice and water detection. The communicating antenna is used for Wi-Fi (2.45 GHz) applications. The simulated and measured results of the dual-functional antenna systems are in good agreement. The proposed design is fabricated on a PCB with {varepsilon} _r=3.5 and tan delta =0.0027 with an overall size of 86.7times 26times 0.8,text {mm}^3.

DLR related model development and performance analysis in the framework of FLEXITRANSTORE

This paper demonstrates a practical implementation of a dynamic line rating (DLR) system and an effective ice prediction and prevention method using line monitoring sensors within the FLEXITRANSTORE international project framework. The presented anti-icing system operated continuously for two years. As a part of the project, 4 line monitoring sensors and 2 weather stations were installed on that section of the power line where icing is most likely to occur. These sensors worked continuously and reliably during the demonstration period. The sensor measurements were supplemented with the load value from the SCADA system and weather forecasts, with the help of which, in addition to the real-time line rating and sag-clearance analysis, a predetermined line rating could also be determined. The investigated system also included an ice prediction subsystem as well as a sag-calculation-based ice detection subsystem. Based on the comparison of the measured and calculated parameters, it can be said that both the operation of the sensors and the quality of the data provided by them were satisfactory. In the case of conductor temperature calculation, the developed mathematical models, which can be applied independently, provided information to the system operator with almost similar accuracy than sensors’ measurements. During the observed period, the ice prediction and detection subsystem were also tested and functioned correctly. As an overview of the project, it can be concluded that an efficient DLR-sensor-based anti-icing system is possible to be realized on the high voltage distribution power grid.

Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbines

Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ice, thus ensuring turbine safety. This article presents a wavelet-driven multiscale graph convolutional network (MWGCN), which is a supervised deep learning model for blade icing detection. The proposed model first uses wavelet decomposition to capture multivariate information in the time and frequency domains, and then employs a temporal graph convolutional network (GCN) to model the intervariable correlations of the decomposed multiscale wavelets and their temporal dynamics. In addition, this article introduces scale attention to the MWGCN for a further improvement of the model and proposes the method to address the class imbalance problem of the training data sets. Finally, the article conducts comprehensive experiments to evaluate the proposed model, and the results demonstrate the effectiveness of the model in blade icing detection and its better performance over eight state-of-the-art algorithms, with 17.2% and 11.3% higher F1 scores over the best state-of-the-art baseline on the labeled datasets.

Broadband spectral induced polarization for the detection of Permafrost and an approach to ice content estimation – a case study from Yakutia, Russia

Abstract. The reliable detection of subsurface ice using non-destructive geophysical methods is an important objective in permafrost research. The ice content of the frozen ground is an essential parameter for further interpretation, for example in terms of risk analysis and for the description of permafrost carbon feedback by thawing processes. The high-frequency induced polarization method (HFIP) enables the measurement of the frequency-dependent electrical conductivity and permittivity of the subsurface, in a frequency range between 100 Hz and 100 kHz. As the electrical permittivity of ice exhibits a strong characteristic behaviour in this frequency range, HFIP in principle is suitable to estimate ice content. Here, we present methodological advancements of the HFIP method and suggest an explicit procedure for ice content estimation. A new measuring device, the Chameleon-II (Radic Research), was used for the first time. Compared to a previous generation, the new system is equipped with longer cables and higher power, such that we can now achieve larger penetration depths up to 10 m. Moreover, it is equipped with technology to reduce electromagnetic coupling effects which can distort the desired subsurface signal. The second development is a method to estimate ice content quantitatively from five Cole–Cole parameters obtained from spectral two-dimensional inversion results. The method is based on a description of the subsurface as a mixture of two components (matrix and ice) and uses a previously suggested relationship between frequency-dependent electrical permittivity and ice content. In this model, the ice relaxation is considered the dominant process in the frequency range around 10 kHz. Measurements on a permafrost site near Yakutsk, Russia, were carried out to test the entire procedure under real conditions at the field scale. We demonstrate that the spectral signal of ice can clearly be identified even in the raw data and show that the spectral 2-D inversion algorithm is suitable to obtain the multidimensional distribution of electrical parameters. The parameter distribution and the estimated ice content agree reasonably well with previous knowledge of the field site from borehole and geophysical investigations. We conclude that the method is able to provide quantitative ice content estimates and that relationships that have been tested in the laboratory may be applied at the field scale.

Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves

Monitoring the status of Arctic marine ecosystems is aided by multi-sensor oceanographic moorings that autonomously collect data year-round. In the northeast Chukchi Sea, an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) collected data from the upper 30 m of the water column every 10–20 s from 2014 to 2020. We here describe the processing of the AZFP’s 455 kHz acoustic backscatter return signal for the purpose of developing methods to assist in characterizing local sea ice conditions. By applying a self-organizing map (SOM) machine learning algorithm to 15-min ensembles of these data, we are able to accurately differentiate between the presence of open water and sea ice, and thereby characterize statistical properties surface wave height envelopes and ice draft. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables efficient and rich characterizations of environmental conditions, such as frequency of sparse ice floes in otherwise open water and brief open-water leads amidst the ice pack. Corrections for instrument tilt, speed of sound, and water level allow us to resolve the sea surface reflection interface to within approximately 0.06 ± 0.09 m. By automating the acoustic data processing and alleviating labor- and time-intensive analyses, we extract additional information from the AZFP backscatter data, which is otherwise used for assessing fish and zooplankton densities and behaviors. Beyond applications to new datasets, the approach opens possibilities for the efficient extraction of new information from existing upward-looking sonar records that have been collected in recent decades.

Wind turbine blade icing detection using a novel bidirectional gated recurrent unit with temporal pattern attention and improved coot optimization algorithm

Blade icing detection is significant for the safe operation of wind turbines and to reduce power generation losses. Traditional additional sensor methods for monitoring are limited due to the high cost and damage to the original mechanical structure. The deep learning model shows a good performance for icing detection. This paper proposes a temporal pattern attention-based bidirectional gated recurrent unit (BiGRU-TPA). This novel deep learning framework incorporates the TPA module into the BiGRU module to determine the relationship between multiple sensors at different time steps, extracting features from the raw sensor data for discrimination. Meanwhile, its hyperparameters are optimized using an improved coot optimization algorithm (ICOOT) to further enhance its recognition performance. To alleviate the severe imbalance in the dataset, adaptive synthesis and the sliding window upsampling method are imported to oversample and sliding window the icing state samples, which belong to the minority class. ICOOT-BiGRU-TPA illustrates its advantages compared with other state-of-the-art baseline methods, widely used optimization algorithms, and attention mechanisms applied to the real icing dataset. The ablation study and sensitivity analysis also demonstrate the performance of crucial components in the proposed model. Furthermore, its feasibility and practicality are demonstrated by real-time icing detection.

A Black Ice Detection Method Based on 1-Dimensional CNN Using mmWave Sensor Backscattering

Black ice on the road can be dangerous, as it renders the road slippery and is difficult to identify, owing to its transparency. Although studies on black ice detection using cameras, optical sensors, and infrared sensors have been conducted, these sensors have limitations, as they are affected by low light conditions and sunlight. To detect black ice regardless of low light conditions or sunlight, in this study, we incorporate a mmWave sensor that is consistent with varying light conditions. In the proposed method, a frequency modulated continuous wave is transmitted to the surface by the mmWave sensor, and the mmWave sensor backscattering is modulated by the surface medium and roughness. The proposed method also includes preprocessing to calculate the Range-FFT result of the mmWave sensor backscattering and a classification based on a 1-dimensional convolutional neural network to precisely detect the presence of black ice from the Range-FFT result. As a result of the indoor experiment, the proposed black ice detection method achieves an accuracy of 98.2% on dry, wet, and black ice surfaces. Additionally, under low light conditions and in an outdoor environment with sunlight, the proposed method achieves accuracies of 95.6% and 98.5%, respectively.

Assessing Ice Island Drift Patterns, Ice Island Grounding Locations, and Gridded Bathymetry Products between Nares Strait and the North Atlantic

Large, tabular icebergs known as “ice islands” frequently transit the eastern Canadian Arctic and sub-Arctic after breaking away from ice tongues in northern Greenland. Here, we mine the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database to contribute a descriptive assessment of the drift and grounding locations of Petermann ice islands (PII) following calving events at the Petermann Glacier in 2008, 2010, and 2012. We also use the CI2D3 Database to demonstrate how gridded bathymetry products can be improved using observations of ice island grounding and knowledge of ice island thickness. We find that most PII fragments followed a common southbound drift route directed by outflow from the Arctic Ocean and the dominant Baffin and Labrador Currents, which are strongest along the steep continental shelf break. Smaller ice islands were more prone to drift into the deeper waters of central Baffin Bay. As previously noted by northern community members, ice islands were also observed to drift into many adjacent sounds, channels, inlets, and straits. PIIs often grounded on shoals in Kane Basin, to the east of Coburg Island, and along the southeast coast of Baffin Island. Potential inaccuracies in two gridded bathymetry products were located in Jones Sound, near Coburg Island, and along the east coast of Baffin Island. Our approach to identifying these potential inaccuracies is shown to be sensitive to the estimate of ice island keel depth. Overall, this work provides synthesized observations of ice island occurrence and grounding as well as an approach to improving bathymetry products in a resource-rich marine region where traffic and industry operations are increasing.

Ice detection and tracking by the infrared thermography based on the electro-impulse de-icing system

An infrared thermography method was proposed, which exploited the electro-impulse de-icing system as electromagnetic excitation to detect ice shape and track ice motion. The coupling physical fields of circuit, electromagnetic, stress and heat were expounded theoretically and numerical simulated to verify the feasibility of the measurement. With the use of image processing for ice samples of different system parameters and shapes, the original ice edge was obtained at discharge voltages of 100–200 V. As the residual icing state discussed at discharge voltages of 350–450 V, the trajectory of fragmented ice was tracked by interframe difference and centroid method, and de-icing effect was evaluated by the ratio of the Euclidean distance of the fragmented ice centroids as well. The proposed method presents a novel research insight into integrating ice detection and anti/de-icing technology of aircrafts. It can also be referenced for infrared thermography detection and fragmented ice tracking via heat sources.

An Internet-of-Things-Enabled System for Road Icing Detection and Prediction

Road icing has become one of the most critical factors threatening traffic safety. This article proposes an Internet of Things (IoT)-enabled road icing detection and prediction system. In the proposed system, we first design a low-power icing sensor equipped with IoT function to periodically collect current road status and transmit the sampled data to IoT gateway through Long Range Radio (LoRa). Then, we design a simple but effective algorithm deployed on IoT gateway to identify road icing in time. The algorithm is proposed based on the change trend of the sampled data of the road state, and can be adapted to the icing recognition on the road covered with various impurities. Furthermore, we put forward a newly designed deep neural network model called Trans-CGAN to achieve accurate road icing prediction even the positive and negative samples are imbalanced. Through a real system deployment and experiments, the results show that our proposed system can detect the formation of road icing effectively and timely, and shows better prediction performance of road icing than several representative models.

Fiber Bragg Grating Sensors ice detection: Methodologies and performance

New Researches have been done recently in order to create and adapt new systems to the recent Appendix O FAR 25 Aeronautical aircraft icing regulations. One important issue are the Supercooled Large Droplets (SLD) that are an important hazard in many cases. Novel sensor technologies for aircraft are being developed in order to assess the ice severity and to discriminate between large size droplets and small size droplets ice accretions. The European SENS4ICE project gives founding for the research and development of several aeronautical ice detection systems that can discriminate between Appendix C and Appendix O conditions. In the present paper, the strengths and weaknesses of the Fiber Optic ice Detector (FOD) developed by INTA are discussed using a test matrix from the Canadian National Research Council (NRC). The FOD has presented good performance at the NRC ice wind tunnel tests in the ice detection time, detecting ice in most cases earlier than required by the actual standard (ED-103). Its lightness, low intrusiveness and small size, makes the FOD ideal for aeronautical applications. It measures indirectly the icing cloud characteristics, such as the liquid water content (LWC) and the ice accretion rate, so only an approximate severity assessment could be done. • FBG sensors are useful for fast detection. • The Optic Fiber sensors can estimate the icing conditions in rime conditions • The optic fiber sensor can provide of information about the type of ice and its location

Atmospheric icing process measurement utilizing impedance spectroscopy and thin film structure

To solve the detection problem in icing process, a thin film impedance sensor for atmospheric icing environment is proposed and evaluated. The sensor identifies rapid icing process and static ice layer as different conditions. A theoretical model of temperature-dependent impedance icing detection is built to describe the polarization effect. And the finite element simulation in the case of water film covering ice layer is carried out. The impedance curve distortion caused by phase mixing and temperature gradient during atmospheric icing is analyzed, and 9 related features are extracted. The evaluation experiments of the sensor involve 5 surface conditions, and totally 3143 impedance curves are obtained. The classification results of various models indicate that support vector machine achieves the highest recognition accuracy, reaching 93.1 %. And in atmospheric icing conditions, the RMSE of ice thickness measurement using neural network reaches 0.25 mm in the laboratory tests and 0.51 mm in the wind tunnel tests.

Evaluation of meteorological measurements made on the nacelle of wind turbines in cold climate

Operating wind turbines in cold climate leads to several challenges not encountered in regular climates such as frequent icing. These challenges are amplified by the difficulties associated with accurately quantifying icing without specialized instrumentation. Typically, the performance of wind turbines is monitored by comparing the turbine active power to the available power based on the wind speed measured on the nacelle. However, multiple other factors not related to cold climate can also lead to a reduction of turbine performance. Applying a temperature threshold under which ice accretion is deemed to be able to impact turbine performance is a popular tool to reduce the influence of these other factors. Currently, there is no generally accepted threshold temperature value. Using large data sets from 3 different wind farms equipped with different wind turbine models, this topic was investigated. Following this analysis, it has been found that the optimal strategy to define icing events is to consider that ice may remain on surfaces only if a minimal temperature of 1 °C in the previous 2-h window is observed. This strategy is supported by a temperature bias analysis, a turbine performance analysis and 3 case studies. An ice sensor can also be an efficient strategy to accurately identify icing events. Previous studies raised doubts on the accuracy of ice measurements made on a wind turbine nacelle due to the different conditions experienced by the blade tips. To verify this claim, the present paper provides comparisons of icing periods, defined by the wind turbine performance method, to the detection of an ice sensor installed on the nacelle. It was found that the start of 71 of the 74 identified icing events coincided with ice detection on the nacelle. The three remaining events were minor and could not be linked to the incapacity of detecting ice on the nacelle. In conclusion, sensitive nacelle-based sensors should be considered as suitable options for ice detection on wind turbine nacelle.

Three-dimensional ice shape detection based on flash pulse infrared thermal wave testing

Ice detection is an important issue in the field of icing prevention and de-icing. In this study, an experimental platform was built for ice detection using flash pulse infrared thermal wave detection, followed by a quantitative recognition approach for identifying the three-dimensional shape of ice. The new method was combined the edge recognition with thickness calculation of inverse heat transfer problem. And the edge of the ice was based on the Principal Component Analysis (PCA) and the Canny algorithm. Thus, by processing the ice edges and giving an initial thickness, the finite element model of the ice was established to numerically simulate the temperature distribution for ice thickness inversion based on the forward heat transfer problem. Meanwhile, the thickness of the ice was inversed by the Levenberg-Marquardt (LM) method based on the inverse heat transfer problem. The resulting edges and thickness of the ice were found to be in good agreement with the experimental results, demonstrating the feasibility of the proposed methods. This paves the way to explore an effective accurate and quantitative identification method for three-dimensional ice shape detection in infrared thermal wave detection.

Multifunctional MXene/CNT-based layered film for icing detection, anti-icing, and deicing application

Icing phenomenon usually happens in our daily life, especially in the cold winter or in high altitude areas, which makes us feel inconvenient and greatly threats some fields such as civil aviation or the manufacturing industry. In this study, a multifunctional film with properties of icing detection, anti-icing and deicing was fabricated. One-dimensional material carbon nanotube (CNT) and two-dimensional material Ti3C2Tx MXene were combined by two-step vacuum filtration. Polydimethylsiloxane (PDMS) as the flexible hydrophobic materials was then used to encapsulate layered film. Additionally, PDMS curing process on the sandpaper could make the surface of MXene/CNT layered film possess micro-structures. The low surface energy material PDMS and rough surface structures endow MXene/CNT layered film with good water-repellency. Compared with pure PDMS film (103°), the contact angle of MXene/CNT layered film surface reaches 128°. The result exhibits that a waterdrop(100uL) on the layered film surface takes 1425s to be frozen, which takes longer time than glass and pure PDMS. Additionally, excellent sensibility of the layered film could be used to detect icing phenomenon. The result manifests that gauge factor (GF) of MXene/CNT film reaches 15051 so that different icing stages could be identified by layered film clearly. MXene/CNT layered film possesses good electric heating and photo-thermal properties. The result shows that surface temperature can reach 89 °C with the 2.5 V voltage supply, and temperature reaches 95 °C through the radiation of near-infrared lamp as well. The dual-driven heating of MXene/CNT layered film shows the ability of deicing. 1000 mg ice just takes 223s to be melted entirely by 2.5 V input voltage, and 269s by radiation of 200 mW/cm2, respectively. The multifunctional MXene/CNT based layered film prepared by a simple fabrication method that integrates hydrophobicity, dual-driven heating, and sensibility together, which shows potential application in icing detection, anti-icing, and deicing.