Thermal Anomaly Detection Research Articles

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

UAV-BASED THERMAL ANOMALY DETECTION FOR DISTRIBUTED HEATING NETWORKS

Abstract. District Heating Systems (DHS) distribute heat in terms of hot water or steam. Loss of media (water or steam), and thus energy, is expensive and has a negative impact on the environment. It is therefore of great interest to develop techniques to detect and localize potential leakages fast and cost-effectively. To avoid interference with the operating process of a DHS, airborne thermography comes into place. The use of Unmanned Aerial Vehicles (UAV) as a flexible and low-cost platform equipped with a Thermal InfraRed (TIR) camera is a promising alternative to a conventional manned flight.This paper describes a method for automatic thermal anomaly detection of a DHS. Thermal data acquisition using a UAV is followed by photogrammetric processing of the TIR images. In this way, a thermal orthophoto is produced. The next step is an identification of anomalies by means of image analysis. We apply the Laplacian of Gaussian (LoG) blob detector to find high temperature regions in areas of interest of a thermal orthomosaic. This area of interest is defined around the DHS position in the images as defined in a Geographic Information System. Finally, segmentation and classification are employed to reduce false alarms and localize thermal anomalies. An experimental evaluation using real-world data is presented, showing that the developed method deliverers promising results.

Feasibility study of thermal anomaly detection for earthquake: A case study from 2014 Mae Lao earthquake, Thailand

Earthquake is the natural disaster which causes damage to human lives and their properties, domestic animals and buildings in the areas near the epicentre. The ability to predict the earthquake can greatly reduce in catastrophic damages, but nowadays, earthquake prediction is still the unsolvable problem. However, the earthquake prediction is still an interesting topic for scientists all over the world. One of the important earthquakes precursors in earthquake preparatory phase is thermal anomaly. The thermal region data from remote sensing have been employed recently based on the concept of stress accumulation in the active plate tectonics region, which can be transformed as temperature variation prior event. Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data have been commonly used to locate thermal anomalies prior to occurrence of earthquake event. In Thailand, 2014 Mae Lao Earthquake, the largest earthquake in the Thailand historical record with magnitude Mw 6.1, shook the area of Mae Lao District, Chiang Rai Province on 5th May 2014. To locate possibility of thermal anomalies, the daily data of MODIS MYD11A1 product for 30 days before and after the earthquake were processed and analysed. Average LST before and after earthquake events were used for removing background temperature in the area and comparative method was performed to detect the thermal anomalies. The result found that this simple technique detected the thermal anomaly occurrence during 12-23 days prior to the earthquake and 9-28 days after the earthquake. Nevertheless, in order to understand furthermore about earthquake mechanism, it is necessity of discovered thermal precursors.

Permeability and voids influence on the thermal signal, as inferred by multitemporal UAV-based infrared and visible images

This work presents for the first time associated thermal anomalies and deformations over subsurface cavities/voids located within two harbour quays in Le Havre Harbourg, Normandy, France. An U.A.V. (Unmanned Aerial Vehicle) was used to acquire visible and thermal images over a diurnal cycle (from 7 a.m. to 5 p.m.). The visible images were processed to realize an altimetric model of the platforms by mean of the photogrammetric method, while the thermal infrared (TIR) images were used to study the evolution of their surface temperatures. The obtained 3D model shows the location of five topographic depressions on both quays. The analysis of the evolution of the surface thermal field leads to the detection of cold thermal anomalies that are (1) not correlated to surface properties, and (2) spatially associated to the flanks of the five topographic depressions. Using a 2D conductive-radiative model, we show that these anomalies are not directly due to the thermally insulating effect of an air-filled area. Finally, we conclude that preferential infiltration and subsequent evaporation in the micro-fracturation present within the flanks of the depressions may be responsible for the strong cooling of these zones.

Detection of Thermal Anomalies in the Images of Volcanoes Taken at Night

The problem of revealing the appearance and development of thermal anomalies in the images of volcanoes taken at night in the visible and near-infrared ranges is discussed. An algorithm for detecting and classifying such anomalies is proposed and is tested on the data from the archive of the video monitoring of volcanoes on Kamchatka. The results obtained suggest the possible use of the developed solution in the tasks of real-time monitoring of volcanic activity in the Russian Far East.

Thermal anomalies detection before Saravan earthquake (April 16th, 2013, MW = 7.8) using time series method, satellite, and meteorological data

Thermal anomaly detection related to strong earthquakes is one of the earthquake precursors extensively investigated by researchers. In this research, five years (March 16th to May 16th, every year from 2009 to 2013) of Land Surface Temperature (LST) data products, obtained from satellite data (MODIS-Aqua), and meteorological data (air and soil temperature), using three-dimensional time series method, have been analyzed to characterize the thermal anomalies related to the Saravan earthquake (April 16th, 2013, Mw = 7.8). The results indicate that the thermal anomalies were observed in the time period before and after the earthquake. In the LST time–space–temperature coordinates, the thermal anomaly pattern was seen before the Saravan earthquake. In the Kriging surfaces of the air temperature and the difference between the LST and air temperature (∆T), considerable changes were seen a few days before the earthquake until a few days after it. In addition, the soil temperature time–space–temperature coordinates show changes (increase) a few days after the Saravan earthquake. Therefore, the obtained results suggest that these changes (thermal anomalies) are related to the earthquake and can be expressed as an earthquake predictor.

Improving SEVIRI-Based Hotspots Detection by Using Multiple Simultaneous Observations

Geostationary satellites like meteosat second generation (MSG) allow the detection and monitoring of thermal anomalies (wild fires and volcanic eruptions) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in monitoring and containing the fire events and could provide information on the temporal behavior of the fire (through fire radiative power) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation of the operational applicability of this tool is currently represented by the low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. The limitations related to the sensitivity of the geostationary sensor to fire sizes have been, at least in part, overcome by introducing specific algorithms. However, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km2 (at Mediterranean latitudes), makes this information not very interesting for the institutions involved in firefighting. This paper analyzes the feasibility of improving the localization of the thermal anomalies (hotspots) by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 located at long. 9.0°, MSG-10 located at 0.0° and MSG-8 located at long. 41.5°. The results confirm the possibility of improving the accuracy of the detection by exploiting the observation of the events from different positions in space.

Remote sensing satellite imagery for prospecting geothermal systems in an aseismic geologic setting: Yankari Park, Nigeria

The aim of this investigation is to evaluate the applicability of multispectral satellite imagery for hydrothermal alteration mineral mapping and thermal anomaly detection as a proxy for the characterization of subtle geothermal systems in an aseismic geologic setting. The Yankari Park, an area in northeastern Nigeria characterized by several surface manifestations of hotsprings and hydrothermally altered rock deposits was selected for this study. The study evaluates the effectiveness of the Independent Component Analysis and Mixture Tuned Matched Filtering (MTMF) algorithm for target detection of hydrothermal alteration zones and the Single Channel Algorithm for thermal anomaly detection associated with subtle geothermal systems using spectral bands of Landsat-8, ASTER and Landsat-7, respectively. The results indicated that the use of verified image endmember spectra gives more accurate results compared to that from library spectra by implementing MTMF. Results of mapping thermally anomalous pixels did not conform to known locations of the thermal springs, however, examining the spatial correlation of the anomaly areas with the major fault-fracture systems from the geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat conserving sedimentary formations. This investigation has significant implications as it signifies the need for the integrative use of alteration mineral mapping and thermal anomaly detection for cost-effective prefeasibility stage mapping of geothermal systems in an aseismic geologic setting located in African plate and other regions around the world.

High resolution terrestrial thermography of archaeological sites

AbstractThermal infrared imaging, or thermography, is the remote sensing technique of detecting variations in ground temperature caused by exposed or subsurface archaeological remains either absorbing or radiating heat. Despite its conception in the 1970s, the practice has to date been rarely utilized, as a result of the high cost of the technology and the complex interplay of environmental variables. However, recent studies have demonstrated the effectiveness of the technique, especially when combining modern thermal cameras with unmanned aerial vehicles (UAV). Yet these papers often focus on mid‐ to high‐altitude flights, where the technique is only effective at detecting larger thermal anomalies. This article presents a new method for terrestrial thermography, developed for the Zagora Infrared Photogrammetry Project (The University of Sydney and the Australian Archaeological Institute at Athens). The project undertook a six week thermal investigation of the Early Iron Age site of Zagora and the surrounding hinterland utilizing the newest commercial thermal cameras and UAVs. The method of terrestrial thermography involves using photographic poles and photogrammetry to create high‐resolution thermal orthophotographs, which allow the detection of smaller thermal anomalies, providing significantly more detail than aerial thermography. Several features were discovered using this method, including a possible kiln, which would be the first ever identified at the site.

Thermal anomalies detection before earthquake using three filters (Fourier, Wavelet and Logarithmic Differential Filter), A Case study of two earthquakes in Iran

Earthquake is one of the most destructive natural phenomena which has human and financial losses. The existence of an efficient prediction system and early warning system will be useful for reducing effects of destroying earthquake. In this paper by applying three filters (Fourier, Wavelet and Difference Logarithmic Filter (LDF)) on soil temperature time-series, anomaly behavior before the major earthquakes was studied. Aforementioned methods were performed of the Bam (2003), and Zarand (2005) earthquakes in Iran. The results indicate thermal anomalies were detected before earthquake occurrence. Furthermore, the LDF filter detects thermal anomaly as well as the Fourier and Wavelet filters. For validation of the results, the soil temperature data of the Bam earthquake were considered from the Bam meteorological station and also from the Joroft meteorological stations that located in effective radius (Dobrolsky radius) and the same results was obtained. It states that there is a relation between temperature anomaly behavior and the major earthquakes.

Evaluation of Sensitivity of Downhole Temperature Estimates From Distributed Temperature Sensing Measurements

A range of Australian CCS research activities (CO2CRC Otway Stage 3, CO2CRC Otway shallow controlled release and CSIRO Insitu-lab) involving subsurface characterisation and/or CO2 injection are at the planning stage. Fibre-optic sensing is considered as part of the downhole reservoir characterisation and surveillance monitoring system for all of these projects. In this work, we investigate the sensitivity of DTS for the detection of thermal anomalies in view of the design of future geological storage activities in Australia. Ahead of Phase 2 of the CO2CRC Otway shallow CO2 controlled release experiment, a DTS system was installed in the CRC-3 well for equipment testing and baseline characterisation, at the same time as the acquisition of the CO2CRC Stage 2C M5 seismic survey. Temperature measurements were acquired from well-head to bottom-hole for 13 days at a rate of one-minute measurement every 4 minutes. The temperature profile did not change significantly over time. The integration and processing of more than 3400 temperature traces enabled estimation of a temperature profile with a resolution of 0.01 °C. The use of DTS during the CO2CRC M5 survey highlights the detectability of a VSP tool producing 11W and 4W at two distinct depths. The setup was capable of detecting a 0.7 °C anomaly very quickly. A smaller anomaly (0.1 °C) was detected with the benefit of data processing of the long term dataset. The high spatial resolution of DTS enables location of thermal anomalies to within 2 metres.

Assessing Performance of the RSTVOLC Multi-Temporal Algorithm in Detecting Subtle Hot Spots at Oldoinyo Lengai (Tanzania, Africa) for Comparison with MODLEN

The identification of subtle thermal anomalies (i.e., of low-temperature and/or spatial extent) at volcanoes by satellite is of great interest for scientists, especially because minor changes in surface temperature might reveal an unrest phase or impending activity. A good test case for assessing the sensitivity level of satellite-based methods is to study the thermal activity of Oldoinyo Lengai (OL) (Africa, Tanzania), which is the only volcano on Earth emitting natrocarbonatite lavas at a lower temperature (i.e., in the range 500–600 °C) than usual magmatic surfaces. In this work, we assess the potential of the RSTVOLC multi-temporal algorithm in detecting subtle hot spots at OL for comparison with MODLEN: A thermal anomaly detection method tailored to OL local conditions, by using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Our results investigating the eruptive events of 2000–2008 using RSTVOLC reveal the occurrence of several undocumented thermal activities of OL, and may successfully integrate MODLEN observations. In spite of some known limitations strongly affecting the identification of volcanic thermal anomalies from space (e.g., cloud cover; occurrence of short-lived events), this work demonstrates that RSTVOLC may provide a very important contribution for monitoring the OL, identifying subtle hot spots showing values of the radiant flux even around 1 MW.

Development of a miniature microwave radiothermograph for monitoring the internal brain temperature

To improve efficiency of non-invasive monitoring of the internal brain temperature, a small-size single-channel microwave radiothermograph consisting of a miniature radiometer and a radiometric sensor based on a printed antenna was developed. Such solution is necessary to provide physicians with a system of non-invasive monitoring of diagnosis and treatment processes. Mathematical modeling and experimental verification of the technical solutions obtained are described in this paper. A miniature radiothermometer was developed. It is a balance modulation radiometer designed on the basis of the R.H. Dicke’s circuit with two loads. Taking into account the requirements of miniaturization, a radiometric sensor was developed by means of numerical simulation. As a result of calculations, optimum antenna dimensions were determined (the total size: o30 mm, the size of the foil flane substrate: o23 mm, dimensions of the emitter slot: 16 mm×2 mm). According to the mathematical modeling, the depth of detection of thermal anomalies was not less than 20 mm for the printed antenna which is practically the same as for the waveguide antenna successfully used at present in brain radiothermometry. The standing wave coefficient was determined for various head regions: frontal, temporal, parietal, occipital and the transient between the occipital and parietal regions. Experimental tests of the radiothermograph on water phantoms and biological objects have been carried out. A very good coincidence between the data of numerical simulation and the physical SWR experiment in a range of 1.04–1.8 was obtained. As a result of the study, it has been found that the radiothermograph with a printed slot antenna enabled measurement of internal brain temperature with an acceptable accuracy (±0.2 °C). This will ensure control of craniocerebral hypothermia in patients with brain stroke and allow doctors to promptly change the hypothermia tactics. Small size of the created unit will make it possible to combine it with medical robotic systems to improve treatment effectiveness.

Model-Based Thermal Anomaly Detection in Cloud Datacenters Using Thermal Imaging

The growing importance, large scale, and high server density of high-performance computing datacenters make them prone to attacks, misconfigurations, and failures (of the cooling as well as of the computing infrastructure). Such unexpected events often lead to thermal anomalies – hotspots, fugues, and coldspots – which impact the cost of operation of datacenters. A model-based thermal anomaly detection mechanism, which compares expected (obtained using heat-generation and -extraction models) and observed thermal maps (obtained using thermal cameras) of datacenters, is proposed. In addition, a novel Thermal Anomaly-aware Resource Allocation (TARA) is designed to induce a time-varying thermal fingerprint (thermal map) of the datacenter so to maximize the detection accuracy of the anomalies. As shown via experiments on a small-scale testbed as well as via trace-driven simulations, such model-based thermal anomaly detection solution in conjunction with TARA significantly improves the detection probability compared to anomaly detection when scheduling algorithms such as random, round robin, and best-fit-decreasing are employed.

Karymsky volcano eruptive plume properties based on MISR multi-angle imagery, and volcanological implications.

Space-based, operational instruments are in unique positions to monitor volcanic activity globally, especially in remote locations or where suborbital observing conditions are hazardous. The Multi-angle Imaging SpectroRadiometer (MISR) provides hyper-stereo imagery, from which the altitude and microphysical properties of suspended atmospheric aerosols can be derived. These capabilities are applied to plumes emitted at Karymsky volcano from 2000 to 2017. Observed plumes from Karymsky were emitted predominantly to an altitude of 2-4 km, with occasional events exceeding 6 km. MISR plume observations were most common when volcanic surface manifestations, such as lava flows, were identified by satellite-based thermal anomaly detection. The analyzed plumes predominantly contained large (1.28 µm effective radius), strongly absorbing particles indicative of ash-rich eruptions. Differences between the retrievals for Karymsky volcano's ash-rich plumes and the sulfur-rich plumes emitted during the 2014-2015 eruption of Holuhraun (Iceland) highlight the ability of MISR to distinguish particle types from such events. Observed plumes ranged from 30 to 220 km in length, and were imaged at a spatial resolution of 1.1 km. Retrieved particle properties display evidence of downwind particle fallout, particle aggregation and chemical evolution. In addition, changes in plume properties retrieved from the remote-sensing observations over time are interpreted in terms of shifts in eruption dynamics within the volcano itself, corroborated to the extent possible with suborbital data. Plumes emitted at Karymsky prior to 2010 display mixed emissions of ash and sulfate particles. After 2010, all plumes contain consistent particle components, indicative of entering an ash-dominated regime. Post-2010 event timing, relative to eruption phase, was found to influence the optical properties of observed plume particles, with light-absorption varying in a consistent sequence as each respective eruption phase progressed.

Thermal Anomalies Detection Using Comparative Method for Small Earthquake

Thermal anomaly is one of the earthquake precursor in the earthquake preparatory phase. Remote sensing in thermal region has been employed based on the concept of stress accumulation in the active plate tectonics region, which may be transformed as temperature variation prior to earthquake. MODIS Land Surface Temperature has been commonly used to locate the thermal anomalies before the earthquake. Recently researches have been focusing on moderate or large magnitude earthquake events. In Thailand, small earthquake can severely damage the unprepared area. This study, the daily day- and nighttime data of MODIS MOD11A1 product for 30 days before and 15 days after the earthquake on April 22, 2007, in Wiang Pa Pao District, Chiang Rai Province, Thailand, were processed and analysed to locate possibility of thermal anomalies. Thermal anomalies before and after the earthquakes were detected using the comparative method. The result found that the thermal anomaly temperature could be high up to 4.1 - 10.9 C which occurred in 21 - 22 days prior to the earthquake. Therefore, it may conclude that small earthquake can also release energy as the detectable thermal anomaly. However, more study about the relationship between thermal precursor and earthquake is needed to continue.

Remote Sensed Land Surface Temperature Anomalies for Earthquake Prediction

Thermal anomaly Detection prior to earthquake events has been widely confirmed by researchers over the past decade. In this paper, we use robust satellite technique approach (RST) on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdès Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of robust satellite technique as an approach highly effective for monitoring the earthquakes.

DSC and single-crystal X-ray structural study on phase transitions in RbNaSO4

ABSTRACTAs one of the new ABXY4-type member in the (NH4)2SO4 family, rubidium sodium sulphate (RbNaSO4) was crystallized by evaporating the aqueous solution at 303 K. The phase transition and crystal structure were studied by means of thermogravimetric analysis, differential scanning calorimetry (DSC) and single-crystal X-ray diffraction. As a result of thermal anomaly detection in the temperature range between 93 K and 773 K, anomalies corresponding to phase transitions were observed at around 693 K and 503 K. It was also revealed that RbNaSO4 belonged to the monoclinic crystal system at room temperature and had lattice constants of a = 5.9778(5) Å, b = 10.4509 (8) Å and c = 7.8294 (11) Å, γ = 90°.

The Influence of Land Use on the Grassland Fire Occurrence in the Northeastern Inner Mongolia Autonomous Region, China.

Grassland, as one of the most important ecosystems on Earth, experiences fires that affect the local ecology, economy and society. Notably, grassland fires occur frequently each year in northeastern China. Fire occurrence is a complex problem with multiple causes, such as natural factors, human activities and land use. This paper investigates the disruptive effects of grassland fire in the northeastern Inner Mongolia Autonomous Region of China. In this study, we relied on thermal anomaly detection from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to identify fire occurrences, and land use data were acquired by Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM). We discussed the relationship between land use and the spatial distribution of grassland fires. The results showed that the impact of land use on grassland fires was significant. Spatially, approximately 80% of grassland fires were clustered within 10 km of cultivated land, and grassland fires generally occurred in areas of intense human activity. The correlation between the spatial distribution of grassland fires and the land use degree in 2000, 2005 and 2010 was high, with R2 values of 0.686, 0.716, 0.633, respectively (p < 0.01). These results highlight the importance of the relationship between land use and grassland fire occurrence in the northeastern Inner Mongolia Autonomous Region. This study provides significance for local fire management and prevention.

VIIRS Nightfire Remote Sensing Volcanoes

Satellite based remote sensing of active volcanoes has been performed in various forms since 1965. Compared to “on the ground” observations it lets data to be gathered globally at regular pace for long periods of time without the need for local maintenance. Currently existing publicly available volcanoes thermal activity monitoring systems rely on the detection algorithms narrowly specified for volcanoes temperature ranges and operate using the data from previous generation of sensors, which is supported with non-reserved constellation of two satellites. The presented work proposes pipeline (the sequence of actions) based on the clustering of the data received from the Nightfire thermal anomalies detection algorithm, which is not focused on the specific type of infrared sources. Pipeline has been tested on Kamchatka’s region 2016 year dataset and proved to produce sound results corresponding to manual observations.