THERMAL ANOMALY DETECTION BASED ON SALIENCY ANALYSIS FROM MULTIMODAL IMAGING SOURCES

Abstract. Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.

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Processing Thermal Infrared Imagery Time-Series from Volcano Permanent Ground-Based Monitoring Network. Latest Methodological Improvements to Characterize Surface Temperatures Behavior of Thermal Anomaly Areas

Remote Sensing ◽

10.3390/rs11050553 ◽

2019 ◽

Vol 11 (5) ◽

pp. 553 ◽

Cited By ~ 4

Author(s):

Fabio Sansivero ◽

Giuseppe Vilardo

Keyword(s):

Time Series ◽

Processing System ◽

Monitoring Network ◽

Thermal Anomaly ◽

Thermal Infrared ◽

Heat Fluxes ◽

Campi Flegrei ◽

Thermal Anomalies ◽

Fast Processing ◽

Thermal Infrared Imagery

In this technical paper, the state-of-art of automated procedures to process thermal infrared (TIR) scenes acquired by a permanent ground-based surveillance system, is discussed. TIR scenes regard diffuse degassing areas at Campi Flegrei and Vesuvio in the Neapolitan volcanic district (Italy). The processing system was developed in-house by using the flexible and fast processing Matlab© environment. The multi-step procedure, starting from raw infrared (IR) frames, generates a final product consisting mainly of de-seasoned temperatures and heat fluxes time-series as well as maps of yearly rates of temperature change of the IR frames. Accurate descriptions of all operational phases and of the procedures of analysis are illustrated; a Matlab© code (Natick, Massachusetts, U.S.A.) is provided as supplementary material. This product is ordinarily addressed to study volcanic dynamics and improve the forecasting of the volcanic activity. Nevertheless, it can be a useful tool to investigate the surface temperature field of any areas subjected to thermal anomalies, both of natural and anthropic origin.

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Autonomous Detection of Thermal Anomalies in Data Centers

ASME 2009 InterPACK Conference, Volume 2 ◽

10.1115/interpack2009-89140 ◽

2009 ◽

Cited By ~ 1

Author(s):

Manish Marwah ◽

Ratnesh K. Sharma ◽

Wilfredo Lugo

Keyword(s):

Anomaly Detection ◽

Data Center ◽

Data Centers ◽

Principal Component ◽

Thermal Anomaly ◽

Real Data ◽

Detection Methods ◽

Large Temperature ◽

Efficient Operation ◽

Thermal Anomalies

In recent years, there has been a significant growth in number, size and power densities of data centers. A significant part of data center power consumption is attributed to the cooling infrastructure, consisting of computer air conditioning units (CRACs), chillers and cooling towers. For energy efficient operation and management of the cooling resources, data centers are beginning to be extensively instrumented with temperature sensors. While this allows cooling actuators, such as CRAC set point temperature, to be dynamically controlled and data centers operated at higher temperatures to save energy, it also increases chances of thermal anomalies. Furthermore, considering that large data centers can contain thousands to tens of thousands of such sensors, it is virtually impossible to manually inspect and analyze the large volumes of dynamic data generated by these sensors, thus necessitating autonomous mechanisms for thermal anomaly detection. Also, in addition to threshold-based detection methods, other mechanisms of anomaly detection are also necessary. In this paper, we describe the commonly occurring thermal anomalies in a data center. Furthermore, we describe — with examples from a production data center — techniques to autonomously detect these anomalies. In particular, we show the usefulness of a principal component analysis (PCA) based methodology to a large temperature sensor network. Specifically, we examine thermal anomalies such as those related to misconfiguration of equipment, blocked vent tiles, faulty sensor and CRAC related anomalies. Furthermore, several of these anomalies normally go undetected since no temperature thresholds are violated. We present examples of the thermal anomalies and their detection from a real data center.

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Pre-seismic Thermal Anomalies from Satellite Observations: A Review

10.5194/nhess-2017-211 ◽

2017 ◽

Author(s):

Zhong-Hu Jiao ◽

Jing Zhao ◽

Xinjian Shan

Keyword(s):

Anomaly Detection ◽

Thermal Anomaly ◽

Global Scale ◽

Satellite Observations ◽

Detection Methods ◽

Thermal Anomalies ◽

Detection Algorithms ◽

Detection Technology ◽

Earth Observation Satellites ◽

Future Work

Abstract. Detecting thermal anomalies prior to strong earthquakes is a key in understanding and forecasting earthquake activities because of its recognition of thermal radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper dedicatedly reviews the progress and development of pre-seismic thermal anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth’s surface, are discussed. Second, different anomaly detection methods, which are used to extract thermal anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring and improve short- and medium-term forecasting, which should play a large and growing role in pre-seismic thermal anomaly research.

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Fault distance-based approach in thermal anomaly detection before strong Earthquakes

10.5194/nhess-2020-391 ◽

2020 ◽

Author(s):

Arash Karimi Zarchi ◽

Mohammad Reza Saradjian Maralan

Keyword(s):

Anomaly Detection ◽

Land Surface ◽

Input Data ◽

Active Faults ◽

Thermal Anomaly ◽

Detection Methods ◽

False Alarms ◽

Earthquake Intensity ◽

Strong Earthquakes ◽

Thermal Anomalies

Abstract. The recent scientific studies in the context of earthquake precursors reveal some processes connected to seismic activity including thermal anomaly before earthquakes which is a great help for making a better decision regarding this disastrous phenomenon and reducing its casualty to a minimum. This paper represents a method for grouping the proper input data for different thermal anomaly detection methods using the land surface temperature (LST) mean in multiple distances from the corresponding fault during the 40 days (i.e. 30 days before and 10 days after impending earthquake) of investigation. Six strong earthquakes with Ms > 6 that have occurred in Iran have been investigated in this study. We used two different approaches for detecting thermal anomalies. They are mean-standard deviation method also known as standard method and interquartile method which is similar to the first method but uses different parameters as input. Most of the studies have considered thermal anomalies around the known epicentre locations where the investigation can only be performed after the earthquake. This study is using fault distance-based approach in predicting the earthquake regarding the location of the faults as the potential area. This could be considered as an important step towards actual prediction of earthquake’s time and intensity. Results show that the proposed input data produces less false alarms in each of the thermal anomaly detection methods compared to the ordinary input data making this method much more accurate and stable considering the easy accessibility of thermal data and their less complicated algorithms for processing. In the final step, the detected anomalies are used for estimating earthquake intensity using Artificial Neural Network (ANN). The results show that estimated intensities of most earthquakes are very close to the actual intensities. Since the location of the active faults are known a priori, using fault distance-based approach may be regarded as a superior method in predicting the impending earthquakes for vulnerable faults. In spite of the previous investigations that the studies were only possible aftermath, the fault distance-based approach can be used as a tool for future unknown earthquakes prediction. However, it is recommended to use thermal anomaly detection as an initial process to be jointly used with other precursors to reduce the number of investigations that require more complicated algorithms and data processing.

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A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory

Symmetry ◽

10.3390/sym10060183 ◽

2018 ◽

Vol 10 (6) ◽

pp. 183 ◽

Cited By ~ 5

Author(s):

Naeem Ayoub ◽

Zhenguo Gao ◽

Bingcai Chen ◽

Muwei Jian

Keyword(s):

Evidence Theory ◽

Fusion Rule ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection

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THERMAL INFRARED INSPECTION OF ROOF INSULATION USING UNMANNED AERIAL VEHICLES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w4-381-2015 ◽

2015 ◽

Vol XL-1/W4 ◽

pp. 381-386 ◽

Cited By ~ 8

Author(s):

J. Zhang ◽

J. Jung ◽

G. Sohn ◽

M. Cohen

Keyword(s):

Anomaly Detection ◽

Random Field ◽

Markov Random Field ◽

Thermal Anomaly ◽

Thermal Infrared ◽

Throughput Capacity ◽

Detection Accuracy ◽

Visual Interpretation ◽

Markov Random ◽

Calibration Algorithm

UAVs equipped with high-resolution thermal cameras provide an excellent investigative tool used for a multitude of building-specific applications, including roof insulation inspection. We have presented in this study a relative thermographic calibration algorithm and a superpixel Markov Random Field model to address problems in thermal infrared inspection of roof insulation using UAVs. The relative thermographic radiometric calibration algorithm is designed to address the autogain problem of the thermal camera. Results show the algorithm can enhance the contrast between warm and cool areas on the roof surface in thermal images, and produces more constant thermal signatures of different roof insulations or surfaces, which could facilitate both visual interpretation and computer-based thermal anomaly detection. An automatic thermal anomaly detection algorithm based on superpixel Markov Random Field is proposed, which is more computationally efficient than pixel based MRF, and can potentially improve the production throughput capacity and increase the detection accuracy for thermal anomaly detection. Experimental results show the effectiveness of the proposed method.

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Thermal anomaly detection in datacenters

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406211429764 ◽

2011 ◽

Vol 226 (8) ◽

pp. 2104-2117 ◽

Cited By ~ 1

Author(s):

Yang Yuan ◽

Eun Kyung Lee ◽

Dario Pompili ◽

Junbi Liao

Keyword(s):

Neural Network ◽

Anomaly Detection ◽

Large Scale ◽

Thermal Anomaly ◽

Detection Methods ◽

Support Vector ◽

Self Organizing Map ◽

Air Conditioner ◽

Thermal Anomalies ◽

The Relationship

The high density of servers in datacenters generates a large amount of heat, resulting in the high possibility of thermally anomalous events, i.e. computer room air conditioner fan failure, server fan failure, and workload misconfiguration. As such anomalous events increase the cost of maintaining computing and cooling components, they need to be detected, localized, and classified for taking appropriate remedial actions. In this article, a hierarchical neural network framework is proposed to detect small- (server level) and large-scale (datacenter level) thermal anomalies. This novel framework, which is organized into two tiers, analyzes the data sensed by heterogeneous sensors such as sensors built in the servers and external sensors (Telosb). The proposed solution employs a neural network to learn about (a) the relationship among sensing values (i.e. internal, external, and fan speed) and (b) the relationship between the sensing values and workload information. Then, the bottom tier of our framework detects thermal anomalies, whereas the top tier localizes and classifies them. Our solution outperforms other anomaly-detection methods based on regression model, support vector machine, and self-organizing map, as shown by the experimental results.

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Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

The Open Fuels & Energy Science Journal ◽

10.2174/1876973x01407010078 ◽

2014 ◽

Vol 7 (1) ◽

pp. 78-83 ◽

Cited By ~ 1

Author(s):

Jiatang Cheng ◽

Li Ai ◽

Zhimei Duan ◽

Yan Xiong

Keyword(s):

Fault Diagnosis ◽

Information Fusion ◽

Evidence Theory ◽

Fault Classification ◽

Fusion Method ◽

Inconsistent Result ◽

High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

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