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Relevant bibliographies by topics / Smart sensors; Fault detection; Thermocouples

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Academic literature on the topic 'Smart sensors; Fault detection; Thermocouples'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Smart sensors; Fault detection; Thermocouples"

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Martinez, Dominique, and Daniel Estève. "Adaptive quantization and fault detection in smart sensors." Sensors and Actuators A: Physical 47, no. 1-3 (1995): 530–33. http://dx.doi.org/10.1016/0924-4247(94)00956-i.

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Ji, Yongzhi. "Application of fault detection using distributed sensors in smart cities." Physical Communication 46 (June 2021): 101182. http://dx.doi.org/10.1016/j.phycom.2020.101182.

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Trothe, Max Emil S., Hamid Reza Shaker, Muhyiddine Jradi, and Krzysztof Arendt. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings." Energies 12, no. 9 (2019): 1601. http://dx.doi.org/10.3390/en12091601.

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Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To t

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N.S.Kalpana, R., and Dr P.Nallathai. "Survey on Fault Detection and Diagnosis Using Neural Network in WBAN." International Journal of Engineering & Technology 7, no. 2.20 (2018): 346. http://dx.doi.org/10.14419/ijet.v7i2.20.16731.

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Wireless Body Area Networks (WBAN) is the sensor network used for monitoring health information in e-health systems. WBAN is a combination of sensors used to obtain vital information from the body. It is a special type of WSN. WBAN technology should handle the data in a smart way by reacting to the monitored data and to evaluate the data. It requires fault detection and diagnosis methods for sensors used in WBAN. This paper gives a survey of different types of neural-network approaches for faults detection and diagnosis in WBAN using neural network.

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Hadi, Mohammed H., Abbas H. Issa, and Atheer A. Sabri. "Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks." Engineering and Technology Journal 39, no. 4A (2021): 653–62. http://dx.doi.org/10.30684/etj.v39i4a.1951.

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In this paper, both the design and hardware of Fault Detection (FD) in Wireless Sensor Network (WSN) was implemented using FPGA NI myRIO kit, wireless temperature sensors network with small size, low cost, and low power consumption. Work data processing was performed using pattern recognition methods to detect residual generation. LabVIEW software environment was employed for system performance. In this paper. The design of the hardware circuit NI myRIO kit received temperature from the sensors. The examined system showed an ability to monitor and track any fault or fire that may occur; based

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Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (2019): 6171. http://dx.doi.org/10.3390/su11216171.

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With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optim

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Omar, Khaldoon Ammar, Ahmed Dhahir Malik, Ansar Jamil, and Hasan Muwafeq Gheni. "Faulty sensor detection using multi-variate sensors in internet of things (IoTs)." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1391. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1391-1399.

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IoT devices are lightweight and have limited computational capabilities often exposed to harsh environments, which can cause failure on the IoT devices [1, 2]. The failure on the IoT devices is also caused due to limited battery life, hardware failure or human mistakes. Sensor faults can be categorized under one type of hardware failure, such as sensor burn, reduced sensor sensitivity and malfunctioned sensors. Any faulty on the IoT devices can cause a problem on the overall operation of the IoT system. Traditional ways in the management of IoT devices is a maintenance officer require to check

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Kolbadi Nejad, M., A. Selk Ghafari, and A. Zabihollah. "Fault Detection in a Cracked Pipeline Embedded with Piezoelectric Sensors/Actuators Employing Bond Graph Approach." Advanced Materials Research 476-478 (February 2012): 1015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.1015.

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The main scope of this article is to simulate a cracked pipeline embedded with piezoelectric sensors and actuators utilizing bond graph approach. Piezoelectric sensors/actuators are becoming very popular in various applications such as health monitoring, active vibration control or noise reduction, and as a part of the systems called smart structures. The proposed bond graph structure in this study, graphically illustrates the power flow between the electrical and mechanical frameworks included in the system. In addition, the proposed framework makes it possible to utilize a modular structure

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Malik, Ahmed Dhahir, Ansar Jamil, Khaldoon Ammar Omar, and Mohd Helmy Abd Wahab. "Implementation of Faulty Sensor Detection Mechanism using Data Correlation of Multivariate Sensor Readings in Smart Agriculture." Annals of Emerging Technologies in Computing 5, no. 5 (2021): 1–9. http://dx.doi.org/10.33166/aetic.2021.05.001.

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Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers, and crops regardless of their geographical differences. Faulty sensor detection is critical in IoT. When a sensor becomes faulty, missing data and/or bad data is provided to the control and management systems, which may lead to potential malfunction or even system failures. Because of this, a sensor fault detection mechanism must be implemented in an IoT system to eliminate this potential fault. This paper focuses on the implementation of a faulty sensor detection

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Liu, Jinfu, Zhenhua Long, Mingliang Bai, Linhai Zhu, and Daren Yu. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems." Energies 14, no. 2 (2021): 389. http://dx.doi.org/10.3390/en14020389.

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As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at t

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Dissertations / Theses on the topic "Smart sensors; Fault detection; Thermocouples"

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Yang, Janice Ching-Yi. "Self-validating sensors." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336611.

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Abhinav, Abhinav. "Sensor Failure Mode Detection and Self-Validation." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227254283.

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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of

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Books on the topic "Smart sensors; Fault detection; Thermocouples"

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Lehrasab, Nadeem. A generic fault detection and isolation approach for single-throw mechanical equipment. University of Birmingham, 1999.

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Book chapters on the topic "Smart sensors; Fault detection; Thermocouples"

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Campana, Tiziana, and Gregory M. P. O’Hare. "Intellectus: Multi-Hop Fault Detection Methodology." In Smart Sensors, Measurement and Instrumentation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12898-6_9.

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Zhang, X. F., N. Q. Hu, L. Zhang, X. F. Wu, L. Hu, and Z. Cheng. "On the Use of Stochastic Resonance in Mechanical Fault Signal Detection." In Smart Sensors, Measurement and Instrumentation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56126-4_13.

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Conference papers on the topic "Smart sensors; Fault detection; Thermocouples"

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Ye, Juan, Graeme Stevenson, and Simon Dobson. "Fault detection for binary sensors in smart home environments." In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2015. http://dx.doi.org/10.1109/percom.2015.7146505.

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Kao, Imin, and Kunbo Zhang. "Miniaturized sensors for intelligent system fault detection and diagnosis (FDD)." In The 14th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, edited by Masayoshi Tomizuka, Chung-Bang Yun, and Victor Giurgiutiu. SPIE, 2007. http://dx.doi.org/10.1117/12.717915.

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Wang, Nan, Visvakumar Aravinthan, and Yanwu Ding. "Feeder-level fault detection and classification with multiple sensors: A smart grid scenario." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884569.

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Herrera, Guillermo A., Geoffrey P. McKnight, Xiujie Gao, Nancy Johnson, and Alan L. Browne. "Use of Intrinsic Electrical Resistance Changes in Shape Memory Alloys as Robust Actuator State and Fault Detection Sensors." In ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/smasis2011-5000.

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Shape memory alloy (SMA) used as electrically controlled on-demand actuators provide engineers new opportunities to create lighter automated components and devices in vehicles due to their compact size, silent operation, and inherently low mass. Outstanding and critical issues are cost-effective and robust control and protection of the SMA actuator element within the device to achieve long lasting service. SMA responds autonomously to external conditions such as temperature and stress and exhibit many property changes during excitation, but many current devices only use SMA as compact actuator

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Munir, Muhammad Ibrahim, Sajid Hussain, Ali Al-Alili, Reem Al Ameri, and Ehab El-Sadaany. "Fault Detection and Classification in Smart Grids Using Wavelet Analysis." In ASME 2020 14th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/es2020-1641.

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Abstract One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This pap

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Parlos, Alexander G., Kyusung Kim, and Raj M. Bharadwaj. "Sensorless Early Detection of Mechanical Faults: Developments in Smart Rotating Machines." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21750.

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Abstract Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy and while exhibiting acceptably low false alarm rates. Such designs must have applicability to a large class of machines, require installation of no additional sensors, and require minimal detailed information regarding the specific machine design. Electromechanical systems, such as electric motors driving dynamic loads like pumps and compressors, often develop incipient failures that result in downtime. There is a large number of such failure modes, with a large majority being of mecha

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Libeyre, François, Francis Bainier, and Pascal Alas. "A Comprehensive Modeling of Centrifugal Compressor Vibrations for Early Fault Detection." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15641.

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Abstract In the last decade, the development of machine connectivity has made possible early fault detection with remote analysis of operating data. Solutions aiming to reduce maintenance costs and production losses due to unplanned downtimes were brought to market. These solutions provide with a model of the equipment in healthy conditions using machine learning techniques applied on historical data. During operation a warning is issued when expected and actual measurements do not match. Although these solutions have proven their value to detect abnormal behaviors, they generate a large numbe

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Eshghi, Amin Toghi, Soobum Lee, HyunJun Jung, and Pingfeng Wang. "Design of a Probabilistic Health Monitoring System Using Embedded Piezoelectric Patch Sensors." In ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/smasis2019-5506.

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Abstract This paper proposes a probabilistic model for the placement of sensors that considers uncertain factors in the sensing system to find the best arrangement of sensor locations. Traditional procedures for structural health monitoring (SHM) usually rely on simplified behavior and deterministic factors from structure’s response. Incorporating the sources of uncertainty (e.g., loading condition, material properties, and geometrical parameters) in the design of sensor network will enhance the safety and extend the useful life of the complex mechanical systems. The proposed method is defined

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Herzog, James P., Jason Hanlin, Stephan W. Wegerich, and Alan D. Wilks. "High Performance Condition Monitoring of Aircraft Engines." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68485.

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A similarity-based modeling (SBM) technique is demonstrated that provides very early annunciation of the onset of gas path faults in aircraft engines. This powerful approach is shown to provide high fidelity estimates for real-time condition monitoring of aircraft engine signals. These estimates are used to detect the onset of changes in the inter-relationship between the various signals using a sophisticated set of built-in algorithms and tools. The ability of the SBM software to reliably detect subtle changes in signal behavior that are characteristic of a developing anomaly is coupled with

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Sundararajan, V., Andrew Redfern, Michael Schneider, Paul Wright, and James Evans. "Wireless Sensor Networks for Machinery Monitoring." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82224.

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Wireless sensor networks (WSNs) are currently been actively investigated in the research community on account of their unprecedented spatial density of sensors, local computational plus storage capacity, and potential for distributed and fault-tolerant monitoring. Today, they are mainly deployed for environmental monitoring - e.g. for “smart building” control, water quality monitoring, and botanical studies. In the future, it is clear they have a huge potential for industrial applications such as machinery monitoring, shop instrumentation, and process control. Wireless sensor nodes can be moun

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