Relevant bibliographies by topics / Sensor failures

Academic literature on the topic 'Sensor failures'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Sensor failures"

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Reeves, J., R. Remenyte-Prescott, and J. Andrews. "Sensor selection for fault diagnostics using performance metric." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 4 (October 10, 2018): 537–52. http://dx.doi.org/10.1177/1748006x18804690.

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As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected. In this article, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this article.
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ALAG, SATNAM, ALICE M. AGOGINO, and MAHESH MORJARIA. "A methodology for intelligent sensor measurement, validation, fusion, and fault detection for equipment monitoring and diagnostics." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 4 (September 2001): 307–20. http://dx.doi.org/10.1017/s0890060401154053.

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In equipment monitoring and diagnostics, it is very important to distinguish between a sensor failure and a system failure. In this paper, we develop a comprehensive methodology based on a hybrid system of AI and statistical techniques. The methodology is designed for monitoring complex equipment systems, which validates the sensor data, associates a degree of validity with each measurement, isolates faulty sensors, estimates the actual values despite faulty measurements, and detects incipient sensor failures. The methodology consists of four steps: redundancy creation, state prediction, sensor measurement validation and fusion, and fault detection through residue change detection. Through these four steps we use the information that can be obtained by looking at: information from a sensor individually, information from the sensor as part of a group of sensors, and the immediate history of the process that is being monitored. The advantage of this methodology is that it can detect multiple sensor failures, both abrupt as well as incipient. It can also detect subtle sensor failures such as drift in calibration and degradation of the sensor. The four-step methodology is applied to data from a gas turbine power plant.
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Gutiérrez, Sebastián, and Hiram Ponce. "An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions." Sensors 19, no. 4 (February 19, 2019): 854. http://dx.doi.org/10.3390/s19040854.

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Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.
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Wu, Wenbo, Lu Zhang, Hongyong Fu, Ke Wang, and Xuzhi Li. "Safety Impact Analysis Considering Physical Failures and Cyber-Attacks for Mechanically Pumped Loop Systems (MPLs)." Sensors 22, no. 13 (June 24, 2022): 4780. http://dx.doi.org/10.3390/s22134780.

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As complex systems composed of physical and cyber components, mechanically pumped loop systems (MPLs) are vulnerable to both passive threats (e.g., physical failures) and active threats such as cyber-attacks launched on the network control systems. The impact of the aforementioned two threats on MPL operations is yet unknown, and there is no practical way to evaluate their severity. To assess the severity of the impact of physical failures and cyber-attacks on MPLs, a safety impact analysis framework based on Elman Neural Network (ENN) observers and the Gaussian Mixture Model (GMM) algorithm is suggested. The framework discusses three common attack and failure modes: sensor hard failure that occurs suddenly, sensor soft failure that occurs gradually over time, and denial-of-service (DoS) attacks that prevent communication between the controller and valve. Both sensor failures and DoS attacks render the system unsafe, according to simulation data. In comparison to DoS attacks, however, sensor failures, particularly soft failures, inflict the greatest harm to the MPLs. Furthermore, sensors engaged in global control, rather than those involved in local control, need additional protection.
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Choe, K., and H. Baruh. "Sensor Failure Detection in Flexible Structures Using Modal Observers." Journal of Dynamic Systems, Measurement, and Control 115, no. 3 (September 1, 1993): 411–18. http://dx.doi.org/10.1115/1.2899117.

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A procedure is developed to detect sensor failures in flexible structures by means of observers designed at the modal level. Estimates of the modal coordinates generated by the modal observers are used to estimate the system output at the sensor’s locations. These estimates of the system output are then compared with the sensors’ measurements to detect failure. It is shown that, when the observer gains are properly selected, failure of a certain sensor primarily affects the estimate of that sensor, and it affects the estimates of the operational sensors much less. This makes it possible to detect multiple sensor failures. Because the observers are designed for each mode individually, one can obtain closed-form expressions for the observer poles, making the failure detection procedure applicable to high-order systems.
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Sun, Bing, Chenxi Wu, and Huailin Ruan. "Array Diagnosis and DOA Estimation for Coprime Array under Sensor Failures." Sensors 20, no. 9 (May 11, 2020): 2735. http://dx.doi.org/10.3390/s20092735.

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A coprime array of N sensors can achieve O ( N 2 ) degrees of freedom (DOFs) by possessing a uniform linear array segment of size O ( N 2 ) in the difference coarray. However, the structure of difference coarray is sensitive to sensor failures. Once the sensor fails, the impact of failure sensors on the coarray structure may decrease the DOFs and cause direction finding failure. Therefore, the direction of arrival (DOA) estimation of coprime arrays with sensor failures is a significant but challenging topic for investigation. Driven by the need for remedial measures, an efficient detection strategy is developed to diagnose the coprime array. Furthermore, based on the difference coarray, we divide the sensor failures into two scenarios. For redundant sensor failure scenarios, the structure of difference coarray remains unchanged, and the coarray MUSIC (CO-MUSIC) algorithm is applied for DOA estimation. For non-redundant sensor failure scenarios, the consecutive lags of the difference coarray will contain holes, which hinder the application of CO-MUSIC. We employ Singular Value Thresholding (SVT) algorithm to fill the holes with covariance matrix reconstruction. Specifically, the covariance matrix is reconstructed into a matrix with zero elements, and the SVT algorithm is employed to perform matrix completion, thereby filling the holes. Finally, we employ root-MUSIC for DOA estimation. Simulation results verify the effectiveness of the proposed methods.
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Spoorthi, K., Saha Snehanshu, and Mathur Archana. "Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks." Cybernetics and Information Technologies 16, no. 3 (September 1, 2016): 137–53. http://dx.doi.org/10.1515/cait-2016-0039.

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Abstract Exertion of wireless sensor networks has been increasing in recent years, and it imprints in almost all the technologies such as machine industry, medical, military and civil applications. Due to rapid growth in electronic fabrication technology, low cost, efficient, multifunctional and accurate sensors can be produced and thus engineers tend to incorporate many sensors in the area of deployment. As the number of sensors in the field increases, the probability of failure committed by these sensors also increases. Hence, efficient algorithms to detect and recover the failure of sensors are paramount. The current work concentrates mainly on mechanisms to detect sensor node failures on the basis of the delay incurred in propagation and also the energy associated with sensors in the field of deployment. The simulation shows that the algorithm plays in the best possible way to detect the failure in sensors. Finally, the Boolean sensing model is considered to calculate the network coverage of the wireless sensor network for various numbers of nodes in the network.
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Chen, Lei, Lijun Wei, Yu Wang, Junshuo Wang, and Wenlong Li. "Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors." Sensors 22, no. 6 (March 9, 2022): 2106. http://dx.doi.org/10.3390/s22062106.

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Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.
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Luo, Junhai, and Tao Li. "Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/202950.

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We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensors which may or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the “bathtub curve”), which can be divided into the three following regions: initial failure period, random failure period, and wear-out failure period. Considering the possibility of the failed sensors which still work but in a bad situation, it is unreasonable to trust the data from these sensors. Based on the above situation, we bring in new characteristics to failed sensors. Each sensor quantizes its local observation into one bit of information which is sent to the FC for overall fusion because of power, communication, and bandwidth constraints. Under this sensor failure model, the Extension Log-likelihood Ratio Test (ELRT) rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures.
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Ferreira, Pedro M. G. "Tracking with sensor failures." Automatica 38, no. 9 (September 2002): 1621–23. http://dx.doi.org/10.1016/s0005-1098(02)00070-5.

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Dissertations / Theses on the topic "Sensor failures"

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Paturu, Raghunatha Rao Nityananda Suresh. "Path Planning under Failures in Wireless Sensor Networks." Thesis, North Dakota State University, 2013. http://hdl.handle.net/10365/22971.

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This paper explores how an all pair shortest path can be obtained in a wireless sensor network when sensors fail. Sensors are randomly deployed in a predefined geographical area, simulating the deployment of sensors from an airplane, and finding shortest path between all the sensors deployed based on distance. A major problem to address in wireless sensor networks is the impact of sensor failures on existing shortest paths in the network. An application is developed to simulate a network and find shortest paths affected by a sensor failure and find alternative shortest path. When a sensor fails, all the shortest paths and all the remaining sensors in the network are checked to see if the sensor failure has any impact on the network. Alternative shortest path is calculated for those paths affected by sensor failures.
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Thimmapuram, Aravind. "Distributed recovery of actor failures in wireless sensor and actor networks /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1559859581&sid=7&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Philipps, Joseph Caleb. "Sensor characterization for long-term remote monitoring of bridge piers." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4907.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 2, 2008) Includes bibliographical references.
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Vemulapalli, Shanthi. "Mobility-based route recovery from multiple node failures in movable sensor networks /." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1967797561&sid=5&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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VEMULAPALLI, SHANTHI. "MOBILITY-BASED ROUTE RECOVERY FROM MULTIPLE NODE FAILURES IN MOVABLE SENSOR NETWORKS." OpenSIUC, 2009. https://opensiuc.lib.siu.edu/theses/80.

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In wireless sensor networks (WSNs), maintaining connectivity with the sink node is a crucial issue to collect data from sensors without any interruption. While sensors are typically deployed in abundance to tolerate possible node failures, a number of such failures within the same region simultaneously may result in losing the connectivity with the sink node which eventually reduces the quality and efficiency of the network operation. Given that WSNs are deployed in inhospitable environments, such multiple node failures are very likely due to storms, volcano eruptions, floods, etc. To recover from these multiple node failures, in this thesis, we first present a local partition detection algorithm which makes the sensors aware of the partitioning in the network. We then utilize this information to recover the paths by exploiting sensor mobility. The idea is to locate the failed nodes by keeping complete routing information from each sensor to the sink node and move some of the sensors to such locations to re-establish the routes with the sink node. When performing the recovery, we make sure that the least number of nodes will be moving so that total movement distance can be minimized to improve the lifetime of the WSN. Our proposed approach depends only on the local information to not only minimize the messaging overhead on the sensors but also to ensure the scalability when large-scale Failures and larger networks are considered. The effectiveness of the proposed route recovery approach is validated through simulation experiments.
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Kaur, Amardeep. "Vehicle positioning using image processing." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Kaur_09007dcc80665391.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2009) Includes bibliographical references (p. 72-74).
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Zhang, Guangfan. "Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6846.

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Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.
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Rusek, Bartosz [Verfasser]. "Digital Modeling and Simulations of High Voltage Circuit Breaker Failures for Optimization of Sensor Technique / Bartosz Rusek." Aachen : Shaker, 2007. http://d-nb.info/1166512703/34.

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An, Younghwan. "A design of fault tolerant flight control systems for sensor and actuator failures using on-line learning neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 1998.

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Thesis (Ph. D.)--West Virginia University, 1998.
Title from document title page. Document formatted into pages; contains xix, 179 p. : ill. Includes abstract. Includes bibliographical references (p. 173-178).
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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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Books on the topic "Sensor failures"

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Merrill, Walter C. Advanced detection, isolation, and accommodation of sensor failures - real-time evaluation. Cleveland, Ohio: Lewis Research Center, 1987.

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Merrill, Walter C. Sensor failure detection for jet engines. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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Johnson, M. L. Ampoule failure sensor time response testing: Experiment 1. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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L, Johnson M. Ampoule failure sensor time response testing: Experiments 2 and 3. Marshall Space Flight Center, Ala: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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A, Watring D., and George C. Marshall Space Flight Center., eds. Ampoule failure sensor time response testing: Experiments 2 and 3. Marshall Space Flight Center, Ala: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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Arimori, Takashi. Ginkō no hanzai: "Jūsen" no karakuri to yakuza kanpanī. Tōkyō: Nesuko, 1996.

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Bowles, Tiffany. Analytical derivation and verification of zero-gyro control for the IUE satellite. Greenbelt, MD: National Aeronautics and Space Administration, Goddard Space Flight Center, 1989.

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L, Weiss Jerold, United States. National Aeronautics and Space Administration, Alphatech Inc, and Lewis Research Center, eds. Robust detection/isolation/accommodation for sensor failures. [Washington, D.C.]: National Aeronautics and Space Administration, 1985.

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Robust detection, isolation, and accommodation for sensor failures. Palo Alto, Calif: Systems Control Technology, Inc., 1986.

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C, DeLaat John, Bruton William M, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Office., eds. Advanced detection, isolation, and accomodation of sensor failures, real-time evaluation. [Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Office, 1987.

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Book chapters on the topic "Sensor failures"

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Marinho, Euler Horta, João P. Diniz, Fischer Ferreira, and Eduardo Figueiredo. "Evaluating Sensor Interaction Failures in Mobile Applications." In Communications in Computer and Information Science, 49–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85347-1_5.

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Patil, Sheetal, and V. Ramgopal Rao. "Microcantilever-Based Nano-Electro-Mechanical Sensor Systems: Characterization, Instrumentation, and Applications." In Materials and Failures in MEMS and NEMS, 325–60. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119083887.ch11.

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Wang, Yue, and Jun Wang. "Research on Data Storage Scheme Under Sink Failures in Wireless Sensor Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 27–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72998-5_4.

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DallaLibera, Fabio, Shuhei Ikemoto, Takashi Minato, Hiroshi Ishiguro, Emanuele Menegatti, and Enrico Pagello. "Biologically Inspired Mobile Robot Control Robust to Hardware Failures and Sensor Noise." In RoboCup 2010: Robot Soccer World Cup XIV, 218–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20217-9_19.

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Yang, Heejung, and Chin-Wan Chung. "An Effective and Efficient Method for Handling Transmission Failures in Sensor Networks." In Database Systems for Advanced Applications, 92–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00887-0_9.

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Lim, Chansook, Stephan Bohacek, João P. Hespanha, and Katia Obraczka. "On the Effectiveness of Proactive Path-Diversity Based Routing for Robustness to Path Failures." In NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet, 574–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79549-0_50.

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Tran, Cuong Dinh, Pavel Brandstetter, Sang Dang Ho, Thinh Cong Tran, Minh Chau Huu Nguyen, Huy Xuan Phan, and Bach Hoang Dinh. "Improving Fault Tolerant Control to the One Current Sensor Failures for Induction Motor Drives." In Lecture Notes in Electrical Engineering, 789–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14907-9_76.

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Wang, Jing-huan. "Fault Tolerance Controller Is Designed for Linear Continuous Large-Scale Systems with Sensor Failures." In Advances in Intelligent and Soft Computing, 761–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_83.

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Sun, Chen, Yan Lin, and Lin Li. "Adaptive State Feedback Fault-Tolerant Tracking Control for Uncertain Nonlinear Systems with Sensor Failures." In Lecture Notes in Electrical Engineering, 284–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_30.

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Filburn, Thomas. "Flight System Sensor Failure." In Commercial Aviation in the Jet Era and the Systems that Make it Possible, 169–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20111-1_14.

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Conference papers on the topic "Sensor failures"

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Lim, Tiong Hoo, Iain Bate, and Jon Timmis. "Multi-modal routing to tolerate failures." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146513.

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Berfield, Andreea, Panos K. Chrysanthis, and Alexandros Labrinidis. "Efficient handling of sensor failures." In the 3rd workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1315903.1315911.

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Fu, Xiuwen, and Wenfeng Li. "Cascading failures of wireless sensor networks." In 2014 IEEE 11th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/icnsc.2014.6819699.

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Bullock, T. E., S. Sangsuk-iam, R. Pietsch, and E. J. Boudreau. "Sensor Fusion Applied To System Performance Under Sensor Failures." In 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, edited by Charles B. Weaver. SPIE, 1988. http://dx.doi.org/10.1117/12.946658.

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"RAID’ing Wireless Sensor Networks - Data Recovery for Node Failures." In International Conference on Sensor Networks. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004672702980308.

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Winkler, Nicolas P., Patrick P. Neumann, Erik Schaffernicht, and Achim J. Lilienthal. "Using Redundancy in a Sensor Network to Compensate Sensor Failures." In 2021 IEEE Sensors. IEEE, 2021. http://dx.doi.org/10.1109/sensors47087.2021.9639479.

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Bopardikar, Shaunak D. "Sensor Selection in Presence of Random Failures." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814624.

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Tao, Gang, and Jason Burkholder. "Adaptive Detection of Sensor Uncertainties and Failures." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5889.

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Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. "Learning to classify with possible sensor failures." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854029.

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Pignaton de Freitas, Edison, Tales Heimfarth, Ivayr Farah Netto, Carlos Eduardo Pereira, Armando Morado Ferreira, Flavio Rech Wagner, and Tony Larsson. "Handling Failures of Static Sensor Nodes in Wireless Sensor Network by Use of Mobile Sensors." In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2011. http://dx.doi.org/10.1109/waina.2011.104.

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Reports on the topic "Sensor failures"

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Peynot, Thierry. Sensor Data Integrity and Mitigation of Perceptual Failures. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada536583.

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Chew, Paul, and Keith Marzullo. Masking Failures of Multidimensional Sensors. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada235580.

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Marzullo, Keith. Tolerating Failures of Continuous-Valued Sensors. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada227140.

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Chew, Paul, and Keith Marzullo. Masking Failures of Multidimensional Sensors (Extended Abstract). Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada231471.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Abstract:
Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Walraven, Jeremy Allen, Michael Sean Baker, Rebecca C. Clemens, John Anthony Mitchell, Matthew Robert Brake, David S. Epp, and Jonathan W. Wittwer. The Sandia MEMS Passive Shock Sensor : FY08 failure analysis activities. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/939847.

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Lowder, Kelly S., Daniel Briand, and Donald Shirah. Updating time-to-failure distributions based on field observations and sensor data. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/896865.

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Stewart, Paul. Sensor Fusion, Prognostics, Diagnostics and Failure Mode Control for Complex Aerospace Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada535693.

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Udd, Eric, Mike Winz, Stephen Kreger, and Dirk Heider. Failure Mechanisms of Fiber Optic Sensors Placed in Composite Materials. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada444111.

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Day, Allan E. Disaster-Proofing Senior Leadership: Preventing Technological Failure in Future Nano-War. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada539889.

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