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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Gugan, K., and V. Saravanan. "Diminishing Connectivity Failures by Auto-Reconfiguration in WSN." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (December 1, 2017): 662. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp662-666.
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<p>The Wireless Sensor Network is one of the most significant purposes behind the accomplishment of long range wireless communication. Frequent connectivity failures are occurred in the sensor-organised network due to obstruction, snags, message drop because of node energy depletion; obstacle and so forth. The total communication gets collapsed if there any lessening in the nature of correspondence or quality between the sensor nodes or from the sensor nodes to the sink nodes and this prompts to connection failures. To overcome the frequent connectivity failures we propose Diminishing Connectivity Failures by Auto-Reconfiguration in WSN (DCFA). This scheme provides steadfast routes to reduce the connectivity failure and improve the network performance. </p>
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Fentaye, Amare, Valentina Zaccaria, and Konstantinos Kyprianidis. "Sensor Fault/Failure Correction and Missing Sensor Replacement for Enhanced Real-time Gas Turbine Diagnostics." PHM Society European Conference 7, no. 1 (June 29, 2022): 136–45. http://dx.doi.org/10.36001/phme.2022.v7i1.3315.
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Gas turbine sensors are prone to bias and drift. They may also become unavailable due to maintenance activities or failure through time. It is, therefore, important to correct faulty signal or replace missing sensors with estimated values for improved diagnostic solutions. Coping with a small number of sensors is the most difficult to achieve since this often leads to underdetermined and indistinguishable diagnostic problems in multiple fault scenarios. On the other hand, installing additional sensors has been a controversial issue from cost and weight perspectives. Gas path locations with difficult conditions to install sensors is also among other sensor installation related challenges. This paper proposes a sensor fault/failure correction and missing sensor replacement method. Auto-regressive integrated moving average models are employed to correct measurements from faulty and failed sensors. To replace additional sensors needed for further diagnostic accuracy improvements, neural network models are devised. The performance of the developed approach is demonstrated by applying to a three-shaft turbofan engine. Test results verify that the method proposed can well-recover measurements from faulty/failed sensors, no matter with small or major failures. It can also compensate key missing temperature and pressure measurements on the gas path based on the data from other available sensors.
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DASH, DINESH, AROBINDA GUPTA, and ARIJIT BISHNU. "DYNAMIC MAINTENANCE OF SUPPORT COVERAGE IN SENSOR NETWORKS." Parallel Processing Letters 20, no. 02 (June 2010): 155–72. http://dx.doi.org/10.1142/s0129626410000120.
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Ensuring different types of coverage is an important problem in many wireless sensor applications. In this paper, we address the problem of maintaining support coverage in the presence of sensor failures. Given a placement of n sensors in an area A, and any two points i and f in A, the support value of any path between i and f is the maximum distance of any point on the path from its closest sensor. The path with the minimum support value is called the maximal support path. The support value of a path may increase if a sensor fails. Given a maximal support path with a support value ψ, we first present two centralized approximation algorithms that, on failure of a single sensor, compute a new path with a support value close to ψ by moving exactly one nearby sensor. The first algorithm assumes that the sensors are allowed to move in any direction, and the second one assumes that the sensors are constrained to move in any of the four directions east, west, north, and south. Both the support value for the new path computed and the movement necessary are shown to be within a constant-factor of the initial support value. We then show that even in case of multiple sensor failures, a new path with a bounded support value can be computed. Detailed simulation results are provided to show that the algorithms result in significant improvement in many cases in practice, and the improvements obtained are significantly better than the worst case bounds given by the analysis. We also discuss distributed implementations of the algorithms.
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Tang, Junqing, Li Wan, Timea Nochta, Jennifer Schooling, and Tianren Yang. "Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns." ISPRS International Journal of Geo-Information 9, no. 4 (April 17, 2020): 247. http://dx.doi.org/10.3390/ijgi9040247.
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Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.
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Kwon, Do-Soo, Chungkuk Jin, MooHyun Kim, and Weoncheol Koo. "Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network." Applied Sciences 10, no. 18 (September 21, 2020): 6591. http://dx.doi.org/10.3390/app10186591.
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This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under various random wave excitations and failure/intact scenarios. Then, the big-data is collected at various locations of numerical motion sensors along the SFT to be used for the present DNN algorithm. In the input layer, tunnel motion-sensor signals and wave conditions are inputted while the output layer provides the probabilities of 21 failure scenarios. In the optimization stage, the numbers of hidden layers, neurons of each layer, and epochs for reliable performance are selected. Several activation functions and optimizers are also tested for the present DNN model, and Sigmoid function and Adamax are respectively adopted to enhance the classification accuracy. Moreover, a systematic sensitivity test with respect to the numbers and arrangements of sensors is performed to find the appropriate sensor combination to achieve target prediction accuracy. The technique of confusion matrix is used to represent the accuracy of the DNN algorithms for various cases, and the classification accuracy as high as 98.1% is obtained with seven sensors. The results of this study demonstrate that the DNN model can effectively monitor the mooring failures of SFTs utilizing real-time sensor signals.
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Xu, R., and C. Kwan. "Robust Isolation Of Sensor Failures." Asian Journal of Control 5, no. 1 (October 22, 2008): 12–23. http://dx.doi.org/10.1111/j.1934-6093.2003.tb00093.x.
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Dmitrzak, Marta, Pawel Kalinowski, Piotr Jasinski, and Grzegorz Jasinski. "Identification of defected sensors in an array of amperometric gas sensors." Sensor Review 42, no. 2 (December 17, 2021): 195–203. http://dx.doi.org/10.1108/sr-10-2021-0348.
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Purpose Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty. Design/methodology/approach In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method. Findings The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response. Originality/value This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.
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Qi, Ping, Fucheng Wang, and Shu Hong. "A Novel Trust Model Based on Node Recovery Technique for WSN." Security and Communication Networks 2019 (September 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/2545129.
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With the rapid development of sensor technology and wireless network technology, wireless sensor network (WSN) has been widely applied in many resource-constrained environments and application scenarios. As there are a large number of sensor nodes in WSN, node failures are inevitable and have a significant impact on task execution. In this paper, considering the vulnerability, unreliability, and dynamic characteristics of sensor nodes, node failures are classified into two categories including unrecoverable failures and recoverable failures. Then, the traditional description of the interaction results is extended to the trinomial distribution. According to the Bayesian cognitive model, the global trust degree is aggregated by both direct and indirect interaction records, and a novel trust model based on node recovery technique for WSNs is proposed to reduce the probability of failure for task execution. Simulation results show that compared with existing trust models, our proposed TMBNRT (trust model based on node recovery technique) algorithm can effectively meet the security and the reliability requirements of WSN.
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Lin, Yi-Bing, Yun-Wei Lin, Jiun-Yi Lin, and Hui-Nien Hung. "SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming." Sensors 19, no. 21 (November 4, 2019): 4788. http://dx.doi.org/10.3390/s19214788.
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In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%.
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Feng, Jiangtao, Jiaqi Geng, Hangyu She, Tao Zhang, Bo Chi, and Jian Pu. "Thermal Stress Simulation and Structure Failure Analyses of Nitrogen–Oxygen Sensors under a Gradual Temperature Field." Energies 15, no. 8 (April 11, 2022): 2799. http://dx.doi.org/10.3390/en15082799.
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Nitrogen–oxygen sensors are pivotal for NOX emission detection, and they have been designed as key components in vehicles’ exhaust systems. However, severe thermal stress concentrations during thermal cycling in the sensors create knotty structural damage issues, which are inevitable during the frequent start–stop events of the vehicles. Herein, to illustrate the effect of thermal concentration on a sensor’s structure, we simulated the temperature and stress field of a sensor through finite element analysis. The failure modes of the sensor based on the multilayer structure model were analyzed. Our simulation indicated that the thermal deformation and stress of the sensors increased significantly when the heating temperature in the sensors increased from 200 to 800 °C. High stress regions were located at the joint between the layers and the right angle of the air chamber. These results are consistent with the sensor failure locations that were observed by SEM, and the sensor’s failures mainly manifested in the form of cracks and delamination. The results suggest that both the multilayer interfaces and the shape of the air chamber could be optimized to reduce the thermal stress concentrations of the sensors. It is beneficial to improve the reliability of the sensor under thermal cycling operation.
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Aqil, Muhammad, and Jin Hur. "Multiple Sensor Fault Detection Algorithm for Fault Tolerant Control of BLDC Motor." Electronics 10, no. 9 (April 27, 2021): 1038. http://dx.doi.org/10.3390/electronics10091038.
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A direct redundancy-based fault tolerant control system (FTCS) is proposed for the operation of a brushless DC (BLDC) motor in the case of multiple sensor failures. The presented method expands on the previously published work that dealt with the failure of a single Hall-effect sensor. In this paper, a novel algorithm is developed along with the new experimental scheme and the FTCS can deal with the failure of up to two Hall-effect sensors. The fault tolerant control (FTC) algorithm is based on three conditions and is designed to deal with any scenario of the sensor faults. Simulation was performed and presented experimental results show effectiveness and validation of the method. The method can be implemented with ease, is fast and does not require high end processors.
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Ali, Nazakat, and Jang-Eui Hong. "Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base." Computers 7, no. 4 (December 6, 2018): 68. http://dx.doi.org/10.3390/computers7040068.
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Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.
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Davis, R. N., M. E. Polites, and L. C. Trevino. "Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 219, no. 6 (June 1, 2005): 483–95. http://dx.doi.org/10.1243/095441005x30270.
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This article describes a novel scheme for autonomous component health management (ACHM) with failed actuator detection and identification and failed sensor detection, identification, and avoidance. This new scheme has features that are very superior to those with triple redundant sensing and voting, yet requires fewer sensors; it can be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided in this article. Simulation results for its application to a single-axis spacecraft attitude control system with a third-order plant and dual-redundant measurements of the system states are presented. The ACHM scheme fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is autonomous; is adaptive; works in real time; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures, reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary estimates of the system states, detects and identifies failed sensors and actuators, and avoids failed sensors in generating preliminary estimates of the system states with a fixed-gain Kalman filter that provides model-based state estimates, which are utilized in the failure detection logic, and generates optimal estimates for the system states. The simulation results show that ACHM can successfully detect, identify, and avoid sensor failures that are single or multiple; persistent and intermittent; and hard-over, soft, and zero-output types. It is now ready to be tested on a computer model of an actual system.
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Qi, Xiaomei, Chengjin Zhang, and Jason Gu. "Robust Fault-Tolerant Control for Uncertain Networked Control Systems with State-Delay and Random Data Packet Dropout." Journal of Control Science and Engineering 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/734758.
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A robust fault-tolerant controller design problem for networked control system (NCS) with random packet dropout in both sensor-to-controller link and controller-to-actuator link is investigated. A novel stochastic NCS model with state-delay, model uncertainty, disturbance, probabilistic sensor failure, and actuator failure is proposed. The random packet dropout, sensor failures, and actuator failures are characterized by a binary random variable. The sufficient condition for asymptotical mean-square stability of NCS is derived and the closed-loop NCS satisfiesH∞performance constraints caused by the random packet dropout and disturbance. The fault-tolerant controller is designed by solving a linear matrix inequality. A numerical example is presented to illustrate the effectiveness of the proposed method.
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Chen, Lingyu, Dapeng Fan, Jieji Zheng, and Xin Xie. "Functional Safety Analysis and Design of Sensors in Robot Joint Drive System." Machines 10, no. 5 (May 10, 2022): 360. http://dx.doi.org/10.3390/machines10050360.
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The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the functional safety issues of the robot sensors themselves. In view of this, based on IEC61508, a functional safety analysis and design method for sensors of RJDs is proposed in this paper. Firstly, the hazard analysis and risk assessment clarified the goals that the safety protection function of the RJD’s sensor should achieve. Then, by establishing the motor drive model and transmission model, a model-based sensor fault diagnosis and isolation strategy is proposed. Considering the fault-tolerant operation of system, a fail-operational hardware architecture of the safety-related system is designed. Markov analysis shows that the safety integrity level (SIL) of safety-related systems can reach SIL3. Finally, experiments are designed to validate the proposed fault diagnosis and fault tolerance strategy. The results show that the safety-related system can effectively locate sensor failures, realize fault-tolerant control when a single sensor fails and perform safe torque off (STO) protection when multiple sensors fail.
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Zadorozhniy, A. A., and N. D. Aleksanin. "The Effect of Icing on the Output Parameters of the Airflow-Angle Sensor in Case of Failure of the Electric Heating System." Intellekt. Sist. Proizv. 19, no. 4 (2021): 69–75. http://dx.doi.org/10.22213/2410-9304-2021-4-69-75.
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At present, with the intensive operation of aircrafts in extremely harsh air conditions, their all-weather use, the work related to improving the flight safety is especially relevant. One of the directions of work in this topic is the study of the types of parametric failures of onboard equipment associated with the influence of the external environment in order to assess their impact on interacting systems and develop recommendations for identifying and countering detected failures. The presented paper considers the results of a full-scale experiment to determine the possibility of operation of the aerodynamic angle sensor in artificially formed icing conditions in case of failure of its electric heating system, as well as a description and results of subsequent modeling of the sensor icing process, and an assessment of its effect on the accuracy characteristics of the sensor output signal. The statement of the research problem is formulated as follows: it is necessary to ensure the testing of the sensor and the subsequent processing of the results obtained in order to form a generalized mathematical model of the sensor icing process, as well as to solve the problem of introducing additional errors into the sensor signals, which makes it possible to study the behavior of air signal systems using vane sensors of aerodynamic angles with artificially introduced failures. A full-scale experiment was carried out in an air-cooling tube on the basis of the enterprise JSC "UKBP". Numerical modeling and determination of correction factors were carried out in the software packages Ansys FENSAP ICE and MathCAD. Analysis of the results achieved in the course of modeling confirmed the convergence of the model obtained with the data of real experiments in an air-cooling tube. The data obtained made it possible to confirm the possibility of detecting distorted values for the angle of attack by the parametric quorum control method.
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Busson, F., A. Aïtouche, B. Quid Bouamama, and M. Staroswiecki. "Sensor Failures Detection in Steam Condensers." IFAC Proceedings Volumes 31, no. 10 (June 1998): 317–22. http://dx.doi.org/10.1016/s1474-6670(17)37578-x.
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Takahashi, Masanori. "Simple Adaptive Control Against Sensor Failures." IFAC-PapersOnLine 52, no. 29 (2019): 55–60. http://dx.doi.org/10.1016/j.ifacol.2019.12.621.
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Yang, G. H., J. L. Wang, and Y. C. Soh. "Reliable LQG control with sensor failures." IEE Proceedings - Control Theory and Applications 147, no. 4 (July 1, 2000): 433–39. http://dx.doi.org/10.1049/ip-cta:20000499.
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Nguyen, Tien Xuan, Minh Chau Huu Nguyen, and Cuong Dinh Tran. "Sensor fault diagnosis technique applied to three-phase induction motor drive." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3127–35. http://dx.doi.org/10.11591/eei.v11i6.4253.
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This paper conducts research on sensor fault diagnosis for a three-phase induction motor drive (IMD) in steady-state operation. An improving diagnostic technique based on the integration algorithm of the sinusoidal current signal is proposed for detecting and locating faulty current sensors in the induction motor drive. The IMD integrated a proposed fault detection-isolation (FDI) system is investigated for operating characteristics when sensor failures occur. The faulty sensor needs to be accurately identified and quickly isolated from the control system. Then the estimated signal will be used to replace the fault signal to retain the IMD stability. MATLAB/Simulink software will be applied to simulate the speed-torque characteristics of the IMD system as well as sensor failures occurring during operation. The performance of the proposed method will be evaluated through the accuracy and timeliness in each fault case corresponding to each sensor.
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Peng, Hao, Can Liu, Dandan Zhao, Zhaolong Hu, and Jianmin Han. "Security Evaluation under Different Exchange Strategies Based on Heterogeneous CPS Model in Interdependent Sensor Networks." Sensors 20, no. 21 (October 28, 2020): 6123. http://dx.doi.org/10.3390/s20216123.
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In the real Internet of Everything scenario, many large-scale information systems can be converted into interdependent sensor networks, such as smart grids, smart medical systems, and industrial Internet systems. These complex systems usually have multiple interdependent sensor networks. Small faults or failure behaviors between networks may cause serious cascading failure effects of the entire system. Therefore, in this paper, we will focus on the security of interdependent sensor networks. Firstly, by calculating the size of the largest functional component in the entire network, the impact of random attacks on the security of interdependent sensor networks is analyzed. Secondly, it compares and analyzes the impact of cascading failures between interdependent sensor networks under different switching edge strategies. Finally, the simulation results verify the effect of the security of the system under different strategies, and give a better exchange strategy to enhance the security of the system. In addition, the research work in this article can help design how to further optimize the topology of interdependent sensor networks by reducing the impact of cascading failures.
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Kullu, Omer, and Eyup Cinar. "A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults." Machines 10, no. 11 (November 21, 2022): 1105. http://dx.doi.org/10.3390/machines10111105.
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Condition monitoring is a part of the predictive maintenance approach applied to detect and prevent unexpected equipment failures by monitoring machine conditions. Early detection of equipment failures in industrial systems can greatly reduce scrap and financial losses. Developed sensor data acquisition technologies allow for digitally generating and storing many types of sensor data. Data-driven computational models allow the extraction of information about the machine’s state from acquired sensor data. The outstanding generalization capabilities of deep learning models have enabled them to play a significant role as a data-driven computational fault model in equipment condition monitoring. A challenge of fault detection applications is that single-sensor data can be insufficient in performance to detect equipment anomalies. Furthermore, data in different domains can reveal more prominent features depending on the fault type, but may not always be obvious. To address this issue, this paper proposes a multi-modal sensor fusion-based deep learning model to detect equipment faults by fusing information not only from different sensors but also from different signal domains. The effectiveness of the model’s fault detection capability is shown by utilizing the most commonly encountered equipment types in the industry, such as electric motors. Two different sensor types’ raw time domain and frequency domain data are utilized. The raw data from the vibration and current sensors are transformed into time-frequency images using short-time Fourier transform (STFT). Then, time-frequency images and raw time series data were supplied to the designed deep learning model to detect failures. The results showed that the fusion of multi-modal sensor data using the proposed model can be advantageous in equipment fault detection.
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ElHady, Nancy E., Stephan Jonas, Julien Provost, and Veit Senner. "Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining." Sensors 20, no. 23 (November 26, 2020): 6760. http://dx.doi.org/10.3390/s20236760.
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Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.
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Cartocci, Nicholas, Marcello R. Napolitano, Gabriele Costante, and Mario L. Fravolini. "A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation." Sensors 21, no. 5 (February 26, 2021): 1645. http://dx.doi.org/10.3390/s21051645.
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Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.
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Zhu, Li Ming, Zong Da Zhu, and Yong Gang Yan. "A Class of Uncertain Networked Control Systems with Robust H∞ Fault-Tolerant." Advanced Materials Research 912-914 (April 2014): 1065–68. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1065.
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T For the networked control system (NCS), the considered system has actuator and sensor failures. In considering the impact of the network delay on system performance, establish a new class of uncertain NCS fault model Then use Lyapunov stability theory, fault-tolerant control theory and the static state feedback, the sufficient conditions for closed-loop NCS possessing robust asymptotically stable against actuator and sensor failure are given . And the robust H-inf fault-tolerant controller design method under the sensor and actuator failures is deduced in terms of linear matrix inequalities (LMI). An numerical simulation is provided to show the effectiveness of the proposed conclusion.
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Schmidt, Simon, Jens Oberrath, and Paolo Mercorelli. "A Sensor Fault Detection Scheme as a Functional Safety Feature for DC-DC Converters." Sensors 21, no. 19 (September 29, 2021): 6516. http://dx.doi.org/10.3390/s21196516.
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DC-DC converters are widely used in a large number of power conversion applications. As in many other systems, they are designed to automatically prevent dangerous failures or control them when they arise; this is called functional safety. Therefore, random hardware failures such as sensor faults have to be detected and handled properly. This proper handling means achieving or maintaining a safe state according to ISO 26262. However, to achieve or maintain a safe state, a fault has to be detected first. Sensor faults within DC-DC converters are generally detected with hardware-redundant sensors, despite all their drawbacks. Within this article, this redundancy is addressed using observer-based techniques utilizing Extended Kalman Filters (EKFs). Moreover, the paper proposes a fault detection and isolation scheme to guarantee functional safety. For this, a cross-EKF structure is implemented to work in cross-parallel to the real sensors and to replace the sensors in case of a fault. This ensures the continuity of the service in case of sensor faults. This idea is based on the concept of the virtual sensor which replaces the sensor in case of fault. Moreover, the concept of the virtual sensor is broader. In fact, if a system is observable, the observer offers a better performance than the sensor. In this context, this paper gives a contribution in this area. The effectiveness of this approach is tested with measurements on a buck converter prototype.
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Yang, Wen Xue, Zhe Chen, and Feng Yang. "A Survey of Sensor Technologies for Prognostics and Health Management of Electronic Systems." Applied Mechanics and Materials 602-605 (August 2014): 2229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2229.
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Recently, the field of Prognostics and Health Management (PHM) for electronic products and systems has received increasing attention due to the potentialities to provide early warning of system failures, reduce life cycle costs, and forecast maintenance as needed. This paper introduces the sensors and their sensor technologies. The required attributes of sensors for the development for PHM of electronics are discussed. Finally, their trends in sensor systems are presented.
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Wang, Jun, Zhuangzhuang Du, Xunyang Wang, and Zhengkun He. "Bioinspired Mitigation Scheme for Cascading Failures in Farmland Wireless Sensor Networks." Complexity 2020 (November 4, 2020): 1–17. http://dx.doi.org/10.1155/2020/1065810.
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Existing mitigation strategies on wireless sensor networks (WSNs) against cascading failures cannot appropriately adapt the particular characteristics of farmland WSNs. Spider web provides a new reference for improving network invulnerability. In this study, a bionic network scheme is built based on symmetric analysis of a series of spider-web vibration transmission trials, which include networking methodology, communication rules, and load capacity model. The basic idea of this scheme is to apply the cascading-failure coping mechanism inspired by spider web into the construction and operation of farmland WSNs. We found that the link backup contributed by a topological structure and communication rules had positive effects on suppressing the spread of cascading failures. The study showed that the damages of cascading failures can be efficiently lowered by regulating the adjustment coefficient of the load capacity model. The difference between the inner-layer node failures and outer-layer node failures for network invulnerability was verified under deliberate attack circumstances. Based on these results, the proposed network scheme can be utilized to enhance the invulnerability performance of farmland WSNs.
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Salehpour-Oskouei, Farzin, and Mohammad Pourgol-Mohammad. "Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 1 (November 30, 2017): 65–81. http://dx.doi.org/10.1177/1748006x17742766.
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Validity of sensor data is a challenge in system monitoring due to stochastic nature of failure occurrence. The quantity and location of sensors affect the system health information, while sensor malfunction causes misleading results about the system condition. Occurred economic losses are irrecoverable expenses in respect to the monitoring system as well as the system failure. In this study, a dual index approach is proposed for the determination of sensor placement scenarios based on two criteria: (1) uncertainty of sensor information and (2) risk of sensor failure. With about variation of environmental factors conditions (e.g. temperature) and their failure threshold characterization, system failure model is developed and analyzed by a proposed efficient Monte Carlo simulation. Statistical variance of sensor information about estimating of system state as the quantitative uncertainty measure of choice in this research is estimated according to the information value that each possible sensor placement scenario provides through sensor information. In the next phase, risk index is determined based on sensor malfunction and corresponding quantifiable losses through both failure costs and maintenance expenditure. All feasible combinations of sensor failures are considered in the risk model. Finally, a combinatorial criterion is determined through information entropy calculation, which considers both indexes proposed above simultaneously. Sensor placement scenarios are comparatively prioritized based on this criterion. Accordingly, the technical directions are provided for suitability of the criteria for prioritizing sensor arrangement in various systems with different reliability-based characteristics. As a case study, determination of sensor placement is demonstrated on a typical steam turbine. According to the low variation of both information uncertainty and risk indexes, it is concluded that the combinatorial index is the proper criterion for sensor placement determination in health monitoring process of the steam turbine.
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Cho, Byeong-Moon, Sangjun Kim, Kyoung-Dae Kim, and Kyung-Joon Park. "A Controller Switching Mechanism for Resilient Wireless Sensor–Actuator Networks." Applied Sciences 12, no. 4 (February 10, 2022): 1841. http://dx.doi.org/10.3390/app12041841.
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Controller failures can result in unsafe physical plant operations and deteriorated performance in industrial cyber–physical systems. In this paper, we present a controller switching mechanism over wireless sensor–actuator networks to enhance the resiliency of control systems against problems and potential physical failures. The proposed mechanism detects controller failures and quickly switches to the backup controller to ensure the stability of the control system in case the primary controller fails. To show the efficacy of our proposed method, we conduct a performance evaluation using a hardware-in-the-loop testbed that considers both the actual wireless network protocol and the simulated physical system. Results demonstrate that the proposed scheme recovers quickly by switching to a backup controller in the case of controller failure.
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Holst, Christoph-Alexander, and Volker Lohweg. "A Redundancy Metric Set within Possibility Theory for Multi-Sensor Systems." Sensors 21, no. 7 (April 3, 2021): 2508. http://dx.doi.org/10.3390/s21072508.
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In intelligent technical multi-sensor systems, information is often at least partly redundant—either by design or inherently due to the dynamic processes of the observed system. If sensors are known to be redundant, (i) information processing can be engineered to be more robust against sensor failures, (ii) failures themselves can be detected more easily, and (iii) computational costs can be reduced. This contribution proposes a metric which quantifies the degree of redundancy between sensors. It is set within the possibility theory. Information coming from sensors in technical and cyber–physical systems are often imprecise, incomplete, biased, or affected by noise. Relations between information of sensors are often only spurious. In short, sensors are not fully reliable. The proposed metric adopts the ability of possibility theory to model incompleteness and imprecision exceptionally well. The focus is on avoiding the detection of spurious redundancy. This article defines redundancy in the context of possibilistic information, specifies requirements towards a redundancy metric, details the information processing, and evaluates the metric qualitatively on information coming from three technical datasets.
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Gruosso, Giambattista, Giancarlo Storti Gajani, Fredy Ruiz, Juan Diego Valladolid, and Diego Patino. "A Virtual Sensor for Electric Vehicles’ State of Charge Estimation." Electronics 9, no. 2 (February 6, 2020): 278. http://dx.doi.org/10.3390/electronics9020278.
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The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor.
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Lin, Xiaogong, Heng Li, Kun Liang, Jun Nie, and Juan Li. "Fault-Tolerant Supervisory Control for Dynamic Positioning of Ships." Mathematical Problems in Engineering 2019 (January 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/9134952.
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A fault-tolerant supervisory control method for dynamic positioning of ships with actuator failures and sensor failures is presented in this paper. Unlike the traditional fault detection and control, fault detection and fault-tolerant controller are designed as a unit in this paper through a supervisor. By introducing a nonlinear estimation error and virtual controller, the sensor failures are separated from the actuator failures in the supervisory control system. It guarantees that the detectability property and matching property of the switched system are satisfied. Firstly, a new extended state observer is designed to match the models of different actuator failures. Secondly, by introducing a virtual controller, the detectability property of the switched system is guaranteed. Finally, a nonlinear estimation error operator is used in the designing of switching logic to guarantee stability of the closed-loop system with sensor failures. When sensor failures and actuator failures occur, we show that all the states of the closed-loop system are guaranteed to be bounded. The effectiveness of the fault-tolerant control is verified by simulation experiments.
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Lafi Aljohani, Sarah, and Mohammed J. F. Alenazi. "Evaluation of WSN's Resilience to Challenges in Smart Cities." International Journal of Computer and Communication Engineering 9, no. 4 (2020): 193–206. http://dx.doi.org/10.17706/ijcce.2020.9.4.193-206.
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Smart cities are considered to be one of the most important applications of the IoT notion. Most smart city applications rely fundamentally on ubiquitous sensing, enabled by Wireless Sensor Network (WSN) technologies. These sensor networks are vulnerable to different challenges that cause failures in some parts of the network, which in turn interfere with the availability of network services and weaken the user experience. In this paper, we introduce a graph-theoretic model of wireless sensor networks used in smart cities. Moreover, we present several challenges, such as natural disasters and random failures and evaluate the system's performance in terms of data delivery, end to end delay, and energy consumption. The evaluation results show that fire is the challenge that causes the most damage among the three challenges examined, while random failure has the least effect on network performance. The results also show that the modeled WSN's can cope well with the challenge of random failures.
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Ahmadi, Bahar, and Mehrzad Namvar. "Robust detection and isolation of failures in satellite attitude sensors and gyro." Robotica 30, no. 7 (January 12, 2012): 1157–66. http://dx.doi.org/10.1017/s0263574711001391.
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SUMMARYReliability of a satellite attitude control system depends on accurate detection of failures in its sensors. This paper presents an observer for robust detection and isolation of a class of failures in satellite attitude sensors. The proposed observer uses measurement of a three-axis gyro together with only one attitude sensor, and generates a residual signal which is sensitive to faults and is simultaneously robust against disturbance and noise. A nonlinear model of satellite kinematics is considered for design of the observer. The structure of the observer is in the form of a delayed continuous-time differential equation ensuring its robustness properties. A realistic simulation is provided to illustrate the performance of the proposed observer in the face of the faults occurring in a magnetometer, as the attitude sensor, and also the faults occurring in the gyro.
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Ashok, Pradeepkumar, Ganesh Krishnamoorthy, and Delbert Tesar. "Guidelines for Managing Sensors in Cyber Physical Systems with Multiple Sensors." Journal of Sensors 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/321709.
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Cyber physical systems (CPSs) typically have numerous sensors monitoring the various physical processes involved. Some sensor failures are inevitable and may have catastrophic effects. The relational nature of the diverse measurands can be very useful in detecting faulty sensors, monitoring the health of the system, and reducing false alarms. This paper provides procedures on how one may integrate data from the various sensors, by careful design of a sensor relationship network. Once such a network has been adopted, choices become available in real time for enhancing the reliability, safety, and performance of the overall system.
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Hajiyev, Chingiz. "Integrated Sensor/Actuator FDI Based on RKF Insensitive to Sensor Failures." IFAC Proceedings Volumes 37, no. 6 (June 2004): 765–70. http://dx.doi.org/10.1016/s1474-6670(17)32269-3.
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Burkholder, Jason O., and Gang Tao. "Adaptive Detection of Sensor Uncertainties and Failures." Journal of Guidance, Control, and Dynamics 34, no. 6 (November 2011): 1605–12. http://dx.doi.org/10.2514/1.50285.
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Rizzoni, G., and P. S. Min. "Detection of sensor failures in automotive engines." IEEE Transactions on Vehicular Technology 40, no. 2 (May 1991): 487–500. http://dx.doi.org/10.1109/25.289431.
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Jian Liu, Jian Liang Wang, and Guang-Hong Yang. "Reliable guaranteed variance filtering against sensor failures." IEEE Transactions on Signal Processing 51, no. 5 (May 2003): 1403–11. http://dx.doi.org/10.1109/tsp.2003.810303.
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