scholarly journals A Survey on Distributed Filtering and Fault Detection for Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongli Dong ◽  
Zidong Wang ◽  
Steven X. Ding ◽  
Huijun Gao
Keyword(s):  
Sensor Networks ◽  
Fault Detection ◽  
Large Scale ◽  
Future Research ◽  
Information Structures ◽  
Distributed Information ◽  
Practical Research ◽  
And Mathematics ◽  
Faulty Sensors ◽  
Core Part

In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.

scholarly journals A method for composite fault detection and isolation using overlapping decomposition

2003 ◽  
Vol 14 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Rogério Bastos Quirino ◽  
Celso Pascoli Bottura
Keyword(s):  
Fault Detection ◽  
Dynamic Systems ◽  
Kalman Filters ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Large Scale System ◽  
Single State ◽  
Faulty Sensors ◽  
Overlapping Decomposition

In this article, a method is developed for fault detection in linear, stochastic, interconnected dynamic systems, based on designing a set of partially decentralized Kalman filters for the subsystems resulting from the overlapping decomposition of the overall large scale system. The faulty sensors can be detected and isolated by comparing the estimated values of a single state from partially decoupled Kalman filters. The method is applied to an example system with two sensors.

scholarly journals FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Javad Hassannataj Joloudari ◽  
Mohammad GhasemiGol ◽  
Hamid Saadatfar ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Large Scale ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Classification Methods

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.

scholarly journals A Low-Intensity, Hybrid Design between a “Traditional” and a “Course-Based” Research Experience Yields Positive Outcomes for Science Undergraduate Freshmen and Shows Potential for Large-Scale Application

2018 ◽  
Vol 17 (4) ◽  
pp. ar53
Author(s):  
Thushani Rodrigo-Peiris ◽  
Lin Xiang ◽  
Vincent M. Cassone
Keyword(s):  
Student Perceptions ◽  
Student Outcomes ◽  
Large Scale ◽  
Undergraduate Research ◽  
Future Research ◽  
Research Experience ◽  
Research Experiences ◽  
Stem Majors ◽  
Outcomes Evaluation ◽  
And Mathematics

Based on positive student outcomes, providing research experiences from early undergraduate years is recommended for science, technology, engineering, and mathematics (STEM) majors. To this end, we designed a novel research experience called the “STEMCats Research Experience” (SRE) for a cohort of 119 second-semester freshmen with diverse college preparatory levels, demographics, and academic majors. The SRE targeted student outcomes of enhancing retention in STEM majors, STEM competency development, and STEM academic performance. It was designed as a hybrid of features from apprenticeship-based traditional undergraduate research experience and course-based undergraduate research experience designs, considering five factors: 1) an authentic research experience, 2) a supportive environment, 3) current and future needs for scale, 4) student characteristics and circumstances, and 5) availability and sustainability of institutional resources. Emerging concepts for facilitating and assessing student success and STEM curriculum effectiveness were integrated into the SRE design and outcomes evaluation. Here, we report the efficient and broadly applicable SRE design and, based on the analysis of institutional data and student perceptions, promising student outcomes from its first iteration. Potential improvements for the SRE design and future research directions are discussed.

scholarly journals Contactless Fault Detection of a DC Motor Direction of Rotation Using Its Stray Magnetic Field

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 281
Author(s):  
Michal Matějásko ◽  
Martin Brablc ◽  
Martin Appel ◽  
Robert Grepl
Keyword(s):  
Magnetic Field ◽  
Fault Detection ◽  
Automotive Industry ◽  
Large Scale ◽  
Dc Motor ◽  
Future Research ◽  
Electric Motors ◽  
Stray Magnetic Field ◽  
Non Invasive ◽  
Insight Into

In large-scale manufacturing and assembly applications, especially when trying to automate most steps, implementing quality control as early in the process as possible is the key to prevent expenses later. We deal mainly with the production of DC motor powered fuel pumps, which are commonly used in the automotive industry. The goal of this paper is to present a newly developed technique for non-invasive fault detection of a DC motor’s direction of rotation using a stray magnetic field out of the motor chassis. The results presented in this paper show that it is possible to detect faults even on low-power motors while the algorithm is kept as simple as possible to allow for large-scale deployment on a production line. It also gives new insight into the behavior of the stray magnetic field of electric motors, which may benefit other applications and future research.

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