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sensor failure detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures

IEEE ACCESS 2018年 6卷 56562-56574页

作者： Thiyagarajan, Karthick Kodagoda, Sarath Linh Van Nguyen Ranasinghe, Ravindra Univ Technol Sydney Ctr Autonomous Syst Sydney NSW 2007 Australia

In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.

关键词： Anomalies detection faulty data accommodation forecasting SARIMA model sensor failure detection sensor monitoring sewer corrosion time series modeling wastewater infrastructure

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sensor failure detection, Identification and Accommodation Using Neural Network and Fuzzy Voter 17

Sensor Failure Detection, Identification and Accommodation U...

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17th International Conference on Control, Automation and Systems (ICCAS)

作者： Kwon, Seong-Ho Ahn, Hyo-Sung GIST Sch Mechatron 123 Cheomdan Gwagiro Gwangju 500712 South Korea

ISBN: (纸本)9788993215144

sensor failure detection, identification and accommodation (SFDIA) is a challenging and important problem on unmanned aerial vehicles (UAVs) or other safety critical applications. This paper proposes new SFDIA scheme. The new scheme combines advantages of hardware redundancy and analytical redundancy. Fuzzy voter and neural network (NN)-based sensor estimators are developed in the proposed scheme. The fuzzy voter is based on the fuzzy voting scheme. The new SFDIA scheme has a more reliable redundancy based on the FCC NN. The performance of the SFDIA scheme is validated by experiments of quadrotor with two gyro sensor modules. It can detect the sensors failure, pinpoint what sensor fails, and replace the sensor with the other normal sensor.

关键词： sensor failure detection identification and accommodation (SFDIA) neural networks fuzzy voter fault tolerance quadrotor

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Statistical analysis based online sensor failure detection for continuous glucose monitoring in type I diabetes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 144卷 128-137页

作者： Zhao, Chunhui Fu, Yongji Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Becton Dickinson Technol Res Triangle Pk NC 27709 USA

Wide use of continuous glucose monitoring (CGM) provides time-series sensor data and knowledge about the underlying correlations of glucose concentrations and their progressing dynamics over time direction. From self-monitoring of blood glucose to continuous glucose monitoring, sensor performance is key for successful clinical use. However, sensor failures have not been analyzed and detected before use of CGM data for glucose control, although they are very common problems in real-world cases and may result in unreliable CGM data and thus bad control performance. In the present work, a statistical analysis based failure detection method is proposed as a high-level glucose monitoring tool to identify sensor problems online only using CGM data. By analyzing CGM data, different types of monitoring charts (i.e., fault detection charts) are developed and sensor abnormality is indicated during online monitoring by comparing monitoring profiles against the predefined confidence limits. Comparison is also made between different monitoring charts for two important typical disturbances. Different from the conventional glucose monitoring which worked as a low-level monitoring tool and focused on direct real-time display of CGM readings, the proposed method focuses on detecting undesirable sensor disturbances by analyzing the underlying time-wise glucose autocorrelations. The feasibility of the proposed method to serve as a completely new glucose monitoring engine is successfully assessed using CGM data collected from the Food and Drug Administration (FDA)-accepted University of Virginia/University of Padova (UVa/Padova) metabolic simulator. (C) 2015 Elsevier B.V. All rights reserved.

关键词： sensor failure detection Statistical monitoring chart Multivariate statistical analysis Type I diabetes mellitus (TIDM) Continuous glucose monitoring (CGM)

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Gradual vs. Binary Conflicts in Bayesian Networks Applied to sensor failure detection 18

Gradual vs. Binary Conflicts in Bayesian Networks Applied to...

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18th International Conference on Information Fusion (Fusion)

作者： Krueger, Max Univ Appl Sci Hsch Furtwangen HFU Robert Gerwig Pl 1 D-78120 Furtwangen Germany

ISBN: (纸本)9780982443866

Bayesian Networks have various applications including medical and technical diagnosis, financial scoring, and target behavior/pattern recognition. Bayesian Classification Networks fuse evidence from heterogeneous and homogeneous sources and calculate classification results. For many reasons, pieces of evidence from different sources can carry apparently contradicting information and in these cases are called conflicting evidence. Diagnostic sensor failure-tests for application in Bayesian classification processes may be based on a binary conflict definition or a gradual conflict-level measure. This paper investigates four different failure-tests: (1) binary Conflict Binomial, (2) binary Conflict Ratio, (3) gradual Average Conflict, and (4) gradual Gauss Conflict, with (3) and (4) being new failure-test proposals. In a comparative air surveillance simulation, the detection performance of these diagnostic sensor failure-tests is evaluated and compared.

关键词： Conflicting evidence in Bayesian Networks gradual and binary conflict measures classification identification diagnostic failure-test sensor failure detection detection performance simulation air surveillance systems

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failure detection in sensors via Variational Autoencoders and Image-Based Feature Representation

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sensorS 2025年第7期25卷 2175-2175页

作者： Moreno Haro, Luis Miguel Oliveira-Filho, Adaiton Agard, Bruno Tahan, Antoine Polytech Montreal Dept Math & Ind Engn Lab Intelligence Donnees CIRRELT Montreal PQ H3T 0A3 Canada Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada

This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures.

关键词： sensor failure detection health index variational autoencoder feature representation aeronautical sensors

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Online monitoring and failure detection of capacitive displacement sensor in a Capball device using fractal analysis

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MEASUREMENT 2018年 118卷 23-28页

作者： Rimpault, X. Bitar-Nehme, E. Balazinski, M. Mayer, J. R. R. Polytech Montreal Dept Mech Engn 2900 Edouard Montpetit Blvd Montreal PQ H3T 1J4 Canada

sensor failure may be difficult to detect depending on the nature of the defect. In this study, fractal analysis was used to analyse the raw signal of capacitive displacement sensors in order to detect abnormalities. The fractal analysis of the signals was performed for stationary and dynamic measurements of the target-sensor distance mounted in a Capball device. The fractal results such as fractal dimension and topothesy are found to be effective estimators of the signal characteristics. A sensor failure index, based on a combination of fractal parameters, is proposed herein and is found efficient to detect sensor defects.

关键词： sensor failure detection Capacitive displacement sensor Online monitoring Fractal analysis Machine error

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A Tool-Based Methodology For Long-Term Activity Monitoring 20

A Tool-Based Methodology For Long-Term Activity Monitoring

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13th International Conference on Pervasive Technologies Related to Assistive Environments

作者： Belloum, Rafik Consel, Charles Volanschi, Nic Inria Bordeaux Talence France Bordeaux INP Talence France

ISBN: (纸本)9781450377737

In recent years, remarkable progress has been reported in the field of activity monitoring. However, despite significant breakthroughs in recognizing activities from sensor data, there is still a great deal more to accomplish, especially compared to fields pursuing related goals, such as computer vision and speech recognition. A key factor to move activity monitoring forward, is to enable researchers to build on each other's work more systematically via reproducible research. Besides providing sensor data, reproducibility in activity monitoring requires all aspects of a result to be available to the research community, including collection, processing and interpretation of measurements. This paper presents a tool-based methodology, dedicated to monitor the activities of daily living of older adults, that supports reproducible research. This methodology covers the key steps to defining a monitoring process of these activities, from sensor measurements to actionable activity information. These steps are uniformly described with concise and high-level rules. Additionally, to allow caregivers to monitor older adults' functional decline and to determine what assisting support is needed, our methodology includes a visualization tool, dedicated to handling user activities longitudinally. The proposed approach is validated by a set of rules dedicated to monitor activities of community-dwelling, older adults in their sensor-equipped homes. A preliminary study1 has been conducted to evaluate the intra- and inter-participant consistency of the results produced by our methodology, using longitudinal datasets, collected over several months. Using Signal detection Theory, it has shown that our monitoring rules mostly produced the same interpretations as an expert in activity analysis, who manually analyzed the sensor datasets.

关键词： Older adults smart home assistive computing activity recognition sensor failure detection visualization reproducibility

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Health Electronic Data Sheet (HEDS) for Enhanced Transducer Monitoring, Reliability and Safe Operation

Health Electronic Data Sheet (HEDS) for Enhanced Transducer ...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Discovering New Horizons in Instrumentation and Measurement

作者： Maldonado, Francisco J. Selmic, Rastko R. Figueroa, Fernando Amer GNC Corp Simi Valley CA 93065 USA Louisiana Tech Univ Dept Elect Engn Ruston LA USA NASA John C Stennis Space Ctr Engn & Test Directorate Technol Dev Stennis Space Ctr MS USA

ISBN: (纸本)9781538622223

Due to the continuous growth and ubiquity of sensor networks and technologies related to Internet of Things (IoT) as well as the ever increasing need to enhance the safe operation of critical systems (e.g. avionics, aerospace systems, test technologies, and autonomous systems, just to mention a few) there is a demand for standardized health monitoring practices as well as real-time and on-line sensor network diagnostics algorithms. This need has been recognized by important technological entities such as the National Institute of Standards and Technology (NIST), which has led the standardization efforts. To address technology gaps, this paper presents a Health Electronic Data Sheet (HEDS) design that aims to provide a standardized sensor self-diagnostic framework in smart sensors. This paper also covers: (1) baseline sensor failure detection by data centric and (briefly) system centric algorithms;(2) parameter identification that enables algorithm instantiation, execution, and integration within the HEDS;(3) HEDS definition within the IEEE 1451.0 standard (Dot0);and (4) a current application to support safe operation in NASA test facilities.

关键词： sensor failure detection sensor validation TEDS HEDS smart sensor health monitoring standardization 1451.0

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sensor failure detection Using Interaction Matrix Formulation

Sensor Failure Detection Using Interaction Matrix Formulatio...

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IUTAM Symposium on Multi-Functional Material Structures and Systems

作者： Li, Zhiling Nagarajaiah, Satish Koh, B. H. Rice Univ Dept Civil & Environm Engn Houston TX 77005 USA

ISBN: (纸本)9789048137701

A novel sensor failure detection method is developed in this paper. sensor failure considered in this paper can be any type of measurement error that is different from the true structural response. The sensors are divided into two groups, sensors that correctly measure the structural responses, are termed as reference sensors, and sensors that may fail to correctly measure the structural responses, are termed as uncertain sensors henceforth. A sensor error function, one for each uncertain sensor, is formulated to detect the corresponding uncertain sensor failure in real-time, using the measurements from reference sensors and the uncertain sensor being monitored. The sensor error function is derived using the indirect and direct approaches. In the indirect approach, the error function is obtained from the state space model in combination with the inverse model and interaction matrix formulation. The input term is eliminated from the error function by applying inverse model and the interaction matrix is applied to eliminate the state and all uncertain sensor measurement terms excepted the examined uncertain sensor from the error function. In the direct approach, the coefficients of the error function can be directly calculated from the healthy measurement data from the examined uncertain sensor and all reference sensors without having to know the state-space model of the system. Thus the need to know the state-space model of the plant can be bypassed. The sensor failure detection formulations are investigated numerically using a four degree-of-freedom spring-mass-damper system and experimentally using a 4 m long NASA 8-bay truss structure. It is shown by means of numerical and experimental results that the developed sensor failure formulations correctly detect the instants of sensor failure and can be implemented in real structural systems for sensor failure detection.

关键词： Experimental validation Input error function Interaction matrix formulation Inverse model Mathematical formulation sensor failure detection State space systems

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Ecological Interface Design: sensor failure Diagnosis in Air Traffic Control

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IFAC-PapersOnLine 2016年第19期49卷 307-312页

作者： Bijsterbosch, V.A. Borst, C. Mulder, M. van Paassen, M.M. Control & Simulation Faculty of Aerospace Engineering Delft University of Technology DelftHS2629 Netherlands

Future air traffic control will have to rely on more advanced automation in order to support controllers in their job of safely controlling increased traffic volumes. A prerequisite for the success of such automation is that the underlying data driving it is reliable. Current technology, however, still warrants human supervision in coping with (data) uncertainties and consequently in judging the validity of machine decisions. In this paper the Ecological Interface Design (EID) framework is explored to assist controllers in fault diagnosis using a prototype ecological interface (called the Solution Space Diagram) for tactical conict detection and resolution in the horizontal plane. Results from a human-in-the-loop experiment with sixteen participants indicate that the ecological interface with explicit presentation of the means-ends relations between higher-level functional goals and lower-level physical objects (i.e., aircraft) enables improved sensor failure detection. Especially in high complexity scenarios, this feature had a positive impact on failure detection performance. © 2016

关键词： Air traffic control Air navigation Aircraft detection Automation Aviation Control towers Controllers Decision making Ecology failure analysis Fault detection Fighter aircraft Conict detections Current technology Ecological interface design Human supervision Human in the loop experiments sensor failure sensor failure detection Supervisory control

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