Redundant Sensors Research Articles

Automatic testbed with a visual motion tracking system for airborne wind energy applications

AbstractThe architecture and a flight test campaign of a small‐scale testbed aimed at aerodynamic and dynamic characterization of airborne wind energy systems are presented. The testbed involves a two‐line rigid‐framed delta kite and an automatic ground station for the lateral control of the kite and reel‐in/reel‐out of the two tethers. The environment, and the states of the kite, the tethers and the actuators are measured by a set of on‐ground and onboard sensors that include, among others, an inertial measurement unit, GNSS receivers, load cells, actuator encoders, a wind station, and a visual motion tracking (VMT) system based on three cameras and an artificial neural network (YOLOv2). The results of a 5‐min flight, including the take‐off, cross‐wind flight, and landing, were used to analyze the capabilities of the testbed. It was shown that the time derivative of the kite course angle exhibits a linear correlation with both the delayed steering input and the delayed differential tether tension, being the dispersion lower for the latter. The intrinsic and extrinsic calibrations proposed for the VMT system led to a good agreement between the estimation of the kite position and course angle provided by the VMT system and the onboard computer. Moreover, although the YOLOv2 algorithm failed in the detection of the kite within around 5% of the images, the simultaneous non‐detection from the three cameras was below 0.1% during the full flight. Such a reliability suggests that a VMT system can be used as a redundant or backup sensor for the GNSS.

Modeling and Parameter Identification of a 3D Measurement System Based on Redundant Laser Range Sensors for Industrial Robots.

The low absolute positioning accuracy of industrial robots is one of the bottlenecks preventing industrial robots from precision applications. Kinematic calibration is the main way to improve the absolute positioning accuracy of industrial robots, which greatly relies on three-dimensional (3D) measurement instruments, including laser trackers and pull rope mechanisms. These instruments are costly, and their required intervisibility space is large. In this paper, a precision 3D measurement instrument integrating multiple laser range sensors is designed, which fuses the information of multiple redundant laser range sensors to obtain the coordinates of a 3D position. An identification model of laser beam position and orientation parameters based on redundant distance information and standard spherical constraint is then developed to reduce the requirement for the assembly accuracy of laser range sensors. A hybrid identification algorithm of PSO-LM (particle swarm optimization Levenberg Marquardt) is designed to solve the high-order nonlinear problem of the identification model, where PSO is used for initial value identification, and LM is used for final value identification. Experiments of identification of position and orientation, verifications of the measuring accuracy, and the calibration of industrial robots are conducted, which show the effectiveness of the proposed 3D measurement instrument and identification methods. Moreover, the proposed instrument is small in size and can be used in narrow industrial sites.

An Improved Unscented Kalman Filter for Interrupted and Drift Sensor Faults of Aircrafts

Reliable sensor signals are key factors in the flight control system. The development and application of various sensors not only improve flight performance and maneuvering capability but also bring challenges to the reliability and safety of the system. When sensor faults occur in nonlinear aircraft models, it is crucial to quickly recover system states. In this article, a new state estimation approach is presented using an interrupted-estimation unscented Kalman filter (IEUKF), which is integrated with maximum-likelihood estimations and Gaussian probability distribution. In general, aerial sensors involve complex fault conditions and a large number of strongly nonlinear systems. In spite of that, the proposed method can compute estimations robustly and achieve high precision under hard/soft and interrupted/permanent sensor faults. Compared with cutting-edge methods, the IEUKF can be applied to featureless interrupted faults without redundant sensors, foregone-fault hypotheses, or slow-varying fault assumptions. With a nonlinear F16 Simulink model as the example in simulations, the results illustrate the advantages of the proposed method.

Reduce System Redundancy and Optimize Sensor Disposition for EMG–IMU Multimodal Fusion Human–Machine Interfaces With XAI

Multimodal sensor fusion can improve the performance of human–machine interfaces (HMIs). However, increased sensing modalities and sensor count often cause excess redundancies, and when applying deep learning approaches, the recognition system can become overly complex and difficult for humans to understand. In this article, we propose an explainable artificial intelligence (XAI) approach to reduce redundancies in inertial measurement units (IMUs) and electromyography (EMG) multimodal systems and optimize sensor disposition in prosthetic hand control. Four attribution algorithms and four quantitative evaluation algorithms were used on an open-source dataset of 17 hand gestures from 60 healthy subjects and 11 amputees to explore the working mechanism behind the multimodal system. Using an XAI approach, we reduced the total number of required sensors by 40% while maintaining the same level of accuracy. These results could enable optimized HMI system design with reduced sensor costs and manufacturing costs. The proposed approach lays the foundation for improving HMI systems by reducing complexity and revealing explainable information that is typically hidden within deep neural networks, thereby facilitating patients in the daily use of prosthetic hands and helping improve their quality of life.

Sensor Fusion for Glucose Monitoring Systems

A fully automated artificial pancreas (AP) requires accurate blood glucose (BG) readings. However, many factors can affect the accuracy of commercially available sensors. These factors include sensor artifacts due to the pressure on surrounding tissues, connection loss, and poor calibration. The AP may administer an incorrect insulin bolus due to inaccurate sensor data when the patient is not supervising the system. The situation can be even worse in animal experiments because animals are eager to play with the sensor and apply pressure.In this study, we propose and derive a Multi-Model Kalman Filter with Forgetting Factor (MMKFF) for the problem of fusing information from redundant subcutaneous glucose sensors. The performance of the developed MMKFF was assessed by comparing it against other Kalman Filter (KF) strategies on experimental data obtained in two different animals. The developed MMKFF was shown to provide a reliable fused glucose reading. Additionally, compared to the other KF approaches, the MMKFF was shown to be better able to adjust to changes in the accuracy of the glucose sensors.

Reliable Traffic Sensor Networks Via Fault-Tolerant Sensor Reconciliation Schemes

Wireless sensor networks (WSNs) are in general composed of a large number of sensor nodes spread over a wide geographical area. In order to minimize the processing/communication burdens and hardware/maintenance costs, a limited number of sensors must reasonably be used and they arguably need to be smartly located. Moreover, unavoidable failures may affect the deployed sensors and may cause wrong elaborations and errors in the system state estimates, degrading the system performance or causing instability. This paper presents a traffic flow estimation architecture based on a fault-tolerant reconciliation design procedure for over-sensed sensor networks. In particular, given a subset of p redundant physical sensors, determined from the outset form a larger set of n ≫ p potential sensors through the solution of an Observability-based sensor selection procedure, the goal of the reconciliation observer is to hide corrupted measures in the estimation process, possibly generated by faulty sensors, and provide reliable state estimates. The presented approach envisages the use of Linear Parameter Varying (LPV) Luenberger Observers in charge of robustly estimating the system state along with the bias and the loss of effectiveness sensor faults. A road traffic monitoring problem is used as a case study to demonstrate the effectiveness of the proposed strategy.

Fault-Tolerant Design of Barrier Coverage for Periodically-Repairable Wireless Sensor Networks

Wireless sensor networks are sometimes deployed in circumstances where failed sensors may not be able to be repaired or replaced immediately after failure, e.g. a remote, harsh environment. By deploying redundant sensors, such networks will be more likely to tolerate individual sensor failures without experiencing an intolerable degradation in performance. This paper analyzes the redundancy required in a barrier-covering wireless sensor network where repairs to broken sensors can occur at periodic intervals, called “epochs.” Exact, tractable methods are derived for computing the total gap in coverage, the number of gaps in coverage, and the largest gap in coverage in an epoch. Together, the distributions of these metrics allow network designers to select how much redundancy is required to maintain their desired performance with a specified probability. Numerical simulations then demonstrate the accuracy of the methods, and show how network designers can explore the tradeoff between deploying larger numbers of cheap, poor-quality sensors and deploying smaller numbers of expensive, robust sensors.

A new method for identifying redundant sensors based on completely consistent fault isolability properties

This paper presents a new method to disclose redundant sensors from the viewpoint of the completely consistent fault isolability properties. If a sensor is removed from a diagnosis system, and the updated system also can isolate the same known faults and the potential faults, then the removed sensor is a redundant sensor based on completely consistent fault isolability properties. Through structural model decomposition, if a set of specific variables derived from the updated system is the same as the one obtained from the diagnosis system, then the fault isolability properties of the two related systems are the same for the known faults and the potential faults. Based on this criterion, the corresponding algorithm is provided and all of these redundant sensors can be extracted. It is useful for fault-tolerant control design and optimal sensor placement. Finally, the effectiveness and feasibility of the proposed method are validated by the simulations of a four-tank system.

Influence of Parameter Uncertainty to Stator Current Reconstruction Using Modified Luenberger Observer for Current Sensor Fault-Tolerant Induction Motor Drive.

In modern systems with induction motors (IM), in addition to precision, it is also very important to ensure the highest possible reliability and safety. To ensure the above, information about the stator current value is required. If the current sensor (CS) fails, a redundant sensor or an algorithmic solution can be used. The Luenberger observer (LO) can be used to estimate the lost stator current without increasing the cost of the drive system. However, this solution is based on the mathematical model of IM, which is sensitive to its parameters. Therefore, this paper presents a modified LO (MLO) and investigates the effect of a coefficient in the error gain matrix on improving robustness to changes in the IM parameters. As shown by extensive studies, the proposed solution has significantly reduced the influence of the IM parameters on the accuracy of the stator current estimation, which has not been previously reported in the known literature.

An Efficient Algorithm to Enhance Nonoverlapping Coverage Area with Less Energy Consumption in WSN

Several chargeable sensor nodes are deployed randomly to cover the target points with an efficient heuristic approach for the mobility of sensor nodes in an area of interest (AoI). The heuristic approach generates the cover set that includes the targets for a prolonged time. The cover sets are the subset of the total sensor node area where each set is capable of representing all the targets. The functionality of the sensor nodes depends upon the network lifetime of the target points covering an AoI. The network lifetime would improve by reducing the consumption of battery power through heuristic process. The proposed heuristic process can do this by generating cover sets and selecting sensor nodes with the highest remaining battery power. These cover sets remove the redundant sensor node in an AoI that causes the overlapping issue and assign the maximum lifetime which is the minimum amount of battery power of the sensor node, participating in the cover set. The results show the improvement in the mobility of sensor nodes by coverage and attain maximum network lifetime as compared to the existing algorithms.

Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System

This paper presents a systematic fault analysis and diagnosis method of a PEM fuel cell control system using a model-based approach. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system, and to diagnose them under the assumption that the model and the process are similar. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system and diagnose them under the assumption that the model and the process are similar. In this work, a model-based approach was adopted for fault analysis and diagnosis, and its methods are suggested. A PEM fuel cell is mathematically modelled, analyzed, and verified for the analysis and simulations. Relationships among variables are shown using an incidence matrix and with a Dulmage–Mendelsohn decomposition of the matrix. When it is difficult to detect faults due to a deficient degree of redundancy, a bi-partite graph is used to analyze the effect of faults and to assess the possibility of fault detection through the appropriate redundant sensor placement. Thereafter, residuals are obtained based on analytical redundancies of the system, and a fault signature matrix is subsequently constructed. A fault detection and isolation (FDI) algorithm is developed based on a fault signature matrix that describes the connection between faults and residuals. The simulation results demonstrate the validity and effectiveness of the proposed FDI algorithm for diagnosing faults. With the proposed FDI algorithm, eight faults could be diagnosed by FDI algorithm with given sensors in the system.

Accurate and real-time network calculation for mine ventilation without wind resistance measurement

A real-time correction method is proposed to accurately and comprehensively collect mine air volume data using wind velocity sensors; ventilation network calculation is realized without relying upon wind resistance measurements. For single-roadway wind velocity correction, an improved Kalman filter is employed to identify the error and adapt to dynamic changes in the ventilation system, improving the error updating process and thus the convergence rate. For node air mass flow balance correction, three types of sensor redundancy layouts are summarized with corresponding correction methods. The principle of the proposed real-time network calculation algorithm is discussed, the calculation method for each sensor layout is provided, and the robustness is analyzed accordingly. Simulation experiment results demonstrate significant improvements in the accuracy of the data obtained using the proposed data correction and network calculation algorithm. Thus, the designed real-time calculation algorithm can be practically applied to realize the accurate collection of ventilation data.

Sensor Fault-Tolerant State Estimation by Networks of Distributed Observers

We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system's observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system's state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme.

Online intelligent fault diagnosis of redundant sensors in PWR based on artificial neural network

Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and, hence, seriously affects the reliability of the system. To mitigate the CMF of redundant sensors in nuclear power plants, an artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify any possible abnormal status of the sensors, and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation, and test data, a relevant parameter-based ANN black-box model (RPANN) was established by employing the back-propagation (BP) learning algorithm, which was then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of the RPANN in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring tool of the hardware status and an online diagnosis strategy for sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct the reading of the monitored sensor parameter without having to reduce the reactor output power or even shut down the reactor for emergency maintenance so that the on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR power plants but also plays an important role in building reactor operation schemes with rapid and frequent changes in power output in the future. Simultaneously, the diverse redundancy combining analytic software redundancy and physical hardware redundancy can effectively reduce the threat of CMF of hardware sensors on the operation safety of reactor systems.

LPV Control and Virtual-Sensor-Based Fault Tolerant Strategies for a Three-Axis Gimbal System.

This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios.

SensorTalk: Extending the Life for Redundant Electrical Conductivity Sensor

Air and soil sensors are used in smart agriculture to autonomously collect the soil-related data. It is important that the measurements of the sensors are accurate. This article uses electrical conductivity (EC) as an example to show how to avoid the inaccuracy due to EC sensor aging. We consider a promising solution that packages <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> redundant EC sensors in one device ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}&gt;{1}$ </tex-math></inline-formula> ) called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package. The redundant-sensor package outputs the measured data based on a voting process among <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> sensors, which makes soil measurement more accurate and reliable. Based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package, this article invents the first out-of-the-laboratory automatic calibration procedure in the farm field. We also propose the SensorTalk mechanism to extend the life of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package through calibration. Most people have the illusion that the life of a redundant-sensor package is longer than a nonredundant sensor. This article proves that the life of a redundant-sensor package is shorter than its nonredundant counterpart. Then, we propose the self-calibration process of the redundant-sensor package to extend its operation life. For example, if the life of a sensor in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant package is six months with the aging-after-calibration factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\gamma }={0}.{9}$ </tex-math></inline-formula> , then SensorTalk may extend its life to 52.2 months for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}={9}$ </tex-math></inline-formula> . We also shed some nontrivial insights. In many smart farming solutions, the sensors typically report the measured data with a frequency lower than one per 10 min. Our study indicates that by increasing the sampling frequency up to one per 20 s, calibration for an EC value can be sped up by 19 times. Our study also suggests that an odd <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> number should be chosen. If we add one more sensor to make an even <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> number, the life of the redundant-sensor package is actually shortened.

Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing

Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform composed of more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.

UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning.

Target-barrier coverage is a newly proposed coverage problem in wireless sensor networks (WSNs). The target-barrier is a closed barrier with a distance constraint from the target, which can detect intrusions from outside. In some applications, detecting intrusions from outside and monitoring the targets inside the barrier is necessary. However, due to the distance constraint, the target-barrier fails to monitor and detect the target breaching from inside in a timely manner. In this paper, we propose a convex hull attraction (CHA) algorithm to construct the target-barrier and a UAV-enhanced coverage (QUEC) algorithm based on reinforcement learning to cover targets. The CHA algorithm first divides the targets into clusters, then constructs the target-barrier for the outermost targets of the clusters, and the redundant sensors replace the failed sensors. Finally, the UAV’s path is planned based on QUEC. The UAV always covers the target, which is most likely to breach. The simulation results show that, compared with the target-barrier construction algorithm (TBC) and the virtual force algorithm (VFA), CHA can reduce the number of sensors required to construct the target-barrier and extend the target-barrier lifetime. Compared with the traveling salesman problem (TSP), QUEC can reduce the UAV’s coverage completion time, improve the energy efficiency of UAV and the efficiency of detecting targets breaching from inside.

Virtual temperature sensor using Support Vector Machines for autonomous uninterrupted automotive HVAC systems control

This paper presents the design and application of a virtual sensor used in case of failure of the main physical sensor for temperature readings in automotive air conditioning. Using auxiliary temperature and pressure physical sensors combined with Support Vector Machine (SVM) Regression, the model of the virtual sensor is derived and prepared to be used as a redundant sensor until the broken physical sensor maintenance. Thus, enabling the system to operate continuously and maintaining its original power and thermal comfort features until the physical sensor replacement. In total, 32 h of training and test data involving different environmental conditions were used to generate the model that was further integrated. The designed virtual sensor presented a Mean Square Error (MSE) of 1,3oC, a Mean Absolute Percentage Error (MAPE) of 3,6%, and a Pearson Correlation Coefficient of 0,90. The results enabled the continuous and autonomous operation of the air conditioning plant respecting the designed thermal comfort guidelines even when under the physical sensor failure condition.

Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks

Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.