Search Results (38)

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed. Full article

Digital twins, defined as virtual counterparts of physical systems that evolve with sensor data have potential applications in controlled-environment agriculture. This study previews the integration of adaptive Microclimate Monitoring within a Unity-based digital twin of a strawberry greenhouse to support dynamic sensor selection and reallocation. Using data collected from 56 distributed temperature–relative humidity sensors, a Thompson Sampling algorithm was deployed to assign monthly importance rankings and identify season-specific subsets of sensors. To evaluate how well these subsets represented the whole sensor network, we used the Z-index, which measures distributional consistency. Across all observed months, Z-index values remained close to zero, with values of 0.037 in February, 0.012 in April, −0.002 in June, and 0.025 in October for relative humidity. These results indicate that the digital twin framework sustains the overall climate trend while reducing sensing redundancy, pointing to its potential role in future climate monitoring strategies within greenhouse systems. Full article

Depth completion is a technique to densify the sparse depth maps acquired by depth sensors (e.g., RGB-D cameras, LiDAR) to generate complete and accurate depth maps. This technique has important application value in autonomous driving, robot navigation, and virtual reality. Currently, deep learning has become a mainstream method for depth completion. Therefore, we propose an edge-enhanced dynamically routed adaptive depth completion network, EDRNet, to achieve efficient and accurate depth completion through lightweight design and boundary optimisation. Firstly, we introduce the Canny operator (a classical image processing technique) to explicitly extract and fuse the object contour information and fuse the acquired edge maps with RGB images and sparse depth map inputs to provide the network with clear edge-structure information. Secondly, we design a Sparse Adaptive Dynamic Routing Transformer block called SADRT, which can effectively combine the global modelling capability of the Transformer and the local feature extraction capability of CNN. The dynamic routing mechanism introduced in this block can dynamically select key regions for efficient feature extraction, and the amount of redundant computation is significantly reduced compared with the traditional Transformer. In addition, we design a loss function with additional penalties for the depth error of the object edges, which further enhances the constraints on the edges. The experimental results demonstrate that the method presented in this paper achieves significant performance improvements on the public datasets KITTI DC and NYU Depth v2, especially in the edge region’s depth prediction accuracy and computational efficiency. Full article

In this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from sensors monitoring physical processes. The sense-collect-interchange-explore (SCIE) protocol is proposed to regulate UAV actions, ensuring synchronization and adaptability in a distributed manner. To maintain real-time monitoring while reducing data transmission, we introduce status freshness, which is an extension of age of information (AoI) and allows negative values to reflect the swarm’s predictive capabilities. The trajectory design problem is then formulated as an optimization problem to minimize average status freshness. A virtual force-based algorithm is developed to solve this problem, where UAVs are influenced by attractive forces from sensors and repulsive forces from neighbors. These forces guide UAVs toward sensors requiring data transmission while reducing communication overlap. The proposed distributed algorithm allows each UAV to independently design its trajectory, reducing redundancy and enhancing scalability. Simulation results show that the proposed method can significantly reduce average status freshness under the same energy efficiency conditions compared to artificial potential field algorithm. The proposed method also achieves significantly reduction in terms of communication overhead, compared to fully connected strategies, ensuring scalability in large-scale UAV deployments. Full article

This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system’s redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system’s dynamics—the input observer and the system model—which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheeled omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach’s efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults. Full article

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling’s stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements. Full article

In recent years, the application of machine learning for virtual sensing has revolutionized the monitoring and management of information. In particular, electrochemical sensors generate large amounts of data, allowing the application of complex machine learning/AI models able to (1) reproduce the measured data and (2) predict and manage faults in the measuring sensor. In this work, data-driven models based on an autoregressive model and an artificial neural network have been identified and used to (i) evaluate sensor redundancy and (ii) predict and manage faults in the context of electrochemical sensors for the measurement of ethanol. The approach shows encouraging results in terms of both performance and sensitivity analyses, allowing for the reconstruction of the values measured by two sensors in a series of six sensors with different dopant levels and to reproduce their values after a fault. Full article

Typical structures of monitoring systems (MSs) that are used in urban complex objects (UCOs) (such as large industrial facilities, power facilities, and others) during the post-accident period are combined with the technologies of flying sensor networks (FSNets) and flying edge networks (FENets) (FSNets and FENets); cloud/fog computing and artificial intelligence are also developed. An FSNets and FENets-based MS, composed of one of the Advanced Air Mobility (AAM) systems classes, which comprise main and virtual crisis centers, fleets of flying sensors, edge nodes, and a ground control station, is presented and discussed. Reliability and survivability models of the MS for the UCOs, considering various operation conditions and options of redundancy, are developed and explored. A tool to support the research on MS reliability, survivability, and the choice of parameters is developed and described. Crucially, this paper enhances the technique for assessing systems using the multi-parametrical deterioration of characteristics as a class of multi-state systems. Problems that may arise when using FSNets/FENet-based AAM systems are discussed. The main research results comprise a structural basis, a set of models, and a tool for calculating the reliability and survivability of FSNets/FENet-based AAM systems, with various options for distributing the processing and control resources between components, their failure rates, and degradation scenarios. Full article

Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously. Full article

Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array. Full article

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. Full article

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. Full article

Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. In this paper, a virtual sensor modeling method combining the maximum information coefficient (MIC) and the spatial–temporal attention long short-term memory (STA-LSTM) is proposed, which is named MIC-STALSTM, to achieve the dynamic and nonlinear modeling of the supply and return water temperature at both ends of the chiller. First, MIC can extract the influencing factors highly related to the target variables. Then, the extracted impact factors via MIC are used as the input variables of the STA-LSTM algorithm in order to construct an accurate virtual sensor model. The STA-LSTM algorithm not only makes full use of the LSTM algorithm’s advantages in handling historical data series information, but also achieves adaptive estimation of different input variable feature weights and different hidden layer temporal correlations through the attention mechanism. Finally, the effectiveness and feasibility of the proposed method are verified by establishing two virtual sensors for different temperature variables in the HVAC system. Full article

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed. Full article

Faults frequently occur in the sensors and actuators of process machines to cause shutdown and process interruption, thereby creating costly production loss. centrifugal compressors (CCs) are the most used equipment in process industries such as oil and gas, petrochemicals, and fertilizers. A compressor control system called an anti-surge control (ASC) system based on many critical sensors and actuators is used for the safe operation of CCs. In this paper, an advanced active fault-tolerant control system (AFTCS) has been proposed for sensor and actuator faults of the anti-surge control system of a centrifugal compressor. The AFTCS has been built with a dedicated fault detection and isolation (FDI) unit to detect and isolate the faulty part as well as replace the faulty value with the virtual redundant value from the observer model running in parallel with the other healthy sensors. The analytical redundancy is developed from the mathematical modeling of the sensors to provide estimated values to the controller in case the actual sensor fails. Dual hardware redundancy has been proposed for the anti-surge valve (ASV). The simulation results of the proposed Fault-tolerant control (FTC) for the ASC system in the experimentally validated CC HYSYS model reveal that the system continued to operate in the event of faults in the sensors and actuators maintaining system stability. The proposed FTC for the ASC system is novel in the literature and significant for the process industries to design a highly reliable compressor control system that would continue operation despite faults in the sensors and actuators, hence preventing costly production loss. Full article