Redundant Sensors Research Articles

Optimizing wireless sensor network lifetime through K-coverage maximization and memetic search

Many wireless sensor networks (WSNs) not only need to cover targets within their sensing fields, but also operate for extended periods without requiring expensive recharging. This is a challenging task when planning deployments, as sensing and data processing consumes sensor energy. Redundant sensors can help extend the lifetime, but also require good placement and scheduling to be effective. This paper seeks to design a placement scheme and schedule that can achieve near-optimal network lifetime, while satisfying the target coverage constraint. The problem is approached in two phases. The first phase places sensors to maximize K-coverage, a metric closely related to lifetime, using a novel heuristic called Greedy Intersecting Arc Selection. The second phase schedules the nodes’ operations for maximum lifetime, using Memetic Set Scheduler, a novel memetic algorithm. Experimental results show notable improvements in both solution quality and running time for the proposed approach compared to two existing approaches to the problem.

Human muscle fatigue monitoring method and its application for exoskeleton interactive control

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

An Analytical Study of the Elements of Airworthiness Certification Technology Based on the Development of the Conversion of Diesel Engines for Vehicles to Aviation

Aircraft reciprocating engines have been in operation over the past 100 years, which is a testament to their high levels of reliability and stability. Compared to turbine engines, reciprocating engines are at a disadvantage when it comes to high-speed flight. Nevertheless, they are widely used mainly for small aircraft thanks to their high specific power or power-to-weight ratio. Considering that propulsion systems account for approximately 40% of the aircraft price, lightness and high performance are key attributes of aircraft to achieve longer endurance. With the advantages offered by diesel engines, such as fuel economy, less maintenance, and a long lifespan, many attempts have been made to mount automotive diesel engines on urban air mobility and light aircraft. Recognizing advanced automotive diesel technology, where the power-to-weight ratio of the diesel engine is approximately 1 PS/kg, we analyzed a case where an automobile engine was converted for use in an aircraft. We focused on the Mercedes-Benz OM640 and the Austro AE300 and disassembled the two engines for comparative analysis. We then classified the engine components modified for aircraft use by (1) defining the major engine parts as fixed and alteration ones; (2) identifying the airworthiness-related alteration parts; and (3) categorizing the conversion purposes into classes A, B, and C. Components under class A were further categorized into subgroups in accordance with the airworthiness certification specifications outlined by the European Union Aviation Safety Agency. This helped determine the corresponding airworthiness standards for each subgroup. An inspection of the oil supply system revealed the need to apply safety wiring for some components to prevent possible oil leakages, which can be caused by the pressure difference with increasing altitude. Moreover, given that sensor manufacturers are required to present guidelines for sensor redundancy through numerous designs and tests and secure single-fault tolerance, we established criteria for selecting and applying sensors and separating sensors that must be made redundant from ones that are not subject to sensor redundancy.

Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning, referred to as latent-enhanced variational adversarial active learning (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing active learning strategies.

Estimation‐Based Event‐Triggered Adaptive Terminal Sliding Mode Control without Pressure Sensors for a Polymer Electrolyte Membrane Fuel Cell Air Feeding System

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).

Strategies for sensor virtual in-situ calibration in building energy system: Sensor evaluation and data-driven based methods

Sensor errors greatly affect the optimal control, safe operation, and energy efficiency of the building energy system. The sensor virtual in-situ calibration (VIC) method based on Bayesian inference can calibrate the sensor errors and does not need to remove existing sensors or add redundant sensors. In the previous study, the calibration of all sensors (non-differential calibration) and the physical model-driven based methods are applied in the simulation system. However, in the practical building energy system, these reduce calibration accuracy and efficiency. To solve these two negative factors, two calibration strategies are proposed: (1) sensor evaluation, and (2) data-driven based methods. Four cases are designed to demonstrate calibration accuracy and efficiency improvement by applying the calibration strategies in the variable air volume system. The results show that non-differential calibration causes difficulties in practical applications because of its low accuracy (38.10 %) and long calibration time (5136.35 s). The calibration accuracy of the physical model-driven based methods is 9.06 %, which completely deviates from the true value. The two calibration strategies are applied to improve the sensor calibration accuracy (up to 91.88 %) and reduce the calibration time (28.99 %) although it will increase some replacement costs. Meantime, the calibration accuracy of the temperature sensor is further improved (exceeding 94 %) by dividing the operating conditions according to the compressor power. In summary, the application of the calibration strategies can effectively overcome the negative impacts on calibration accuracy and efficiency, and increase the robustness of the VIC method in the practical building energy system.

Time Series-Based Sensor Selection and Lightweight Neural Architecture Search for RUL Estimation in Future Industry 4.0

Industry 4.0 is the current development trend in manufacturing technology. Regarding prognostic and health management (PHM) systems, a major task is to predict the remaining useful life (RUL) of a machine. To have precise RUL information, massive sensors should be involved, which leads to the dramatic cost of sensor network construction. Therefore, many sensor selection methods have been proposed to remove the redundant sensors under a certain constraint of RUL estimation. However, those approaches do not consider time-series sensing data, which is an intrinsic feature of mechanical signals. Therefore, the current research suffers from sensor under-killing problems due to the pessimistic consideration. To solve this problem, this work first considers the time-series sensing data to propose an integrated group-based valuable sensor selection algorithm by using the Least Absolute Shrinkage and Selection Operator (LASSO) property. In addition, to reduce the computing and hardware overhead, we further propose a heuristic neural architecture search (NAS) method to reduce the number of neurons in each neural network for further RUL estimation. To verify the proposed approaches, we use the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and utilize the PHM score to evaluate the performance of RUL estimation. Compared with the conventional methods, the proposed sensor selection method can reduce the PHM score by 9% to 99% with fewer sensors. On the other hand, the proposed NAS method can reduce the number of neurons by 86% to 89% and reduce the PHM score by 17% to 79% compared with the traditional NAS approaches.

Reliability evaluation of the hybrid-redundancy sensor fault tolerate system in the dredging perception system

The accurate evaluation of equipment reliability is critical in engineering applications and especially challenging for equipment with complex structures and high reliability requirements. In this paper, a reliability evaluation method based on semi-Markov process is proposed and applied to the hybrid-redundancy sensor fault tolerant (HRSFT) system within the perception system of a cutter suction dredger (CSD). The method takes into account the harsh operating environment of the CSD sensor, and considers the temporal occurrence of sensor faults as well as the imperfection of the sensor fault diagnosis and isolation (SFDI). The outcomes of the model applied to a case study indicate that three key factors, namely, the failure rate of the sensors, the false alarm rate (FAR) of the SFDI system and the self-correction rate (SCR) of the HRSFT system, have a significant impact on the system reliability. Additionally, we performed a comparative analysis between the HRSFT and Double Redundancy Sensor Fault Tolerant (DRSFT) systems, from which the superiority of the HRSFT system in terms of reliability is demonstrated. A series of experiments were conducted with varying parameters values, to show the high adaptability of the model to the HRSFT system.

Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet-of-Things (IoT) spectrum sensors, performing some of the previous tasks, are exposed to software manipulation aiming to execute spectrum sensing data falsification (SSDF) attacks to compromise data integrity and spectrum optimization. Novel intrusion detection systems (IDSs) combining device fingerprinting with Machine and Deep Learning (ML/DL) improve the limitation of traditional solutions and remove the necessity of redundant sensors and reputation mechanisms. However, they fail when detecting SSDF attacks accurately while protecting sensors privacy. This work proposes a novel host-based and federated learning-oriented IDS for IoT spectrum sensors that considers unsupervised ML/DL and fingerprints based on system calls. The framework detection performance and consumption of resources are analyzed in local and federated scenarios with six spectrum sensors deployed on Raspberry Pis. The obtained results significantly improve related work when detecting SSDF attacks while protecting sensors privacy, and consuming CPU, memory, and storage of sensors in a reduced manner.

Cyber-Physical System-Based Path Tracking Control of Autonomous Vehicles Under Cyber-Attacks

As one of the typical applications of cyber-physical system (CPS), autonomous vehicles (AVs) are vulnerable to malicious disturbance from cyber-attacks while tracking the desired path. This paper focuses on CPS-based path tracking control problem of AVs under cyber-attacks. Firstly, the nonlinear state and measurement equations of AVs under cyber-attacks are established based on vehicle dynamics model. Secondly, to improve the robustness of AVs against cyber-attacks, sensor redundancy is introduced. A cyber-attack detection method is designed by using extended Kalman filter (EKF), and the computational complexity of the cyber-attack detection method is analyzed. In addition, sensor switching rules are developed to isolate the disturbance of cyber-attacks. Then, the control problem of the AVs is formulated based on model predictive control (MPC). Input-to-state stability (ISS) of the control system under cyber-attacks is established, and a link between the tolerable attack intensity and the detection thresholds is clearly revealed. Finally, the simulation results demonstrate the effectiveness of the proposed control strategy.

Critical measurement parameters estimation in liquid rocket engine using LSTM-based soft sensor

Liquid Rocket engines (LREs) need to be hot tested on the ground, in order to make them flight-worthy. Ground hot tests are critical, expensive, and involve considerable human efforts in acquiring the critical measurement parameters. Measurement sensors are prone to failure due to the hazardous propellant environment and are vulnerable to noise (internal and external) during the LRE ground hot tests. The usage of redundant sensors is not a viable solution for space applications due to cost, power, weight, and space constraints. Data-driven Soft Sensors (SS) are being used in different industrial processes. SS can provide an effective alternative and economical solution for space applications. The objective of the proposed work is to implement a suitable data-driven soft sensor for estimating three critical measurement parameters namely, turbine speed, chamber pressure (Pc), and propellant flow rate of LRE. Long Short-Term Memory (LSTM) networks are capable of learning order dependence in sequence prediction problems and are well-suited to make predictions based on time series data. As the selected LRE test data contain order dependence sequences, LSTM is well suited for the estimation of critical measurement parameters. LSTM-based soft sensor (LSTM-SS) was developed and trained using a large sample set of 59,80,000 data points collected over a period of 12 years of LRE ground hot tests. The maximum root mean square error (RMSE) of 6.06% was obtained for propellant flow-rate estimation, whereas the minimum RMSE of 0.22% for chamber pressure and RMSE of 0.87% for turbine speed were obtained. The obtained results show that the designed LSTM-SS has superior performance and better generalization for practical use.

Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.

Cooperative and Comprehensive Multi-Task Surveillance Sensing and Interaction System Empowered by Edge Artificial Intelligence

Numerous sensors were introduced to intelligent transportation systems (ITS) in the past decade. Consequently, new sensing technologies and the generated data attracted more and more attention, which brought new challenges, such as redundant sensors, huge maintenance costs, and data explosion, to ITS. To satisfy the core demands of traffic agencies in a more effective and efficient way, therefore, this paper proposes the idea of “Sensing as a Service” (SaaS) and implements it in the Cooperative and Comprehensive Smart Edge Node for Sensing and OpeRation (COCO SENSOR) system for practical deployments. COCO SENSOR is an innovative multi-task sensing system, which was developed to address key practical applications, including real-time vehicle counting and recognition, road-surface-condition classification, visibility estimation, and live communication among traffic controllers and road users in a single unit. COCO SENSOR introduced customized cooperative sensing and a parallel computation mechanism to increase perception accuracy and efficiency with limited computation resources on the edge device. In collaboration with the Washington State Department of Transportation and the City of Bellevue, a field experiment was conducted to test the system’s performance. The results of the experiment show that the COCO SENSOR effectively fills the gap between sensing and traffic services and that it successfully executed four practical applications, including traffic-volume counting by vehicle type, traffic-status detection, road-visibility estimation, and road-surface-condition classification, with high accuracy. Additionally, a mobile app was developed for both traffic managers and users to access comprehensive traffic information and live warning messages.

Virtual Multiphase Flowmeter Using Deep Convolutional Neural Networks

Summary Petroleum wells produce a combination of oil, gas, and water in what is called a multiphase flow. This mixture is transported through flowlines to a tank separator that isolates and quantifies the volume of each fluid. However, this mechanical gravity separation process takes a long time, and the tank is often shared between many other wells in a field, making it difficult to allow an individual online measurement of the extracted fluids. Without this information, operators cannot effectively control production or estimate each well’s depletion rate, leading to losses or reduced profits. This paper aims to propose a low-cost, instantaneous model to perform this measure using artificial intelligence, commonly known as a virtual flowmeter (VFM). The idea behind it is to use data from pressure and temperature sensors already available on every well in addition to the state of the opening control valve to train a deep neural network with a convolutional layer to output each fluid’s volume rate. The proposed method is computationally simpler than recurrent neural networks and provides similar results. However, it still requires data to train the neural network. Adequate free databases of well production with telemetry are hard to find, so this paper proposes using the Schlumberger OLGA multiphase flow simulator software to provide the data, adjusting the simulator with fluid and operational information from actual wells. Tests have shown that the approximation with the proposed methods achieves up to 99.6% accuracy, making it possible to replace an expensive multiphase meter or use it as a redundant digital sensor for fault alerts of possible inaccurate readings.

GAS turbine sensor fault diagnostic system in a real-time executable digital-twin

In this study, a sensor fault diagnostic system to detect/isolate and accommodate faults in sensors from an industrial gas turbine has been developed. The sensor fault diagnostic module is integrated with a gas turbine real-time executable digital-twin (RT xDT) reported in a previous study. The sensor fault diagnostic module of the digital-twin considers analytical sensor redundancy using a reference engine model to provide redundant estimates of measured engine variables. A Software-in-the-Loop (S-i-L) architecture and Hardware-in-the-Loop (H-i-L) facility are constructed to assess the sensor diagnostic module (fault detection/ fault isolation) during failure in sensors from the engine. The results demonstrated that if the discrepancy between virtual measurement (provided by digital-twin) and sensor measurement exceeds the prescribed tolerance levels, the sensor fault diagnostic logic determines the state of switching between the virtual and engine sensor measurements in a dual lane control configuration of the gas turbine control system. The sensor fault detection system implemented in the gas turbine RT xDT can be deployed onto a distributed control system of industrial gas turbines to diagnose sensor deficiencies and ensure continuous and safe operation of the gas turbine. Consequently, the developed system will increase engine availability and reliability by diagnosing engine operational deficiencies before severe failure.

Extension of the system boundary of the Digital Twin onto the sensors of the Physical Twin through the introduction of redundant soft sensors

The benefit of Digital Twins depends to a large extent on the quality of the sensor data provided. In many cases, sensor failures are only detected late in operation which can lead to serious consequences. For this reason, one approach to reduce the resulting safety issues is to use redundant sensor systems that monitor the same measureand. However, due to the additional sensors required, this is associated with additional financial and design effort.In this publication an alternative strategy is presented, which provides a redundant sensor system with the help of soft sensors. Soft sensors use already installed physical sensors to anticipate a new measured variable via algorithms. They are often used to avoid placing sensors in inaccessible locations, but are used here to perform redundant computation of already existing metrics. The sensor data of physical and soft sensors are used as input variables for a Digital Twin. Here, these are compared with each other and can be critically questioned by the twin itself. This makes it possible to extend the system boundary of the Digital Twin to the sensors themselves and provided input variables can be checked for their validity. This allows sensor failures to be detected at an early stage and consequential damage to be averted.

High-Precision Gravimetric Monitoring Provides Insights into Subsurface Processes

Summary Monitoring of time-lapse changes in gravity has been carried out for decades, and measurement repeatability with relative spring gravimeters has in recent years improved to 0.6–2 µGal (standard deviation) for area surveys. This is better than often cited vendor specification. Gravimeters based on other physical principles offer high absolute accuracy or improved time-lapse sensitivity. All these may open possibilities for monitoring smaller and deeper subsurface changes and over shorter time intervals, with applications in hydrocarbon and geothermal production, gas and CO2 storage, hydrology, volcanology and more. Survey design and data processing are integral parts of a program. Sensor redundancy and repeat measurements provide opportunities for in-situ calibrations. An example from a gas accumulation shows a reduction in gravity of about 3 µGal and subsidence of 5–6 mm over 8 years. Both signals are clearly above the noise level and are likely to have been caused by production from a neighbouring structure. The information can quantify the amount of reservoir pore pressure drop, size of gas cap expansion and vertical movements of the gas-liquid contact to a precision well below 1 m.

Redundant Parallel Beam Multiaxis Force Sensor—Stress Correlation Coefficient

The stress nonuniformity on the loading neck is an important factor that leads to the local force error of an individual multiaxis force sensor (MAFS) in a distributed multiaxis force sensing system (DMAFSS). The stress correlation coefficient is used to characterize the stress nonuniformity. The stress correlation coefficient decreases with the increase of the loading offset. A full-constrained high-relative-stiffness-ratio structure is important for an MAFS to obtain a good stress correlation coefficient on its loading neck under various external loads. An individual MAFS with two loading necks is employed in the sensing system to increase the relative stiffness ratio. A thin loading neck, a long loading neck distance, and a high stiffness loading frame are important for the high-relative-stiffness-ratio structure. A local high-relative-stiffness-ratio structure is valuable for a large-scale DMAFSS. The mechanical hinge on the loading neck is useful to realize a large-scale lightweight DMAFSS. The extreme load including the load outside the reference loading zone and the concentrated torque affects the stress correlation coefficient apparently which should be specified in future standards.

The Airborne Redundant Sensors Management Method With Markov Set-Value Decision Strategy

The airborne sensor system with system-level redundancy architecture is a convenient and effective method to ensure mission reliability and safety. In practice, the management strategy of the redundant system mainly relies on the engineer’s experience rather than systematic analysis in theory. The Markov set-value decision strategy was proposed to provide an analyzable and systematic strategy for managing the redundant sensor system, such as the redundant inertial reference system. In this strategy, the set-value system is the framework. The Dempster-Shafer (D-S) theory and the Markov decision process are used to improve the accuracy and the stability of the decision results. The results of the simulation experiments certify the advantage of the new strategy over the traditional one with less miss alarm rate and similar false alarm rate.

Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.