Redundant Sensors Research Articles

A novel approach for context-aware sensor optimization in a smart home

Ambient Intelligence is an emerging technology that allows for reasoning of activities that are carried out in a smart environment. Smart sensors play a vital role in activity recognition tasks. Though a great number of sensors provide more pinpointed information about where and when the interaction in the environment occurred, addition of sensors also results in higher power consumption and greater cost constraints. The number of training samples used for activity recognition tasks also increases and becomes computationally expensive. Thus, to overcome these constraints sensor optimization in smart environment is carried out. Mapping of sensors to a user's activity becomes tedious when more than one activity is carried out at the same timestamp. Unambiguous sensor-activity relationship is derived with the help of spatial and temporal context represented by ontology model that captures real time sensor data. Since there exits spatial noise during activity annotation process, it is eliminated with help of pointwise mutual information score. Spatial and temporal data of an activity helps sensor optimization in multi resident living with concurrent activity occurrence. Inactive sensors are removed using mutual information score and redundant sensors are contextually paired based on where an activity happens. By doing so, only unique set of sensors are paired every time even when concurrent activity occurs. The quality of features derived is tested by testing the accuracy of the model by activity recognition task using Random Forest algorithm. Nearly 24% of sensors were identified as inactive in the smart environment. The accuracy of Human Activity Recognition (HAR) task remained constant even after removing the inactive sensors. Addition of spatial context helped to improve HAR with increased accuracy. Contextual pairing of redundant sensors resulted in minimal drop in the model's accuracy.

Development of a reliable measurement station for air quality monitoring based on low-cost sensors and active redundancy

Air pollution is often monitored by conventional measuring stations which require heavy investments and qualified personnel. They allow reliable, accurate, and continuous monitoring of the evolution of air pollution. However, few stations are deployed and territories of several thousand of km2 are coarsely covered. Consequently, it is difficult to observe local pollution phenomena. To try to solve this problem, low-cost sensor networks (LCS) are used. Inexpensive and easy to deploy, they allow finer spatial measurements and detection of local pollution sources. However, LCS present several drawbacks, notably in terms of component quality, measurement reliability, and their relative lifetime. As a result, achieving air quality monitoring objectives reliably and continuously becomes difficult. To overcome these drawbacks, a measuring station based on LCS and active redundancy is developed in this paper. Its main feature lies on the redundancy of smart sensors composed of LCS, which allows monitoring pollutant concentrations at a specific spatial point reliably and continuously. This measuring station is deployed in the city of Argelès-Gazost to monitor the background pollution and track its evolution throughout the year. It transmits its data to a central server which offers a friendly graphical representation and stores the data and use them for later processing and decisions.

Disturbance Observer-Based Force Estimation and Fault Detection for Robotic Manipulator in Radioactive Environments

Robotic systems with force sensing have great potential for use in radioactive environments. In this study, a modified observer-based method was developed to calculate the unknown external force without adding a redundant sensor and achieve the fault detection in the presence of a force sensor. A dynamic model of a serial robotic manipulator was built and the design procedure for a modified disturbance observer (MDO) was established. The output of observer was then used to suppress the disturbance and generate the fault signature. Moreover, the stability analysis shows that the convergence of the observer error is ultimately bounded. Simulation results under the step and composite sinusoidal disturbance torque demonstrate the performance of the force estimation and disturbance rejection. The results, obtained using the Kinova Jaco2 robot manipulator, show that the estimated errors of the external force in X-Y-Z direction are bounded within ±0.5 N, ±2 N, and ±3 N, respectively. Finally, the effectiveness of fault detection is also verified by the experiment results.

Optimal Placement of Laying Hen House Temperature Sensors Based on Genetic Algorithm

The arrangement of temperature sensors in most existing large-scale laying hen houses is random or placed according to the experience of breeders. However, this can’t achieve accurate monitoring of the henhouse. The temperature in the laying hen houses cannot be uniformly controlled, leads to the reduction of production efficiency of laying hens. In this paper, aiming at the placement of temperature sensors in laying hens’ houses, a placement optimization method was proposed. Firstly, the correlation coefficient of sensors is calculated to eliminate redundant sensors. Then, all the remaining sensors are arranged and combined. Finally, taking the grey correlation degree of each combination as the objective function, the dual-structure coding genetic algorithm is used to optimize the sensor combination. The strategy was tested in a large hen house. When the initial deployment of 81 sensors is reduced to at least 3, the established target can still be achieved, and the position of the target sensor can be calculated. This strategy not only meets the goal of accurately monitoring the hen house temperature, but also saves the hardware cost, which has important application value.

TRPM3 Is Expressed in Afferent Bladder Neurons and Is Upregulated during Bladder Inflammation.

The cation channel TRPM3 is activated by heat and the neurosteroid pregnenolone sulfate. TRPM3 is expressed on sensory neurons innervating the skin, where together with TRPV1 and TRPA1, it functions as one of three redundant sensors of acute heat. Moreover, functional upregulation of TRPM3 during inflammation contributes to heat hyperalgesia. The role of TRPM3 in sensory neurons innervating internal organs such as the bladder is currently unclear. Here, using retrograde labeling and single-molecule fluorescent RNA in situ hybridization, we demonstrate expression of mRNA encoding TRPM3 in a large subset of dorsal root ganglion (DRG) neurons innervating the mouse bladder, and confirm TRPM3 channel functionality in these neurons using Fura-2-based calcium imaging. After induction of cystitis by injection of cyclophosphamide, we observed a robust increase of the functional responses to agonists of TRPM3, TRPV1, and TRPA1 in bladder-innervating DRG neurons. Cystometry and voided spot analysis in control and cyclophosphamide-treated animals did not reveal differences between wild type and TRPM3-deficient mice, indicating that TRPM3 is not critical for normal voiding. We conclude that TRPM3 is functionally expressed in a large proportion of sensory bladder afferent, but its role in bladder sensation remains to be established.

Sparse Array Design for DOA Estimation of Non-Circular Signals: Reduced Co-Array Redundancy and Increased DOF

In the past decade, sparse arrays have aroused considerable attention for the ability to provide larger array aperture, more degrees of freedom (DOFs) and better estimation performance compared with the uniform linear array (ULA). In this paper, the problem of sparse array design is investigated for direction of arrival (DOA) estimation of non-circular (NC) signals, where both the difference co-array (DCA) and sum co-array (SCA) of physical array configuration can be utilized. Specifically, we first introduce a sliding array strategy which moves the array along the axis as a whole in order to reduce the co-array redundancy and enhance the consecutive DOFs of the sum-difference co-array (SDCA). Subsequently, we focus on the SCA of the conventional nested array (NA) and propose an approach which increases the DOFs of the SCA by relocating proper sensors. Furthermore, based on the above strategy and approach, we derive an extended sliding nested array (ESNA) with few redundant virtual sensors and an enhanced SCA, which can offer increased consecutive DOFs, enlarged virtual array aperture (VAA) and can thus bring high angle estimation accuracy of NC signals. Finally, analytic solutions and simulation results are given to validate the superiority of the proposed ESNA in terms of DOFs and estimation performance.

Improvement of virtual in-situ calibration in air handling unit using data preprocessing based on Gaussian mixture model

Fault diagnosis of sensors used in air conditioning systems can significantly reduce the energy consumption and improve the indoor comfort. Previously, a virtual in-situ calibration method based on the Bayesian inference and Monte Carlo method was proposed to diagnose sensor faults without installing redundant sensors. However, air conditioning systems operate in continuously changing operating conditions because of the changes in the outdoor environment and indoor load. This dynamic nature of the operating conditions poses difficulty in the selection of the existing historical datasets and results in uncertainty in calibration results. To address this problem, this study proposes Gaussian mixture model (GMM) to preprocess historical data to obtain steady-state measurements under various operating conditions. A variational Bayes expectation maximization (VBEM) algorithm was applied to the GMM to increase its clustering accuracy and remove human intervention. Next, the expectation maximization (EM) algorithm was used to solve the GMM in order to illustrate that unsuccessful clustering will lead to fluctuation of calibration results and verify the accuracy of VBEM. In this case, the number of GMMs was determined manually based on attempt step by step. Finally, the clustering results of the two algorithms were incorporated into a virtual in-situ calibration method. Based on comparison of calibration results, it can be concluded that the calibration results deviated significantly from the true value when the number of GMMs was unreasonable after the EM algorithm. However, the accuracy of all sensors was improved by 75% after calibration, and convergence occurred when the datasets were two to four times the number of sensors after the VBEM algorithm.

Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors.

Many people living in urban environments nowadays are overexposed to noise, which results in adverse effects on their health. Thus, urban sound monitoring has emerged as a powerful tool that might enable public administrations to automatically identify and quantify noise pollution. Therefore, identifying multiple and simultaneous acoustic sources in these environments in a reliable and cost-effective way has emerged as a hot research topic. The purpose of this paper is to propose a two-stage classifier able to identify, in real time, a set of up to 21 urban acoustic events that may occur simultaneously (i.e., multilabel), taking advantage of physical redundancy in acoustic sensors from a wireless acoustic sensors network. The first stage of the proposed system consists of a multilabel deep neural network that makes a classification for each 4-s window. The second stage intelligently aggregates the classification results from the first stage of four neighboring nodes to determine the final classification result. Conducted experiments with real-world data and up to three different computing devices show that the system is able to provide classification results in less than 1 s and that it has good performance when classifying the most common events from the dataset. The results of this research may help civic organisations to obtain actionable noise monitoring information from automatic systems.

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

Virtual sensing of wheel direction from redundant sensors in aircraft ground-steering systems

Virtual sensing of wheel direction from redundant sensors in aircraft ground-steering systems

Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals

Sensor-fusion and different machine-learning methods were used for tool condition monitoring (TCM) when sawing wood in harsh conditions using power, sound, vibration, and acoustic emission (AE) signals. Tool classification was performed using two ensemble learning (XGBoost and random forest) methods and SVM. It was discussed that the optimal combination of sensors for monitoring is a trade-off between the accuracy of classifiers and the tolerance for sensor redundancy. AE was shown to be the critical sensor, which combined with power signals and XGBoost resulted in ∼92% classification accuracy. Ensemble learning outperformed the SVM and showed superior performance for TCM using multi-sensory-features.

Evaluation of Murrell's EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations.

Pico- and nano-satellites, due to their form factor and size, are limited in accommodating multiple or redundant attitude sensors. For such satellites, Murrell’s implementation of the extended Kalman filter (EKF) can be exploited to accommodate multiple sensor configurations from a set of non redundant attitude sensors. The paper describes such an implementation involving a sun sensor suite and a magnetometer as attitude sensors. The implementation exploits Murrell’s EKF to enable three sensor configurations, which can be operationally commanded, for satellite attitude estimation. Among the three attitude estimation schemes, (i) sun sensor suite and magnetometer, (ii) magnetic field vector and its time derivative and (iii) magnetic field vector, it is shown that the third configuration is better suited for attitude estimation in terms of precision and accuracy, but can consume more time to converge than the other two.

Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

A Method for Rapid Self-Calibration of Wearable Soft Strain Sensors

Compared to rigid sensors, soft stain sensors are more suitable for measuring human joint motion because soft sensors are more comfortable to wear. In order to perform more precise measurements, the soft stain sensor should be re-calibrated before performing each measurement in order to eliminate error from donning and doffing. Current calibration methods are often performed by optical motion capture systems (OMCSs). However OMCSs are large and cumbersome, and there is no directly applicable calibration method for soft stain sensors in the measurement of human joint motion. Calibration should be able to be performed quickly and without the need of large equipment such as OMCSs. This paper proposes a calibration method for wearable soft strain sensors that can be done quickly and automatically. The soft sensor structure for self-calibration is parallel and partitioned, and there are integrated primary and redundant sensors. The basic idea of this method is to use redundant sensors to re-calibrate the primary sensor, and only a simple calibration action is required for the self-calibration. After self-calibration, the average errors of measurement were all less than 5 degrees, and the relative errors were all less than 4% for sensor and clothing donned and doffed and long-term sensor migration while wearing. Self-calibration can be performed in only 10 seconds. The results presented that the proposed calibration method is feasible for engineering applications.

“Is your SIF trying to do too much?”

AbstractThe number of inputs and outputs to a SIF (safety instrumented function)—including voting—affects the probability of failure on demand (PFD) and the SIL (safety integrity level), assuming the test interval remains the same. The larger the number of inputs and outputs that perform separate functions, the higher the PFD and potentially the lower the SIL. What if the SIF includes all the sensors and final elements that are involved in any trip of a large processing unit like a heater, a reformer, or a distillation column? What if the SIF includes actions to shut down upstream and downstream units? When SIL verification is done for such a SIF, the target PFD and SIL may not be possible to achieve. The temptation may be to add redundancy in sensors and final elements or to reduce the proof test interval in an attempt to reduce the calculated PFD. Frustration may abound as capital and operating costs rise steeply. This paper shows how to use the principles of layer of protection analysis and the information in the process hazard analysis to split up the massive SIF into smaller SIFs that are more manageable. An example is included.

Assessment of the Technological Maturity of Manned Submersible Navigation Positioning Systems

Abstract Precise navigation and positioning are key requirements for effective and safe operations of the deep-water human-occupied vehicles (HOVs) used for scientific exploration. This article presents the HOV real-time position determination methodology; technological maturity of the Aided Inertial Navigation System (A-INS); likely position errors due to subsystem misalignments, acoustic positioning aid outages, importance of sound velocity profile in precise positioning by numerical modeling; and the reliability of the state-of-the-art A-INS configuration in the HOV. It is identified that the A-INS comprising an internal measurement unit aided by Doppler Velocity Log, redundant depth sensors, and ultra-short‐baseline acoustic positioning systems with inbuilt communication capability shall have a “mean time to fail” period of ~6 years.

Fault-tolerant scheme for robotic manipulator-Nonlinear robust back-stepping control with friction compensation.

Emerging applications of autonomous robots requiring stability and reliability cannot afford component failure to achieve operational objectives. Hence, identification and countermeasure of a fault is of utmost importance in mechatronics community. This research proposes a Fault-tolerant control (FTC) for a robot manipulator, which is based on a hybrid control scheme that uses an observer as well as a hardware redundancy strategy to improve the performance and efficiency in the presence of actuator and sensor faults. Considering a five Degree of Freedom (DoF) robotic manipulator, a dynamic LuGre friction model is derived which forms the basis for design of control law. For actuator’s and sensor’s FTC, an adaptive back-stepping methodology is used for fault estimation and the nominal control law is used for the controller reconfiguration and observer is designed. Fault detection is accomplished by comparing the actual and observed states, pursued by fault tolerant method using redundant sensors. The results affirm the effectiveness of the proposed FTC strategy with model-based friction compensation. Improved tracking performance as well robustness in the presence of friction and fault demonstrate the efficiency of the proposed control approach.

A method for gait events detection based on low spatial resolution pressure insoles data

The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot–ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm2), sampling at 100 Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and <10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight network, the features of the input data can be extracted in real time. A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order. The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.

An enhanced sequential sensor optimization scheme and its application in the system identification of a rail-sleeper-ballast system

The problem of optimal sensor placement for system identification and damage detection is addressed by the development of a robust method based on Bayesian theory. Information entropy is used as the optimality measure to select the optimal configuration from the candidate configurations for a given number of sensors. The enhanced sequential sensor placement (ESSP) algorithm was developed to efficiently address the computational bottlenecks that may arise when a large number of measurable degrees of freedom (DOFs) are considered for candidate configurations. In this paper, the sensor redundancy problem in finely meshed models was addressed by considering (1) the spatial correction of prediction errors at measurable DOFs and (2) a minimum sensor interval. The proposed ESSP algorithm and the two strategies for handling sensor redundancy were studied by comparing the optimal configurations from the conventional methods and that from the ESSP algorithm for a rail-sleeper-ballast system. Finally, the optimal sensor configurations thus obtained were verified via model updating of an in-situ ballasted track system using measured data from an impact hammer test. The analysis results clearly show improvements in the optimality of the sensor configuration with the proposed method relative to the conventional methods.