Sensor Placement Analysis Research Articles

Sensitivity analysis of sensor placement in energy-efficient, grid-interactive ready small office buildings with dynamic shading and lighting control

In smart buildings, dynamically controlled lighting and shading devices have a direct impact on occupants’ thermal and visual comfort, building energy use, and peak demand. This study aims to assess the impacts of adjusting vertical and work plane illuminance sensor location and rotation for different zone orientations across different climate zones, on illuminance levels, glare, energy consumption, and demand. Using the sensor locations of 1.6 m and facing the window (typical assumption), when implementing the integrated shading and lighting controls, the lighting and cooling energy savings range from 41% to 71% across both heating and cooling-dominated climate zones. The greatest cooling peak use demand reduction of 6.5 kW (23%) is achieved in Climate Zone 2 A, where the total cooling and lighting demand savings are 28-33% (9-10 kW). The percent change in lighting energy use is greater as compared to the percent change in cooling energy use when sensor location is varied, however, the total kWh and kW change is greater for cooling in most climate zones. Results suggest that adjusting the sensor distance from the window has a greater impact on illuminance levels, glare, and lighting and cooling energy use in all climate zones as compared to sensor rotation.

Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.

The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

Fault detection and diagnosis (FDD) modules in a modern control system are effective in detecting and identifying abnormal process behaviours in a timely manner, ensuring the high-performance of large-scale engineering systems. The detection and isolation of faults is essentially built on the characterisation of the observed behaviour of a system. However, due to the large number of technical indicators available for measurement, as well as the various constraints of sensor installation, monitoring all the operating parameters of a large-scale engineering system is not feasible. Therefore, locating sensors optimally in a large-scale system, to achieve a comprehensive description of an abnormality, becomes a key issue to successfully apply diagnostic technologies to real world situations. In this paper, a fault feature reduction (FFR) based sensor location approach is proposed for optimal sensor placement so as to achieve the desired performance of fault detection and isolation. The behaviour of faults is firstly analysed using a fault tree to obtain a comprehensive understanding of the multi-dimensional relationships between faults and symptoms. A Boolean matrix is then constructed to represent the corresponding relations around faults and potential sensors. All the alternative configurations of sensors, for a desired diagnosis of a system, are obtained by eliminating the redundant fault features. The trade-off without a certain sensor is also attained using the following proposed approach. Three large-scale systems, including, a diesel engine system and two chemical systems, are used to illustrate the proposed approach. Comparisons to existing competitive techniques indicate the enhanced abilities of the proposed approach to meet the varying requirements of a real-world monitoring network. The analysis of sensor placement can be performed at the design phase of a large-scale engineering system, to locate the preset measured hole, or, during the life-cycle, to perfect an incomplete or redundant monitoring system.

Analysis of sensor placement in beams for crack identification

Abstract A previous study has shown that the mode shapes of a beam are more sensitive to damage than other vibrational parameters, thus making them better suited for crack identification purposes. However, they have the disadvantage of being more difficult to be measured. To overcome this difficulty, an interesting idea is to monitor changes produced by cracks on the mode shapes only in a few strategic points, instead of performing a complete experimental modal analysis. Considering this possibility, the aim of the present work was to determine the most appropriate locations for installing sensors in beams in order to identify and characterize structural damages. The effect of different locations of cracks on the mode shapes of beams was studied through a numerical (computational) model using the finite element model. The results were plotted in 3D graphs relating the relative nodal displacement of damaged and intact beams with the crack position and the location of the point analyzed. Through the analysis of these graphs, it was possible to point out the most adequate sites for placing sensors aiming at identifying cracks in a beam in fixed-free and fixed-fixed boundary conditions. Aiming at testing the results, an optimization problem for crack identification was proposed and solved through genetic algorithm (GA). The cracks were identified with an accuracy that is appropriate for engineering applications, showing that the proposed method is effective and could be used in Structural Health Monitoring (SHM) issues. Limitations on its use were also discussed.

Robustness Analysis of Sensor Placement for Leak Detection and Location under Uncertain Operating Conditions

Some pressure sensor placement methods for leak detection and location in water distribution networks are based on the pressure sensitivity matrix analysis. This matrix depends on the network demands, which are nondeterministic, and the leak magnitudes, which are unknown. In this paper, the robustness of a sensor placement methodology against the fault sensitivity matrix uncertainty is studied. The robustness study is illustrated by means of a small academic network as well as a district metered area in the Barcelona water distribution network. Results reveal that this uncertainty should be taken into account in the sensor placement methodology.

Effects of Noise on Sensor Placement for On-Orbit Modal Identification of Large Space Structures

A method of sensor placement for the purpose on-orbit modal identification and test-analysis correlation is presented. The method is an extension of the Effective Independence method presented in past work to include the effects of a general representation of measurement noise. Sensor noise can be distributed nonuniformly throughout the structure as well as correlated between sensors. The only restriction is that the corresponding noise covariance intensity matrix is positive definite. The technique presented offers a fast and efficient approach for reducing a relatively large initial candidate sensor location set to a much smaller optimum set which retains the linear independence of the target modes and maintains the determinant of the Fisher Information Matrix resulting in improved modal response estimates. The noise covariance intensity matrix which has been introduced into the method can be thought of as a sensor weighting matrix which modifies the shape of the target modes. The mode shape coefficients are modified based upon the noise levels at the sensor locations. Inclusion of the noise model results in higher ranking of sensor locations with low noise levels and suppression of sensor locations with high noise levels. A criterion is also presented which can be used during the course of the sensor placement analysis to determine how many sensors are required to maintain a desired level of signal-to-noise ratio over all the target modes. Simple numerical examples are presented which clearly demonstrate the ideas and trends presented in the paper.

Effect of model error on sensor placement for on-orbit modal identification of large space structures

A theory is presented for the effect of errors in large space structure prelaunch finite element models on sensor placement for the on-orbit independent identification of a set of selected target modes. The idea of positive net information is introduced. If the net information matrix remains positive definite as sensor locations are deleted from an initial candidate set, the analytical model provides useful information for the identification of the real target modes. If the net information matrix becomes indefinite, the sensor placement analysis based on the prelaunch analytical model will actually detract from the independent identification of the target modes. A bound is presented that can be easily checked after each iteration during which a sensor was deleted to determine positive definiteness of the net information matrix. Numerical examples are used to illustrate the ideas that are presented.