Recently, smart home applications are increasing fast, including but not limited to occupancy-dependent control of lighting, heating and cooling. Passive infrared (PIR) sensors play an important role in these applications to perceive the presence and/or the motion of human. However, PIR sensors are not able to detect stationary occupants while stationary presence takes up most time of the day. And thus, the resulted false negative detection leads to uncomfortable light/temperature swings, shortened equipment’s lifespan, and/or energy waste, etc. To address this issue, our group has developed Synchronized Low-Energy Electronically-chopped PIR (SLEEPIR) sensors that integrate an electronic liquid crystal (LC) infrared shutter with an off-the-shelf PIR sensor. In this work, the infrared shutter made of polymer dispersed liquid crystal (PDLC) sandwiched by two germanium windows is proposed to periodically chop the long-wave infrared signal received by the PIR sensor so that stationary human presence can still be detected due to the electronical shuttering. A sensor module is fabricated, consisting of a wireless microcontroller, a SLEEPIR sensor and a traditional PIR sensor, with a field of view of 103° × 103°. Then, a sensor network consists of two sensor modules is developed. Three types of experiments are conducted in this paper: individual action-based, continuous activity-based, and daily routine-based. The detection logic is made by using the threshold value classification method, where the threshold values are determined from the action-based dataset and applied to the other two datasets. The results show that for activity-based dataset, the average accuracy reached 98.96%. For daily routine-based dataset, the average accuracy is 99.57%.

This paper proposes a probabilistic model for the placement of sensors that considers uncertain factors in the sensing system to find the best arrangement of sensor locations. Traditional procedures for structural health monitoring (SHM) usually rely on simplified behavior and deterministic factors from structure’s response. Incorporating the sources of uncertainty (e.g., loading condition, material properties, and geometrical parameters) in the design of sensor network will enhance the safety and extend the useful life of the complex mechanical systems. The proposed method is defined in a reliability-based design optimization framework to search for the sufficient number of sensors for failure detection using Genetic Algorithm. The optimal arrangement is found as the one that minimizes the number and size of sensor patches and maximizes the expected probability for failure detection. This design concept involves a new failure diagnosis indicator, named detectability, formulated based on the Mahalanobis Distance (MD). MD distribution is used as a measure of the quality of the obtained sensor configuration suitable for many sensing/actuation SHM processes, while considering the uncertainties such as those from structure properties and operation condition. The MD classifier categorizes large sets of testing data by comparing the distances of the mean with the distribution of available training data sets. Statistical evaluation of failure detectability can be obtained by comparing the distribution of MD for different failure modes. Kriging modeling, used for metamodel-based design optimization, is applied for surrogate modeling of the stochastic performance of system to reduce computational cost. The surrogate model is constructed by correlating the sensor output to the vibration pattern of the structure and sensor variable inputs (e.g., size and location). Direct finite element analysis (FEA) evaluates the sensor output with respect to the input variables. Consequently, the constructed kriging model enables the estimation of sensor output for any arbitrary sensor arrays. As a case study, a rectangular panel with a size of 40 cm × 30 cm is considered that is fastened using eight screw joints. The harmonic vibration force is applied to the center of the plate and its varied vibration pattern is used to detect the joint failure. Eight different combinations of join failure are defined as health statuses (failure modes), and different size and layouts of the piezoelectric sensors are considered to detect the health status. The results verify the capabilities of the new method for failure diagnosis of screw joints in a panel with high sensitivity of fault detection.

Vibration-based energy harvesting is a growing field for generating low-power electricity to use in wireless electronic devices, such as the sensor networks used in structural health monitoring applications. Locally resonant metastructures, which are structures that comprise locally resonant metamaterial components, enable bandgap formation at wavelengths much longer than the lattice size, for critical applications such as low-frequency vibration attenuation in flexible structures. This work aims to bridge the domains of energy harvesting and locally resonant metamaterials to form multifunctional structures that exhibit both low-power electricity generation and vibration attenuation capabilities. A fully coupled electromechanical modeling framework is developed for two characteristic systems and their modal analysis is presented. Simulations are performed to explore the vibration and electrical power frequency response maps for varying electrical load resistance, and optimal loading conditions are presented. Case studies are presented to understand the interaction of bandgap formation and energy harvesting capabilities of this new class of multifunctional energy-harvesting locally resonant metastructures. It is shown that useful energy can be harvested from the locally resonant metastructure without significantly diminishing their dramatic vibration attenuation in the locally resonant bandgap. Thus, by integrating energy harvesters into a locally resonant metastructure, there is new potential for multifunctional self-powering or self-sensing locally resonant metastructures.

Recent advances in energy harvesting technologies have led to the development of self-powered monitoring techniques that are energy-efficient. This study presents an intelligent damage identification strategy for plate-like structures based on the data provided by a network of self-powered sensors that communicate through a pulse switching protocol, which has been demonstrated as an effective means for minimizing communication energy demands. The energy-aware pulse switching communication architecture uses single pulses instead of multi-bit packets for information delivery, resulting in discrete binary data. A system employing such an energy-efficient technology requires dealing with power budgets for sensing and communication of binary data, which leads to time delay constraints. In this paper, a novel machine learning framework incorporating low-rank matrix decomposition, pattern recognition, and a statistical approach is proposed to overcome challenges inherent in algorithm design for damage identification using time-delayed binary data. Performance and effectiveness of the proposed energy-aware damage identification strategy was examined for the case of a dynamically loaded plate. Damage states were simulated on a finite element model by reducing stiffness in a region of the plate. Results show that the presence and location of the damage can be effectively identified even with noisy features and missing data. The performance and applicability of the proposed localized damage detection strategy for plate-like structures using discrete time-delayed binary data from a novel wireless sensor network is thus demonstrated.

The objective of this study is to develop three dimensional (3D) impact self-sensing composites capable of localizing impact damage in through-the-thickness direction. The 3D impact self-sensing composites (3D-ISSC) are designed by embedding fracto-mechanoluminescent (FML) crystals in cells of honeycomb-cored fiber reinforced polymer (FRP) structural composites. FML crystals were shown to emit light resulting from cleavage of crystalline structures due to external mechanical stimuli. Unlike other conventional sensor networks, without supplying external electrical source, the 3D-ISSC is envisioned to monitor impact occurrences and detect damage. Instead, the emitted light will be utilized for informing severity of impact occurrences and 3D locations of the impact damage. First, FML europium-doped dibenzoylmethide triethylammonium (EuD 4 TEA) crystals are synthesized. Second, the synthesized EuD 4 TEA crystals are embedded in the honey-cored FRP structural composites to fabricate 3D-ISSC. Third, to validate its 3D self-sensing capability, Kolsky bar is employed to apply high strain-rate compressive loading to simulate impact occurrences while taking high-speed video footage for quantifying intensity of FML light emission through image processing technique.

One of the challenges in structural health monitoring (SHM) is the power required for sensors to collect and communicate data. Self-powered sensors are able to harvest power by from their environment, i.e., strain and vibration of the host structure. However, the harvested power with current technology is still limited and improving the system’s efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. The binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with discrete binary (1 or 0) data, which implies system information with reduced resolution. This study presents an investigation on approaches for the interpretation of such kind of binary data for use in structural assessment and damage identification. Pattern recognition (PR) methods based on image data analysis were adapted for the study. The methods used were classifiers based on deviation of patterns with respect to each other, two-dimensional principal component analysis, and two-dimensional linear discriminant analysis. The PR methods and the performance of the interpretation algorithms were evaluated by using virtual data from finite element simulations on an aluminum plate. The ability for each of the PR methods to identify service demands, load variations and localized material degradation was evaluated. Results indicate that PR methods can be used as damage identification algorithms for binary data sets in novel wireless self-powered sensor networks.

Energy harvesting systems for structural health monitoring applications may be described by three stages: ambient energy transduction, electrical power conditioning, and data acquisition and transmission. Recent developments in low-power CMOS devices have allowed for expanded energy harvesting techniques by reducing the total power demand of sensor nodes. This paper investigates the system-level interaction between a corrosion-based energy harvester and the low-power sensor node to which it is supplying power. An equivalent circuit model of the energy harvester is developed and the matched parameters (source voltage and equivalent series resistance) are used in the design of the power conditioning and wireless transmitter circuitry. Analysis of the power demand from the sensor node is used to determine the optimum data sampling parameters in terms of available supplied power for long-term in-situ sensing operations on a marine structure.

In this paper, a Guided Lamb Wave (GLW) technique using piezoelectric transducers is presented to detect and monitor corrosion damage in an aluminum panel. An electro-chemical etching method was applied to simulate corrosion damage in the test article. A signal processing approach using time-based analysis was demonstrated to measure amplitude change and phase shift of GLW signals captured in the aluminum specimen. Damage Index (DI) for each sensor network was calculated based on the time-domain signal processing method. From the DI information, damage profile maps were produced by using a tomographic algorithm. Corrosion damage location and its size were identified from an integrated damage profile map. The severity of the corrosion damage was examined based on the phase shift of a fundamental anti-symmetric mode (A0) in the GLW signals. The experimental results show the capability of the proposed corrosion monitoring technique based on the GLW approach.

This report discusses the development of an embeddable impact detection system utilizing an array of piezoelectric wafer active sensors (PWAS) and a microcontroller. Embeddable systems are a critical component to successfully implement a complete and robust structural health monitoring system. System capabilities include impact detection, impact location determination and digitization of the impact waveform. A custom algorithm was developed to locate the site of the impact.. The embedded system has the potential for additional capabilities including advanced signal processing and the integration of wireless functionality. For structural health monitoring applications it is essential to determine the extent of damage done to the structure. In an attempt to determine these parameters a series of impact tests were conducted using a ball drop tower on a square aluminum plate. The response of the plate to the impact event was recorded using a piezoelectric wafer sensor network attached to the surface of the plate. From this testing it was determined that several of the impact parameters are directly correlated with the features recorded by the sensor network.

Sensors are frequently used in damage diagnosis for structural health monitoring (SHM) of aerospace structures. This process typically requires a considerably large number of sensors. By increasing the number of sensors, the amount of data collected, though useful, becomes a burden due to the required computational overhead. In this paper, a random correlation cumulative approach is used to track damage intensity and propagation. The relative correlation between any two randomly chosen sensors is recorded and compared over time. The randomness of the process leads to detecting and tracking any arbitrary crack propagation. A case study for damage detection and tracking using 40 sensors in a steel plate is presented and discussed. It is shown that the proposed method can successfully allow damage tracking while limiting the data considered in the sensor network.

This paper presents experimental measurements of the response of woven composite laminates to multiple low-velocity impacts. Damage initiation and progression occur at multiple physical and temporal scales in heterogeneous materials, including fiber breakage, matrix cracking, delamination and matrix relaxation. The sensor networks/interrogators were therefore chosen specifically to provide insight into the order and progression of different failure modes. Measurements of the contact force between the impactor and composite are measured throughout impact. Additionally, the dissipated energy per impact event is also calculated from the impactor velocity. Surface mounted and embedded fiber Bragg grating sensors are used for the measurement of the laminate response. Peak wavelength measurements are performed during impact at 1 kHz, while full-spectral scanning is performed at 5 Hz during relaxation period of the laminate immediately after impact and quasi-statically to measure post-impact residual strain states within the laminate. The results highlight the depth of information embedded within the FBG full-spectral data sensors, as well as the added insight to be gained from combined global-local measurements.