Real time detection of stiffness change using a radial basis function augmented observerformulation

A structure's health or level of damage can be monitored by identifying changes in structural or modal parameters. This research directly identifies changes in structural stiffness due to modelling error or damage for a post-tensioned pre-cast reinforced concrete frame building with rocking beam column connections and added damping and stiffness (ADAS) elements. A structural health monitoring (SHM) method based on adaptive least mean squares (LMS) filtering theory is presented that identifies changes from a simple baseline model of the structure. This method is able to track changes in the stiffness matrix, identifying when the building is (1) rocking, (2) moving in a hybrid rocking–elastic regime, or (3) responding linearly. Results are compared for two different LMS-based SHM methods using an L2 error norm metric. In addition, two baseline models of the structure, one using tangential stiffness and the second a more accurate bi-linear stiffness model, are employed. The impact of baseline model complexity is then delineated. The LMS-based methods are able to track the non-linearity of the system to within 15% using this metric, with the error due primarily to filter convergence rates as the structural response changes regimes while undergoing the El Centro ground motion record. The use of a bi-linear baseline model for the SHM problem is shown to result in error metrics that are at least 50% lower than those for the tangential baseline model. Errors of 5–15% with this L2 error norm are fairly stringent compared to the greater than 2 × changes in stiffness undergone by the structure, however, in practice the usefulness of the results is dependent on the resolution required by the user. The impact of sampling rate is shown to be negligible over the range of 200–1000Hz, along with the choice of LMS-based SHM method. The choice of baseline model and its level of knowledge about the actual structure is seen to be the dominant factor in achieving good results. The methods presented require 2.8–14.0 Mcycles of computation and therefore could easily be implemented in real time. Copyright © 2005 John Wiley & Sons, Ltd.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.

Complex, high‐rate dynamic structures, such as hypersonic air vehicles, space structures, and weapon systems, require structural health monitoring (SHM) methods that can detect and characterize damage or a change in the system’s configuration on the order of microseconds. While high‐rate SHM methods are an area of current research, there are no benchmark experiments for validating these algorithms. This paper outlines the design of an experimental test bed with user‐selectable parameters that can change rapidly during the system’s response to external forces. The test bed consists of a cantilever beam with electronically detachable added masses and roller constrains that move along the beam. Both controllable system changes can simulate system damage. Experimental results from the test bed are shown in both fixed and changing configurations. A sliding mode observer with a recursive least squares parameter estimator is demonstrated that can track the system’s states and changes in its first natural frequency.

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters’ centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

An optical fiber with both temperature and strain fiber Bragg grating sensors were embedded into an aluminum cast structure during the casting process. Temperature and strain calibrations were carried out respectively for the metal-embedded sensors. Temperature and external strain decoupling was further demonstrated in a temperature range from 25 to 80 °C and an external strain range from 0 to ∼110 µɛ. With the interpolated temperature measured by two temperature sensors at different positions, the external strain could be decoupled from temperature and thermal strain at the strain sensor. The temperature and external strain values obtained from our embedded optical fiber sensors agreed well with reference values, revealing the good performance of the metal-embedded optical fiber sensors. The difference between the measured values and the reference values are within ±5 µɛ for external strain and ±1 °C for temperature. With only a single fiber, the in-situ temperature and external strain information in the aluminum structure can be monitored in real time, representing an important step towards fiber-optic smart casts. Our investigation demonstrates that embedded optical fiber sensors can be a promising method for structural health monitoring of metallic structures.

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption.

A structure's level of damage is determined using a non-linear model-based method utilizing a Bouc-Wen hysteretic model. It employs adaptive least mean squares (LMS) filtering theory in real time to identify changes in stiffness due to modeling error damage, as well as permanent displacements, which are critical to determining ongoing safety and use. The structural health monitoring (SHM) method is validated on a 4-story shear structure model undergoing seismic excitation with 10% uniform noise added. The method identifies stiffness changes within 0.5-1.0% inside 0.2-1.0 seconds at different sampling frequencies. Permanent deflections are identified to within 10% of the true value in 1.0 second, converging further over the remainder of the record.

Understanding features common to all forms of cancer is a vital component of treatment and research into malignant disease. All solid tumours share the ability to overcome contact inhibition of proliferation (CIP), the process by which intercellular contacts engage signalling to stop proliferation. Merlin, a tumour suppressor protein that is inactivated in a wide variety of cancers, plays a crucial role in CIP; merlin-deficient cells lose the ability to be contact-inhibited and subsequently form tumours. Although the role of merlin in cancer has been investigated extensively over the past 28 years, this protein has been notoriously difficult to study. To address this issue, we develop and utilize 2 split-luciferase biosensor systems that enable accurate quantification of merlin activity in real time. Merlin undergoes a conformational change that is functionally important in tumour suppression. Phosphorylation at a key C-terminal residue promotes transition of the protein from an open, active conformation to a closed, N-to-C terminal autoinhibited conformation. Therefore, monitoring this conformation change in real time could provide unique insights into merlin function. To do this, we apply NanoBiT split-luciferase technology to develop an intramolecular merlin biosensor (intra-Mer-BS). In brief, 2 split-luciferase components, LgBiT and SmBiT, are fused to the N- and C-terminus of merlin, respectively. Upon open-to-closed conformation change of merlin, LgBiT and SmBiT complement to reconstitute a functional luciferase and emit light. This enables accurate quantification of merlin’s functionally relevant conformation changes in real time. Importantly, cotransfection of the intra-Mer-BS alongside PAK1, an upstream merlin regulator that promotes transition to the closed conformation, significantly increases luminescent activity of the intra-Mer-BS, indicating that the biosensor faithfully reports on merlin’s conformation. Moreover, merlin has been shown to exert its tumour suppressive function through activation of LATS, the central mediator of the Hippo signalling pathway. In addition to the intra-Mer-BS, we develop and validate a NanoBiT biosensor to monitor the interaction between merlin and LATS (Mer-LATS-BS). This Mer-LATS-BS is used to quantify the effect of merlin activators and inhibitors on merlin/LATS tumour suppressive activity in cancer cells. In summary, we develop and validate 2 novel bioluminescent biosensors to monitor merlin’s conformation changes and activity in cancer cells. The intra-Mer-BS and Mer-LATS-BS provide real time information with high sensitivity and excellent reproducibility. Ultimately, these biosensors enable high throughput screening to discover novel upstream regulators of merlin in cancer and provide mechanistic insight into how contact inhibitive signalling is propagated through merlin and the Hippo pathway. Citation Format: Alexander J. Pipchuk, Xiaolong Yang. Development of ultrasensitive split luciferase biosensors monitoring activity of the merlin tumor suppressor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 858.

Floating wind turbines may encounter severe situations because of harsh environments. Higher cost of repair and maintenance of floating wind turbines have led researches to focus on damage detection methods that can prevent sudden failures. This paper presents an applicable method of damage detection and structural health monitoring for floating wind turbines based on the autoregressive moving average (ARMA) model and fuzzy classification. First, the dynamic model of a spar type floating wind turbine is constructed, by which the time responses of each degree of freedom of the system are acquired. With the system’s nonlinearity included, the intrinsic mode functions are obtained for the response signal. The Hilbert–Huang transform is applied and the appropriate measured signal for each degree of freedom is chosen for the ARMA modeling. In order to evaluate the proposed method, the ARMA parameters are first estimated for the undamaged condition then assumed damages are injected to the model and the ARMA parameters are once again estimated for the damaged condition. These parameters are considered as inputs for the fuzzy classification method. After training the system using the assumed damaged and undamaged conditions, the proposed method is simulated. Furthermore, the effect of measurement noise on the success rate is investigated. The results show that, in the presence of noise, the proposed method is able to identify the damage location and severity of mooring lines with acceptable success rate.

Guidelines for seismic performance evaluation of existing structures have been published by FEMA and the European Commission. Additionally, these organizations have presented frameworks for strengthening the resilience of society and industry. These frameworks require countermeasures for early recovery after natural disasters. For rapid post-earthquake recovery, it is necessary to assess the structural integrity and implement repairs. To achieve this, the utilization of structural health monitoring is essential. In structural damage assessment, loads and response displacements are important physical quantities, but these are difficult to obtain directly from structures. Therefore, in structural health monitoring, structural integrity is evaluated using measured accelerations. However, the superposition of uncertainties related to seismic motion and structures can sometimes complicate the setting of thresholds for structural integrity evaluation. To detect structural anomalies caused by earthquakes rapidly and with high accuracy, structural health monitoring methods incorporating deep learning have begun to be researched. Additionally, by extracting features from measured data using deep learning, prior setting of evaluation thresholds becomes unnecessary. Generally, training deep learning models requires large amounts of training data. However, since it is difficult to acquire anomaly data from existing structures, seismic response analysis is utilized. Typically, to represent real phenomena, analytical models require many degrees of freedom. In frequency domain response analysis, the frequency response function is defined as the ratio of the Fourier spectra of the structural seismic response to the seismic motion. By modeling the frequency response function, the seismic response of structures can be calculated. In this paper, we develop a method to calculate structural seismic responses by modeling the frequency response function with an AR model. This eliminates the need to set parameters such as degrees of freedom, mass, and stiffness of the analytical model. Furthermore, we propose a structural health monitoring method that uses this approach for training deep learning models. Verification of this method using experimental data from a full-scale steel building demonstrated that anomaly detection was possible from limited observational data only.

In the early design stage, frequent design changes are often made. In order to assist the designer to make sensible changes easily and quickly, a computer system should provide functions that model not only product geometry, but also some aspects of product properties such as stiffness in real time. Current Computer-Aided Design (CAD) systems mainly support product geometric properties and visualisation. Physical properties such as stiffness are difficult or even impossible to be perceived in existing CAD systems. In this paper, a haptic system is developed to evaluate the product physical properties such as product surface textures and stiffness that are directly related to customer comfort. A toothbrush design and evaluation case study is implemented to showcase how effectively the haptic system could help a designer in the early design stage. Since these physical properties of a product, traditionally evaluated based on physical prototypes, can now be perceived and customised in a virtual environment in real time, the product development cycle can be shortened and the cost in making prototypes can be reduced.

Focus on lasers, imaging, and microscopy

Structural Health Monitoring (SHM) seeks to evaluate the condition of a structure by instrumenting the structure with sensors and then using a combination of structural and data analysis. One unifying factor across all structures is the effect of temperature changes on the behavior of the structure. Temperature Driven - Structural Health Monitoring (TD-SHM) considers temperature as the driving force in structural behavior, and consequently the main input in SHM. Temperature as an input is related to strain and displacement as output to form three-dimensional temperature signatures for the structures. These signatures are descriptive of the thermal behavior, as well as sensitive to changes in structural condition. The two main benefits of TDSHM are the applicability to any structure that undergoes daily or seasonal temperature changes, and the possibility to measure temperature as an input for a complete input-output model. This work examines one type of three-dimensional signature: the relationship between changes in temperature with changes in strain and rotation or tilt on a beam-like concrete structure. The three-dimensional figure is fit with a surface to quantify the behavior, and changes in either the goodness of fit or in the fit itself are indicative of changes in structural behavior. These methods are validated using data from a real structure; Streicker Bridge on campus at Princeton University.

The Lamb wave inspection has emerged as a promising method for structural health monitoring and nondestructive testing. However, because of the highly dispersive and multimodal features, the Lamb wave mode separation has become a challenging problem. Based on the dispersion curve analysis, a new signal processing method is proposed in this study to solve this problem. First, a novel function based on the Hessian matrix is constructed to enhance the energy concentration of the dispersion curve in the time–frequency representation to reduce the dispersion effect. Subsequently, the constrained penalty function algorithm is developed for detecting dispersion curves. Finally, a mode reconstruction algorithm is developed to recover Lamb wave modes. The proposed method can separate overlapping wave modes and detect the crack fault by enhancing the time–frequency feature of the Lamb wave signal. Two experiments are carried out to verify the effectiveness of the proposed method for Lamb wave mode separation.