Health Monitoring Method Research Articles

Baseline-Free Detection Method for Change of Lateral Stiffness of High-Rise Building Based on Statistical Moment Curvature

In recent times, there has been a notable increase in the quantity of high-rise buildings, attributed to the swift advancements in both economic growth and construction technology. Assessing the structural integrity of high-rise buildings is important to ensure their safe operation. However, existing structural health monitoring methods typically require a baseline, involving either the measured dynamic and static responses from an intact structure or the finite element (FE) model corresponding to an undamaged state. These prerequisites are often challenging to acquire in practical scenarios. This study introduces a novel baseline-free method for detecting reduction in the lateral stiffness of high-rise buildings. The method is based on the statistical moment curvature (SMC) concept, determined through applying central difference to the second-order statistical moment of displacement. Initially, theoretical formulas were derived to demonstrate the viability of utilizing SMC for identifying reduction in the lateral stiffness of high-rise buildings. Subsequently, a FE model of a representative high-rise building was constructed to validate the proposed approach and assess its sensitivity, where different structural types and noise levels were considered. Lastly, a field test was conducted on a 33-story shear wall structure to provide additional validation for the proposed method. The results confirmed its effectiveness in accurately detecting reduction in the lateral stiffness of high-rise building. It offers two primary benefits: firstly, it obviates the need for a baseline, rendering it more convenient and applicable in real-world scenarios; secondly, its heightened sensitivity to sudden drops in lateral stiffness allows for early-stage detection of structural damage.

Research on white feather broiler health monitoring method based on sound detection and transfer learning

Livestock-vocalization-based health monitoring methods have the advantage of being contactless and non-invasive. In this study, the authors proposed a white feather broiler health monitoring method based on sound detection and transfer learning. The health of white feather broilers in the breeding area was determined by judging the frequency of coughs in the white feather broiler audios, and a quantitative evaluation index, cough rate, was newly proposed. Thus, the white feather broiler health monitoring problem is transformed into the multi-classification problem in machine learning, and training a reliable classification model becomes critical. First, large numbers of labelled white feather broiler audios were collected. Through signal filtering, sub-frame processing, pulse extraction and endpoint detection, the white feather broiler audios were transformed into labelled frame signals. Next, sixty sound features in multiple domains were extracted, and the labelled frame signals were converted into labelled data, thus created the data set. Then, multiple non-linear classification models were trained, the best performing classification model was performed parameter optimization, and a reliable classification model was obtained. Finally, the classification model was used to identify the sound types that appear sequentially in the unfamiliar white feather broiler audio. Based on this, the cough rate of the audio was calculated, and the health of the white feather broilers in the source breeding area was determined. In practice, there are differences in the vocalizations of white feather broilers at different days-old, and training classification models to identify white feather broiler audios of each day-old would result in a large resource loss. To address this problem, the authors considered setting white feather broilers of every three days-old as a collective, and attempted to train applicable classification models, respectively. Specifically, one of the collectives was selected, large number of labelled white feather broiler audios were collected, the master data set was created, and the conventional classification model was trained. It is based on the parameter-optimized random forest, which achieved a classification accuracy of 91.25%. For the other collectives, a small number of labelled white feather broiler audios were collected, and slave data sets were created, respectively. The improved TrAdaBoost multi-classification algorithm was used to filter instances from the master data set to each slave data set, the transfer data sets were created, and the transfer classification models were trained, respectively. They achieved a classification accuracy of approximately 83%. In addition, the definition of identification accuracy was newly proposed. Test results show that, the average classification accuracy achieved by the conventional classification model was 88.93%, and the average classification accuracy achieved by the transfer classification models was approximately 82%. This indicates the strong generalization ability of the classification models. The identification accuracies achieved by several classification models were all 100%. It demonstrates the feasibility and practicality of the method proposed in this paper.

Structural Condition Monitoring Using Deep Learning on a Metallic Part Fabricated by Additive Manufacturing

Additive manufacturing (AM) was originally developed to manufacture polymer prototypes. Today, it has been used for the manufacturing of many critical machine components. Most of the structural health monitoring (SHM) methods were developed for monitoring the condition of large and thin plates on airplane fuselages. Additively manufactured parts are generally small, thick, and have complex geometries. SHM methods have been improved to sense load, detect defects, and identify loose bolts with the help of a permanently installed sensor. In this study, the adaptability of SHM methods was researched with additively manufactured metal parts with complex geometry. Magnets were used to apply pressure to 9 different locations on the surface of a stainless steel additively manufactured thick plate with deep groves. SHM was used to estimate the magnets’ location. Many SHM (Lamb wave) methods cannot work on smaller parts since their dimensions are shorter or very close to the wavelength of the created oscillations. Surface response to excitation (SuRE) method which has similar characteristics to electromechanical impedance methods was used for data collection. To obtain descriptive features of the time domain data, fast Fourier transformation (FFT), short-time Fourier transformation (STFT), continuous wavelet transformation (CWT), and synchrosqueezing transform (SST) were applied to CWT. 1D and 2D convolutional neural networks (CNN) were used to classify the cases. When CNN was optimized for the analysis of our data, 100% location estimation accuracy was obtained by using 50% of 320 scalograms for training. The scalograms were obtained by enhancing the CWT results with SST. STFT-CNN combination was the second best. It obtained 95% accuracy with the same number of spectrograms and training allocation.

System identification and finite element model updating of a 6 MW offshore wind turbine using vibrational response measurements

Offshore wind energy is playing an increasingly vital role in the clean energy transition around the world, and improved reliability of wind turbine structures is necessary for the long-term success and efficiency of renewable energy. Increased reliability would reduce costs associated with maintenance due to breakages and in turn reduce the levelized cost of energy for offshore wind energy sources. Structural health monitoring methods can be used to predict breakages and extend lifetimes by continuously monitoring instrumented structures. This paper presents system identification and model updating of a 6 MW offshore wind turbine using vibration measurements under varying operational conditions. The turbine is monopile-supported and instrumented with strain gauges and accelerometers at several elevations along the tower and monopile. Effective stiffness of soil springs in the model are updated to match modal-predicted natural frequencies and mode shapes of the first two modes with those identified from measurements at different operating conditions. A deterministic and probabilistic (Bayesian) approach to model updating are compared. The sensitivity of identified modal parameters and the updated model parameters are investigated with respect to operational and environmental conditions such as wind speed. Results show that deterministic model updating can match modal parameters with high accuracy across datasets and environmental conditions. Bayesian model updating results successfully estimate the posterior distribution of updating model parameters with an increasing degree of certainty as more data is used.

Retracted: CYP1B1 Gene Polymorphism Based on Health Monitoring and Nursing Methods after Minimally Invasive Surgery for Lung Cancer.

[This retracts the article DOI: 10.1155/2022/7042014.].

Application of Vehicle-Based Indirect Structural Health Monitoring Method to Railway Bridges—Simulation and In Situ Test

In recent years, the vehicle-based indirect Structural Health Monitoring (iSHM) method has been increasingly used to identify the dynamic characteristics of railway bridges during train crossings, and it has been shown that this method has several advantages compared to traditional SHM methods. A major advantage is that sensors are just mounted on the vehicle, and no sensors or data acquisition systems need to be installed on the railway bridge. In this paper, the application of the vehicle-based iSHM method is demonstrated numerically and experimentally for determining the natural frequencies of railway steel bridges during train crossing. The coupled linear equations of motion of the train-bridge multi-body system are derived, and train crossing simulations are conducted numerically, considering different train speeds. Three different railway bridges are considered, and the train-induced vibration responses are calculated for both the train multi-body system and the railway bridge models. Different representative evaluation points are chosen for the wheelsets, bogies, and car bodies of the considered vehicle. To calibrate the numerical model, the resonance frequencies of an existing single-span steel bridge are measured in situ by the application of forced vibration tests. Besides the executed in situ measurements of the bridge, the considered crossing vehicle is also instrumented with several accelerometers at the wheelsets, bogies, and car bodies, and the vibration responses of both the bridge and the crossing vehicle are measured simultaneously during the duration of several train crossings with different train speeds. The recorded vibration responses are analyzed in the frequency domain and compared with numerical simulation results. It is shown that the first bending frequency of the considered railway bridge can be clearly identified from the computed frequency response spectra and that the vehicle-based iSHM method provides a promising tool for identifying the dynamic characteristics of railway bridges.

High-Speed Bearing Health Monitoring Method Based on Attention Mechanism Optimized Siamese Deep Residual Network

High-Speed Bearing Health Monitoring Method Based on Attention Mechanism Optimized Siamese Deep Residual Network

Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring

The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant risk to structural integrity and can lead to premature failure, but is hard to detect using existing nondestructive testing methods. Therefore, there is a need for an effective technique that can detect weak adhesion in single-lap joints (SLJ) to prevent failure and assist in maintenance, namely in the framework of structural health monitoring. This paper presents a novel approach utilizing machine learning and Lamb Waves (LW) to determine the level of weak adhesion. Firstly, a numerical model of SLJs with different levels of weak adhesion is created and an original approach is proposed for its validation with data from real samples so that reliable LW data can further be easily generated to train and test any other data-driven algorithm for tackling damage. Secondly, a damage detection method is proposed, based on artificial neural networks and fed with simulated data, to determine the level of damage in SLJs, independent of their location. The results show that the simulation model can be validated with a small set of experimental data, being capable of replicating real damage in SLJs. Additionally, the use of simulated data in the training algorithm can increase the accuracy of the simulation model up to 26% when compared to only considering experimental data. The adopted artificial neural network for detecting weak adhesion emerges as a promising approach, yielding a precision of over 95%. Thus, machine learning and LW data can be used to improve the reliability and accuracy of adhesive bonding quality control, as well function as a technique for structural health monitoring, which can enhance the safety and durability of bonded structures.

A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method.

The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and gbest selection and particle updating strategies, enhancing the reconstruction accuracy of iFEM for antenna truss structure and algorithm efficiency. The strain sensors optimization deployment on the antenna truss model for different postures is achieved, and the numerical results show that the optimization algorithm IALSCC proposed in this paper can well handle the high-dimensional sensor layout optimization problem.

Multi-spectrum fusion for single-probe blade tip timing

Blade tip timing (BTT) is currently the most promising method for online turbine blade health monitoring. Single-probe BTT minimizes the number of probes required and has the advantages of low cost and high safety. However, the number of probes in single-probe BTT makes its analytical capability significantly weaker than that of multi-probe BTT. In this study, we propose a multi-spectrum fusion (MSF) single-probe BTT signal analysis method that considerably improves the frequency analysis capability of the single-probe BTT and provides a theoretical upper bound for the frequency analysis. The feasibility and robustness of the MSF are verified through numerical simulations and experiments. In addition, we present methods to determine the number of frequency components of the BTT signal, which further reduces the dependence of MSF on a priori information. Finally, we demonstrate that the single-probe BTT is almost identical to the multiprobe BTT in terms of analyzing the asynchronous vibration of a blade.

A Strain Transfer Model for Detection of Pitting Corrosion and Loading Force of Steel Rebar with Distributed Fiber Optic Sensor.

Steel rebar corrosion is one of the predominant factors influencing the durability of marine and offshore reinforced concrete structures, resulting in economic loss and the potential threat to human safety. Distributed fiber optic sensors (DFOSs) have gradually become an effective method for structural health monitoring over the past two decades. In this work, a strain transfer model is developed between a steel rebar and a DFOS, considering pitting-corrosion-induced strain variation in the steel rebar. The Gaussian function is first adopted to describe the strain distribution near the corrosion pit of the steel rebar and then is substituted into the governing equation of the strain transfer model, and the strain distribution in the DFOS is analytically obtained. Tensile tests are also conducted on steel rebars with artificially simulated corrosion pits, which are used to validate the developed model. The results show that the Gaussian function can be used to describe the strain variation near a corrosion pit with a depth less than 50% of the steel rebar diameter, and the strain distribution in the DFOS analytically determined based on the developed strain transfer model agrees well with the tensile test results. The corrosion pit depth and loading force in the steel rebars estimated based on the proposed model agree well with the actual values, and therefore, the developed strain transfer model is effective in detecting pitting corrosion and loading force in steel rebars.

Monopile Foundation Stiffness Estimation of an Instrumented Offshore Wind Turbine through Model Updating

Rapid development of offshore wind foundation models has resulted in a large number of built structures with generally underestimated foundation stiffness properties and a need to update and validate both the individual structural models and the underlying foundation design frameworks. This paper outlines a structural health monitoring approach, based on the combination of output only structural health monitoring methods and model updating, to estimate foundation stiffness parameters using field monitored data. Field monitoring data from an offshore wind turbine under idling conditions, over a large monitoring period, are presented and operational modal analysis is applied to estimate the modal parameters. Those are compared to modal properties predicted by finite element models, employing either old (API/DNVGL) or new (PISA) foundation design properties, which are calibrated using geotechnical site investigation data. A new approach to interpret seabed level statically equivalent foundation stiffness, in terms of effective lateral and rotational stiffness against load eccentricity, is presented. Seabed level statically equivalent foundation properties are updated by comparison against the observed modal behaviour and the optimised foundation parameters are presented, demonstrating a close match to the predictions of the PISA method.

Piezoelectric ceramic sensor-based structural health monitoring of diagonally braced H-shaped steel structures

In this study, we propose a piezoelectric ceramic sensor-based health monitoring method to monitor the stress state of diagonally braced H-shaped steel structures. Loading experiments were carried out on diagonally braced H-shaped steel under different working conditions. Subsequently, the amplitude and energy of piezoelectric signals under these two working conditions were compared and analyzed, and finite element analysis was performed using ABAQUS software to verify the results. The experimental results showed that with an increase in the web height or load, the time-domain waveform energy index of the H-shaped steel increased. Under different working conditions, such as a diagonally braced H-shaped steel member with a web height of 10 cm, when the pressure value was less than 10 N/mm2, the energy index increased by approximately 15.98% for every 1 N/mm2 increase in the pressure. When the pressure value was greater than 10 N/mm2 and less than 15 N/mm2, the energy index increased by approximately 1% for every 1 N/mm2 increase in pressure. Further, the energy index increased by approximately 8.4% for every 1 cm increase in the height of the web. Simultaneously, it can be seen from the results of the finite element analysis that the stress and strain at the induction position of the piezoelectric ceramic sensor increased with an increase in the external pressure. The study of structural health monitoring for diagonally braced H-shaped steel structures holds significant importance in ensuring the safety and reliability of these structures, achieving predictive maintenance, evaluating structural performance and energy efficiency, and optimizing structural maintenance.

Health-monitoring methods for long-span cable-stayed bridges

It is important to consider the impact of non-uniform excitations on long structures, including bridges. This study investigates how the dynamic behaviour of a cable-stayed bridge is impacted by multiple sources of support excitation. The bridge is simulated by using the Abaqus software. The results indicate that various parameters, including cable stresses, tower and support base shear, and the influence of multiple sources of support excitation on a bridge, lead to significant changes in displacements. For instance, in non-uniform excitation mode, the two end supports display an average enhancement of approximately 25% in comparison with the uniform excitation mode; for the towers, the average increase is 23%. Moreover, an examination is conducted into the effectiveness of various health-monitoring techniques, such as mode-shape curvature and energy index (EI), to find the damages on the bridge, as bridges are important infrastructures and monitoring them during their operation could save lives and costs. In this regard, the Matlab software is used and the stiffnesses of some parts of the deck are decreased as intentional damages. After comparing the methods mentioned, the research indicates that using the EI based on strain responses is a highly effective means of detecting damage.

Energy Saving Structural Health Monitoring Using Semi-Active Identification

This paper presents a novel approach to achieving system identification of a structure while minimizing energy consumption. The identified structural model can be used for structural health monitoring. In the aerospace environment, energy consumption is strictly regulated. To address this issue, we propose an energy-saving identification method that utilizes piezoelectric semi-active control as an input generation technique. This approach generates control force through electric switch activation, resulting in a smaller amount of energy consumption for input generation than conventional active control. We achieved semi-active input generation suitable for identification by incorporating a novel control strategy. The semi-active control has the disadvantage of limiting the free control of inputs. The identification performance may degrade if the properties of the semi-active input deviate from the desired ones. To address this issue, we also propose a data processing method that extracts a certain input with appropriate properties for identification from the acquired input. We validated the proposed method through numerical simulations and experiments. The results confirmed the feasibility of the semi-active identification method for structural health monitoring. Additionally, we found that the total energy consumption during the 20-second experiment was only 68 mJ to identify the 50 kg structure.

Parametric analysis of RSB sensors for concrete strength monitoring using hybrid EMI and WP techniques: Numerical investigation

The strength of concrete after its casting increases with time up to a certain limit due to continuous hydration reactions in the cement matrix. To measure the rate of strength development and ultimate capacity of concrete, a novel health monitoring method using Reusable smart bolt (RSB) piezoelectric sensor is proposed in the present paper. The smart piezoelectric Lead zirconate titanate (PZT) patch-based Electro-mechanical impedance (EMI) and Wave propagation (WP) techniques are utilised for accessing the health of concrete at 1, 3, 5, 7, 14, 21 & 28 days of concrete hydration. Firstly, the PZT patch-based sensing capabilities are validated with the experimental results from literature by modelling concrete cube for EMI and beam for WP results. Then, a total of 31 finite element models of concrete cubes having different RSB configurations were taken into consideration for sensor design and optimization. In EMI technique, the shifting of conductance signatures and relative resonance frequency are measured, whereas in WP technique, the shift of P-wave velocity peaks between actuator and sensor is estimated for all the models. The sensitivity of outputs is measured by plotting statistical Root mean square deviation (RMSD) index which proven the efficacy of employing RSB sensors for monitoring concrete strength-development during early-hydration ages with good correlations. Overall, the EMI-identified RMSD plotted for conductance shows 327 % more sensitivity than WP-identified relative change of P-wave velocity in monitoring concrete strength development using RSB sensors.

Probabilistic residual strength assessment of smart composite aircraft panels using guided waves

Typical prognosis methods in Structural Health Monitoring (SHM) link the degradation mechanisms to the extracted health indicators. Such approaches however do not provide estimations on the current strength loss when a new damage is detected. This study presents a framework that enables the SHM-informed asset integrity management of composites. First, the integrated guided wave based SHM system is used to inform on the existence, location and size of the damage and subsequently, the residual strength of the structure is predicted. The framework combines physics-based and data-driven models to mitigate the incompleteness of the former and address issues relating to the representativeness in the training datasets of the later. Detailed finite element models are used to create a digital representation of the structure while a building-block program is initiated to quantify and propagate the uncertainties observed experimentally. A 1.6 m composite panel with a skin-stringer delamination, equipped with a network of 24 piezoelectric transducers, is used as a case study to demonstrate the prognostic capabilities of the framework. The framework estimated the damage size with a mean absolute percent error (MAPE) of approximately 10% while the residual strength of a destructively tested damaged panel was predicted with a MAPE of 5%.

Virtual Axle Detector: Train Axle Localization based on Bridge Vibrations

AbstractInfrastructure worldwide is facing the challenge of aging bridges and increasing traffic loads. Prolonged serviceability and safety of these structures can be enabled by Structural Health Monitoring (SHM) methods. Knowledge of the actual operating loads is critical for evaluation of the remaining service life. However, direct measurement of the loads is challenging and requires a significant financial investment. Bridge Weigh‐In‐Motion (BWIM) methods use the structural response of bridge structures to determine loads, but generally rely on accurate knowledge of the position of loads as a function of time. Positions can be determined using conventional axle detectors, but their lifetime is limited, and their installation is expensive. To avoid these problems, we propose an improved Virtual Axle Detector (VAD) with Enhanced Receptive field (VADER) that can detect axles for all bridge types using accelerometers that can be placed anywhere along the bridge. The same data set with 3787 train passages recorded on a steel trough railway bridge under real operating conditions was used. Our results show that, in comparison with VAD, VADER reduces the number of undetected axles by over 79% and detects 99.5% of axles with an average spatial accuracy of 4.6 cm.

QUICK‐B‐WIM: Large scale application of a moving force identification method on a railway bridge

AbstractGlobally, infrastructure faces the challenge of aging bridges and increasing traffic loads. Extended operational availability and safety of the bridge structures can be enabled by Structural Health Monitoring (SHM) methods. For this, knowledge of the actual vehicle loads is of crucial importance for evaluation of the remaining service life. Direct measurement of the moving loads, however, is either very cumbersome and requires considerable financial effort or in some cases even impossible. Bridge Weigh‐In‐Motion (B‐WIM) methods use the structural responses of bridge structures to determine the external vehicle loads. The present contribution deals with the testing of a novel moving force identification (MFI) method, denoted as Quick‐BWIM (QBWIM) under real operating conditions. In this study, 43 train crossings within 18 hours were investigated and compared with a nearby axle load measurement point. QBWIM achieves an average error of only 1.1% with a standard deviation of 7.5% for individual axles and an average error of 2.1% with a standard deviation of 1.5% for the identification of gross vehicle weights (GVW). This study shows that QBWIM can meet European and US guidelines even under poor conditions and is able to reliably determine axle loads in real time.

Design of Smart Cable for Distributed Cable Force Measurement in Cable Dome Structures and Its Application

The stay cable is one of the most critical structural components of a cable dome structure. However, during its service life, it may lose its stiffness due to environmental factors and metal fatigue, thus making the structure a safety hazard. As the most important mechanical physical parameter of the cable, it is necessary to create a health-monitoring method to ensure the safety of the structure. In this study, a smart cable with a fiber optic Bragg grating (FBG) sensor is proposed. The sensor is embedded in the Z-shaped cable of the stay cable to ensure the simultaneous deformation of the sensor and cable. The monitoring of the cable force can be achieved after obtaining the relationship coefficient between the sensor and the cable force. In the rest of the paper, the sensing principle and fabrication procedure are described. A series of tests are conducted to verify the sensing performance of the smart cable. Finally, the dynamic monitoring and long-term monitoring of the cable force in the cable-supported grid system of Dalian Suoyuwan Football Stadium are carried out by using the smart cable, and the stability and safety of the structure are evaluated by the monitoring results.