Structural Condition Monitoring Research Articles

Introducing Unmanned Aerial Vehicles to multi-criteria performance assessment of inspection techniques for port concrete infrastructure

Port infrastructure plays an important role in the economic and social activities of the regions they serve. Pressures induced by the corrosive marine environment and weather conditions, unexpected events associated with climate change, the usually high freight and vehicle loading conditions, the dynamic load of the equipment, and insufficient maintenance and rehabilitation measures that sometimes are not enough to repair an in-service facility, increase the susceptibility of port structures to rapid aging and deterioration. To ensure port infrastructure safety and reliability, and to maintain operational efficiency, port managers are tasked with forming successful inspection schemes for structural condition monitoring. Within this context, identifying the capabilities and prioritizing existing inspection techniques allow for optimizing resources (cost, time, personnel). The issue of performance assessment for commonly used Non-Destructive Testing techniques has received significant research interest. However, state-of-the-art Unmanned Aerial Vehicle techniques have not been previously involved in studies that assess the performance of various inspection techniques. Hence, the present research seeks to introduce Unmanned Aerial Vehicles techniques into multi-criteria performance assessment practices of inspection techniques for port concrete infrastructure. Three Non-Destructive Techniques: a) Infrared Thermography, b) Ground Penetrating Radar, and c) Impact-Echo, are compared to camera-based Unmanned Aerial Vehicle techniques linked to Close Range Photogrammetry applications. A qualitative approach, including a literature review and expert judgment, is applied to acquire data required for Multi-Criteria Decision Analysis practices with the Preference Ranking Organization METHod for Enrichment Evaluation methodology. The ranking output of the applied model is validated with other multi-criteria methods to ensure the robustness of the results. The obtained results for the specific set of criteria will assist decision-makers in selecting the most suitable technique for inspecting surface or sub-surface defects in port concrete infrastructure. Highlights The integration of UAV-driven techniques into current practices for assessing the performance of inspection techniques affects the ranking order The prioritization of the inspection techniques is conducted qualitatively (expert judgment and literature survey) by implementing the PROMETHEE method A camera-based UAV technique improves the practical part of inspections and provides flexibility A camera-based UAV technique is the optimal inspection technique for detecting surface defects, while Impact Echo is preferred for sub-surface defects

Automated modal parameter estimation for coupled rotor–foundation systems using seal forces as excitation source

Modal analysis techniques are widely used to characterize vibrations in structures, vehicles, machinery, etc. Modal parameters obtained through modal analysis are useful for structural health monitoring and condition monitoring. The common goal is to ensure the integrity of a mechanical system through monitoring of different operational conditions. Operational conditions described by spatial measurements can for instance be used to observe the vibration characteristics of the system and its parts. Therefore, the main originality of this work is to experimentally investigate if the presence of a turbulent gas flow in seal geometries can facilitate operational modal analysis for a coupled rotor–foundation system. A consistent and automatic way of obtaining the vibration characteristics is necessary to be included in the monitoring schemes. Therefore, this work also proposes a method to automatically obtain modal parameters for a bed plate of a rotor–foundation system when the rotor is affected by active magnetic bearings and gas seals. The automatic modal parameter algorithm is built on the stabilization diagram of modal parameters obtained from the stochastic subspace identification. Intermediate results from the automatic modal parameter algorithm show that some of the distance measures i.e., dωn,d, dζ, dMACX, dλ, dMACXωn,d, dMACXζ, and dMACXλ, for modal parameters in a stabilization diagram are better suited than others when distinguishing between possible physical modes and certainly spurious poles. The frequency band of interest for the system is found to influence the number of possible physical modes identified by the automatic algorithm. Measurements from 13 roving accelerometers are combined with the automatic modal parameter algorithm to obtain the vibration characteristics of the rotor and bed plate. For different operational conditions in the seal geometry, seven modes are consistently identified as physical modes by the automatic modal parameter algorithm. The algorithm uses (i) a combination of a white noise disturbance pattern intentionally introduced into the system through the active magnetic bearings and (ii) the operational disturbance coming from the seals, non-intentionally introduced by the turbulent gas flow. The seven physical modes are also identified when (iii) the rotor–foundation system is only disturbed from the turbulent gas flow in the seals. The seven physical modes identified by the algorithm under conditions (i), (ii), and (iii) compare well to the vibration characteristics found in experiments via experimental modal analysis and impact hammer testing as well as via a mathematical model of the rotor–foundation system.

Non-destructive methodology for crack detection using machine learning-assisted resonant sensor

Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures.

Thick Film Sensor Manufacturing Techniques for Realization of Smart Components via Low Pressure Die Casting

Recent developments like autonomous driving have increased the interest of the automotive industry in structural health and condition monitoring of safety-relevant components often manufactured via High or Low Pressure Die Casting (HPDC, LPDC). Specifically the latter process is important in this field as the improved quality of castings produced facilitates T6 heat treatments aimed at optimizing mechanical performance of such components. The specific challenge associated with this approach is that it produces critical thermal loads which may compromise the characteristics of the integrated sensor. The present study shows the results obtained by LPDC on thick film sensor systems prepared by means of screen printing on aluminum substrates. The response of the sensor systems to the thermal loads associated with the casting process is evaluated in casting experiments, with temperatures reached during casting checked by means of thermocouples attached to the inserts. The focus is on the influence on general properties of the piezoresistive sensors as well as on their response to thermal and mechanical stimuli. The results show that in contrast to HPDC, in the case of LPDC, the former provides stimuli for thermally induced processes which can compromise sensor layers. The respective trends have been studied via resistance measurements on samples subjected to repeated firing cycles and actual casting experiments.

Fault detection in beam structure using adaptive immune based approach

Different structural and machine elements are used over the ages. These are subjected to various loads like static and dynamic load, temperature, corrosion etc. Due to the above-mentioned reasons, ageing of the structural elements occur. So, to enhance the designed lifetime of any structure continuous maintenance is required. One such method has been proposed in this research work and the proposed method can be employed as an online tool for the fault identification. Here dynamic analysis of structure has been conducted as the forward method to find out the modal natural frequencies related with the damage. Recently with the application of machine learning approaches and the soft computing, the damage can be detected easily. In this methodology, Clonal Section Algorithm (CSA) has been applied to find out the faults (crack locations and depth) in the structure initially. Later one such method has been developed in the concepts of adaptive immune based technique (Adaptive Clonal Section Algorithm-ACSA) which is the combination of an artificial immune (Clonal Selection Algorithm) and Regression Analysis (RA). The use of regression analysis makes the proposed method more adaptive and the residual error in the collection of vibration data is reduced. The mechanism and various steps involved in CSA, RA and ACSA are analyzed here in a precise manner. The key endeavor of this study is the development of ACSA and its implementation to condition monitoring of structure. To authenticate and check the accuracy of both the methods (CSA and ACSA), laboratory tests are carried out. The results obtained from each method are corroborated with other and found to be convergent.

Framework to evaluate the value of monitoring-technique information for structural performance monitoring

The management of existing civil infrastructure is becoming more crucial as a large share of bridges is approaching their theoretical end of service duration. Structural performance monitoring aims to verify bridge safety at a given time, and it should be differentiated from structural health monitoring, which aims at detecting structural damage. Possible monitoring techniques include bridge load testing, non-destructive testing, and continuous monitoring of structural behaviour, environmental conditions, and load levels. Nonetheless, selecting the optimal combination of monitoring techniques is challenging as each method provides unique but also redundant information. This study proposes a framework to assess the value of information from multiple bridge monitoring techniques. This framework enables defining the appropriate set of monitoring techniques to ensure that the collected information will potentially correct engineering decisions regarding structural safety. A full-scale bridge in Switzerland is used for validating the framework predictions. Combining four monitoring techniques, the expected average increase of degrees of compliance of structural verification is estimated to 19%, which is consistent with the 36% obtained after performing these monitoring techniques. The methodology supports decision-makers in selecting an optimal combination of monitoring techniques for structural performance monitoring by maximizing the value of information.

Asset Assessment of Concrete Structure

Infrastructures which were constructed in the last century are approaching or have exceeded their design life. It is essential to maintain, repair and rehabilitate existing structures to ensure safety and develop a sensible rehabilitation plan. This paper suggests an inspection program for the condition evaluation of existing structures, particularly for structures that are approaching or exceeding their design/campaign lives. Cracking on concrete bridges is often induced by static and cyclic temperature loading. Throughout the service life of the structure, cracks will initiate, propagate, coalesce and form a greater damaged zone. Depending on the location and severity of the crack network; corrosion of reinforcement, debonding between concrete and reinforcement, and spalling of concrete may occur. The implementation of regular inspection or monitoring of structural condition is an essential component of asset management. Early detection of damaged zones can ease scheduling and reduce the cost of repair, which in turn also allows extension of structural lifespan without compromising safety. Similarly, for industrial properties, asset management and rehabilitation programs are paramount as they can significantly reduce capital expenditures in asset maintenance, if implemented prior to or at the early onset of structural degradation. This paper illustrates the use of NDT techniques for the condition assessment of a prestressed concrete bridge, as well as concrete storage silos. Both of which were either approaching or have exceeded their design campaign life and were needing a proper rehabilitation plan. The assessment included crack movement monitoring, crack depth determination, concrete strength estimation, and the detection of debonding between rebar and concrete.

Geoelectric Monitoring of Earthen Hydraulic Structure State by Resistivity and Induced Polarization Methods: Mine Water Settling Pond Dam Case Study

In earth dams, a permanent filtration of water leads to washing out of sand-clay fraction and to a formation of soil decompression sites, which pose a danger to embankment integrity. Condition monitoring of earth hydraulic structures can be executed by geophysical methods. The article presents the results of geoelectric monitoring conducted on the dam of settling pond of mine water with high metal content. The investigations were carried out by vertical electrical soundings, including electrotomography, and by methods of induced polarization in time and frequency domains. According to the results of the electrical soundings, places of reduced soil resistivity in the dam were identified, associated with infiltration of precipitations and of water from the pond. Geoelectric monitoring showed changes of the soils resistivity in different years, depending on hydrological conditions. Induced polarization methods are sensitive to material composition of soils, such as clayiness and presence of electronically conductive minerals. It is determined that the highest content of clay is in the upper and middle parts of the embankment. In eastern part of the dam, intensive polarizability of the medium was detected. It can be caused by filtration of water, contaminated with metals, through the embankment and sedimentary rocks. Thus, by resistivity measurements, it is possible to identify areas of intensive filtration in the dam body, and induced polarization measurements make it possible to determine clay content in the soil and possible pathways of contamination through the dam, which is of great importance for studying the environmental situation of region.

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R2 > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

Damage analysis in composite materials using a hybrid artificial immune system

Structural health diagnosis and condition monitoring of structures and components meant for aerospace industries is a challenging task for researchers and aerospace scientists. Presence of a minute crack in a structural element causes variation in local stiffness. The variations of local stiffness depend on crack location and crack depth. Change in stiffness in consequence affects the vibration behaviour of whole structural member. These abnormities in the structures if sustain over a long period cause catastrophic failure. Unexpected failure of any components of space vehicles results huge loss of life, more casualty and affect the economy of a country. To stay away from such situations, regular monitoring on safe operation of structures is extremely essential. Development of efficient damage detection methods is vital for early stage crack detection and its severity. In this work a novel online crack detection method based on artificial immune system has proposed. This article presents analytical and numerical (Finite element method) analysis for crack detection in a hybrid carbon fiber/ kevlar reinforced epoxy composite cantilever beam under free vibration. From the analysis, by varying crack sites and crack depth, alteration in natural frequencies and corresponding mode shapes curvature are recorded. Data (first three natural frequencies) from FEA has trained in negative selection algorithm (NSA). The result of FEA and NSA has compared. Then total average percentage of error is calculated. One statistical method has been used to make NSA more adaptive for the online damage detection. For this purpose a data mining technique named as “Regression Analysis” (RA) is used to reduce residual errors during the data acquisition method. The results are compared with standalone NSA and flexible NSA. Total average percentage of error has calculated.

Application of Acoustic Emission as Damage Assessment Technique for Performance Evaluation of Concrete Structures: A Review

A significant increase in existing inspection efforts and the creation of more resilient structures using built-in sensors and systems is essential for accurate and reliable assessment and monitoring of structural conditions in order to minimize the risk of catastrophic failure. The acoustic emission (AE) technique could immensely benefit the damage assessment, real-time checking of structural integrity under operating conditions, and identification of the operational risk factors along with the causes of initiation and development of flaws. Therefore, this article presents a comprehensive overview of AE as a potential damage assessment and localization technique to determine the performance and behavior of reinforced concrete (RC) members along with an overview of the latest research in computational AE damage modeling and machine learning tools to optimize the accurate interpretation of voluminous AE data. The primary aim of this review is to identify specific gaps in the literature pertaining to concrete structures that need immediate attention of the researchers working in this domain. The identification of damage mechanisms based on AE events and parameters covered in this review would enable researchers to identify damage sources nondestructively in situ and produce large data sets for detailed statistical analyses of the effects of small-scale local damages in large-scale concrete structures. This review will also be helpful for practicing engineers to choose between different available AE parameters based on the limitations and advantages of these parameters for specific inspection and maintenance requirements of various types of RC structural elements, loading conditions, and failure.

Recursive variational mode decomposition enhanced by orthogonalization algorithm for accurate structural modal identification

Modal identification is critical for structural condition monitoring. Variational mode decomposition (VMD) has been widely applied to identify modal parameters and has achieved excellent performance. It is crucial for VMD to predefine the decomposition parameters, that is, the mode number and balance factor. However, in practical engineering, abnormal impulses and heavy noise render it difficult to preset the mode number and balance factor. Therefore, a novel method, termed orthogonal and recursive VMD (ORVMD), is proposed to overcome the difficulty of setting decomposition parameters in advance. ORVMD consists of two components: recursive VMD (RVMD) and a rough-to-precise decomposition scheme based on an orthogonal algorithm. RVMD is an iterative method of VMD that is used to circumvent the difficulty of predefining the mode number. A rough-to-precise decomposition scheme based on an orthogonal algorithm is proposed to address the difficulty of setting the balance factor. Furthermore, the proposed ORVMD in combination with the Hilbert transform (HT) is employed to estimate the modal parameters of the structures. The raw signals are pre-processed by using the random decrement technique (RDT) to obtain its random decrement signature (RDS) and then the proposed method is applied to the RDS to identify the modal parameters of a simulated system and a real arch bridge. The obtained results show that the proposed method outperforms other existing methods in separating multicomponent signals; thus, it is an efficient method for identifying the natural frequencies and damping ratios of structures.

Landing gear condition monitoring based on back propagation neural network-based on multi-strategy cooperative optimization

To effectively monitor the operation state of landing gear, a back propagation neural network-based on multi-strategy cooperative optimization(MSCO-BPNN) is proposed. The multi-strategy optimization algorithm is composed of chaotic mapping strategy, adaptive spiral capture strategy, crossover mutation strategy and whale optimization algorithm(WOA). WOA is applied to find the optimal hyperparameters of back propagation neural network(BPNN). The search efficiency, multi-local search ability and global search performance of model can be improved by using chaotic mapping strategy, adaptive spiral capture strategy and crossover mutation strategy. The BPNN with optimal hyperparameters is introduced to establish the implicit model of input parameters and output responses. Based on quick access recorder(QAR) data, landing gear left side brake temperature is act as the monitoring objective of this paper. The validity and applicability of MSCO-BPNN are verified by compared with WOA-BPNN, particle swarm optimization BPNN and traditional BPNN. The results show that MSCO-BPNN can monitor the operation status of landing gear with high efficiency and accuracy. The efforts of this paper provide a promising insight for the precise condition monitoring of complex structures.

Theoretical Derivation of and Experimental Investigations into the Dielectric Properties Modeling of Concrete

Investigations into the dielectric properties of concrete form the basis for concrete quality testing and condition monitoring of internal structures when using ground penetrating radar (GPR). The formula used to describe the quantitative relationship between the dielectric properties of composite materials and the dielectric constant and volumetric ratio of each component is known as a dielectric model. In this study, a new dielectric model was deduced from circuit theory on the basis of structural abstraction to calculate the dielectric permittivities of concrete. To validate the applicability and accuracy of the proposed model, because concrete consists of matrix (cement mortar), aggregates (limestone), and air, the dielectric traits of concrete and its components were acquired using an open-ended coaxial probe. Compared with the Brown model, complex refractive index method (CRIM) model, Looyenga model, and Rayleigh model (i.e., the classical models), the results showed that among overall predictions of concrete dielectric constants, the proposed model has the greatest accuracy, because the theoretical results matched the test results more closely. This indicates that the proposed model can better interpret the dielectric properties of concrete, which lays the groundwork for future research into the application of GPR to the nondestructive testing and evaluation of concrete pavement.

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

The weak fault acoustic emission (AE) signals collected in the actual operating conditions of the engine are often submerged in the strong background noise. This paper proposes a denoising method of AE signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the above problem. Firstly, the wavelet packet is used to decompose engine background noise signals and noise-containing fault AE signals to enhance the local analysis capability of the autoencoder. Then, the dataset of each frequency band after decomposition is created. Among them, background noise signals are regarded as normal datasets. Noise-containing fault signals are treated as outlier datasets. The difference between each frequency band of background noise signals and noise-containing fault signals is analyzed. The autoencoder model is trained, validated and tested for effectiveness. In addition, a comparison is made with other commonly used denoising methods. Four types of evaluation indexes are introduced to quantitatively assess various methods. Finally, the real engine background noise signals with different signal-to-noise ratio (SNR) are added to the fault AE signals to verify the robustness of the proposed AE-WPD method. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different SNR. This lays the foundation for engine structural condition monitoring and subsequent fault identification and localization.

Condition Monitoring of the Unfinished Frame Building's Load-Bearing Structures in the Railway Zone

The current technical condition of the load-bearing structures of an unfinished frame building after downtime and under the influence of dynamic loading in the railway zone is considered. The construction under investigation was designed as a railway administrative building. The results of monitoring the technical condition of the elements of building structures of a frame building Real physical and mechanical characteristics of soils were studied on prototypes. The results of a visual survey and field measurements of foundations in control holes, surveys of foundation structures and characteristics of the soil base are presented. Calculations of the strength of concrete foundations and columns were done. Recommendations for the repair of foundations, taking into account the substantiated category of their technical condition have been developed.

Real-time drive-by bridge damage detection using deep auto-encoder

Structural health condition monitoring of bridge structures has been a concern in the last decades due to their aging and deterioration, in which the core task is damage detection. Recently, the drive-by method has gained much attention as it only needs several sensors installed on the passing vehicle. In this paper, we proposed an automatic damage detection method, which can be exploited in real time when the vehicle is passing the bridge. There are three steps in the proposed method: (1) The vehicle’s framed short-time vibrations instead of full-length data are utilized for training a deep auto-encoder model; at this stage, not commonly used time-domain accelerations of the passing vehicle, but its selected frequency-domain responses are employed to circumvent the influence of noises, (2) For the bridge with unknown health conditions, damage indicators can be extracted from its passing vehicle’s short-time vibration data using the trained model, and (3) The bridge’s health states are determined by real-time extracted damage indicators. To verify the proposed idea, a U-shaped continuous beam and a model truck are used to simulate the vehicle bridge interaction system in engineering. Results showed that the proposed method could identify the bridge’s damage with an accuracy of 86.2% when different severity was considered. In addition, it was observed that higher damage severity could not be revealed by greater values of damage indicators in the laboratory test. Instead, a novel index called identified damage ratios was employed as a reference for assessing the severity of the bridge’s damage. It was shown that with the increase in damage severity, the index would increase and gradually approach 100%.

Sensor Distribution Optimization for Composite Impact Monitoring Based on AR Model and LPP

The aim of this article is to provide a sensor distribution optimization method for the effective impact monitoring of composite plates with fewer sensors. In this research, the number of sensors and the minimum difference between categories are used as objective functions I and II, respectively, where the minimum difference is the Euclidean distance between different influence categories. The dual objective functions are defined by means of finite element analysis, the autoregressive (AR) model, and locality−preserving projection (LPP). The sensor distribution is optimized based on Multi−Objective Particle Swarm Optimization (MOPSO). Finally, an impact monitoring method is provided, and an experimental platform is built to verify the method. According to the optimization results, different grid sizes have a certain impact on the identification results, with the smaller the grid size, the smaller the minimum difference between categories. Within a given grid size, the minimum difference between categories increases with the increasing number of sensors. Experiments show that the higher the number of sensors, the higher the recognition rate of the system. Comparing the experimental results with the energy analysis of wavelet bands and PCA methods, it is found that the method used in this study has a higher recognition rate. This research provides an impact monitoring method based on sensor distribution optimization. And the effectiveness of the method is verified by experiments. It provides a useful reference and choice for the structure condition monitoring of composite material plates.

A novel physics-informed framework for reconstruction of structural defects

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process. However, the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results. Besides, the scattering signals collected from industry sectors are often noised and nonstationary. To address these issues, a novel physics-informed framework (PIF) for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper. Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions, a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals. This learning model is then adopted to assess and characterize the defect profiles in structures, improve the accuracy of the analytical model, and eliminate the impact of the noise pollution in the process of inspection. To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising, several numerical examples are carried out. The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method, and provides a valuable insight into the development of artificial intelligence (AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures

This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.