Damage-sensitive Features Research Articles

Detectability of Structural Damage to a Lattice Tower using Eigenfrequencies and Gaussian Process Regression

One main challenge in structural health monitoring is distinguishing between the effects of actual system changes and changing environmental conditions (EC) on the monitored parameters. For this, data normalisation can be performed. Through the utilisation of Gaussian process (GP) regression, it is possible to map the influences of the ECs on the monitored parameters and simultaneously obtain confidence intervals of the predictions. The assumption of standard GPs is homoscedasticity, meaning a constant noise variance of the parameters. However, there is still a lack of research regarding the correctness of this assumption for different types of structures and damage sensitive features. Furthermore, the potential of normalisation techniques that enable the consideration of EC-dependent uncertainties should be analysed. In this real-world case study, damage detectability is investigated using GPs and eigenfrequencies. In contrast to standard homoscedastic GPs, heteroscedastic GPs can account for input-dependent uncertainties. Both methods are compared using real data from a lattice tower. Firstly, it is shown that the homoscedastic assumption does not hold for the eigenfrequencies of the lattice tower. Consequently, the heteroscedastic GPs enable more reliable damage detection than homoscedastic GPs when using a probabilistic novelty metric. Also, in the heteroscedastic case, the additional information on the uncertainty of the prediction increases the interpretability of the results.

Uncertainty quantification for damage detection in 3D printed auxetic structures based on ultrasonic guided-wave using Flipout probabilistic convolutional neural network

Abstract The auxetic structure is widely used in aviation, bio-engineering, automation, and other industries due to its outstanding properties, such as being lightweight, high strength-to-weight ratio, and absorbing energy. Neural network methods have been popularly used for auxetic structures’ structural health monitoring. However, the performance of neural network methods in unknown areas is limited. To increase the reliability of the network model, more comprehensive uncertainty quantification is needed for damage detection in unknown areas. This paper introduces a comprehensive framework for health diagnosis and uncertainty quantification in 3D-printed auxetic structures made of polylactic acid. The framework involves quasi-static uniaxial compression and ultrasonic tests conducted simultaneously to capture ultrasonic signals at different deformation states. Critical damage deformation is identified based on observed deformation patterns and variations in signal energy. Using the Hilbert transform, two damage-sensitive features—envelope and phase—are extracted. These features serve as input data for the Flipout probabilistic convolutional neural network (FPCNN) model, which integrates pseudo-independent weight perturbations and a Gaussian probabilistic layer within the visual geometry group 13 architecture to predict structural deformations and associated uncertainties. The uncertainty quantification framework, based on variational inference and the conditional covariance law, effectively separates and quantifies the predictive variance of the network model into aleatoric and epistemic uncertainty. This framework’s feasibility is demonstrated through compression and ultrasonic tests, utilizing the FPCNN. This showcases the uncertainty quantification capabilities of the FPCNN model.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

Damage-Sensitive Feature for Long-Span Steel Box-Girder Suspension Bridges Under Service Loads Based on Long-Term Monitoring

To extract valuable data from long-term monitoring and identify structural damage to ensure the serviceability and safety of bridges, a commonly used method in large bridge maintenance. This article developed a time-domain energy-based method for identifying bridge damage based on long-term structural health monitoring noisy output measurements. This method first calculated an energy threshold within the structural frequency bandwidth of natural vibration at measurement points. Then, the structural damage was assessed by computing the time-domain energy accumulation. The improved procedure of this proposed method involved extracting modal frequencies from the signal for each modal order, determining the structural frequency bandwidth of natural vibration, and applying the square root envelope method to smooth vibration data and mitigate the impact of abnormal signals. Later, the applicability of the proposed method for damage identification was verified by comparing it with conventional modal flexibility and modal curvature methods. Finally, a case study on the Runyang bridge, a supported steel-box-girder suspension bridge with a main span of 1490 m in China, was made for demonstration. The results indicated that the area between 1/4-span and 1/2-span of the bridge was more sensitive to damage under service loads. The method performed well in identifying potential damage locations and assessing vulnerability. The calculation results using the method proposed in this article were consistent with those of traditional methods. The research offers methodological backing for utilizing extensive data monitoring in evaluating structural damage.

A mode shape sensitivity-based wavelet feature extraction method for interface debonding detection in concrete-filled steel tubes

Abstract The use of concrete-filled steel tube (CFST) composite columns is increasingly prevalent in the construction industry, particularly in high-rise structures. A common issue in CFST columns is interface debonding between the concrete core and the steel tube. If this debonding progresses both superficially and deeply, it can lead to instability and buckling of the column, posing a serious threat to the overall structural integrity. This study presents an innovative and effective method for extracting damage-sensitive features using horizontal, vertical, and diagonal detail coefficients derived from the wavelet analysis of corrected modal signals. The study introduces the total normalized irregularity detection index (NIDIT) as a damage detection metric. The results indicate that NIDIT is highly effective in identifying and detecting debonding regions. NIDIT quantifies the accumulation of irregularities and disturbances in the affected areas, allowing for the detection of concrete surface debonding from the steel tube. The findings show that NIDIT can accurately and efficiently detect damage in middle and end-edge regions, addressing a significant challenge in structural health monitoring with high precision.

Estimation of the Remaining Useful Life of Axially Loaded Members Through Nested Least-Square Support Vector Regression

ABSTRACT Prognosis and Health Management (PHM) is crucial for ensuring the reliable operation of structural components, such as bracing members. Despite this, limited study exists for the RUL estimation of civil engineering components. This paper presents a nested framework utilizing the Least Square Support Vector Regression (LS-SVR) for accurately predicting the Remaining Useful Life (RUL) of axially loaded members. The framework addresses the challenges of sparse data and multidimensional damage-sensitive features (DSFs) by employing a two-stage process. In the first stage, DSFs are forecasted using one-dimensional mapping, providing essential inputs for the second stage, where axial stiffness degradation is predicted using LS-SVR. The proposed framework is validated through an experimental study on axially loaded members subjected to low-cycle fatigue. To enhance model performance, simulated data generated from an OpenSees model is combined with the experimental data. The nested LS-SVR framework demonstrates superior performance compared to traditional methods, achieving high accuracy in RUL estimation as evidenced by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The framework’s ability to forecast axial stiffness degradation, a critical indicator of structural health, makes it a valuable tool for real-time assessment of structural component RUL. By integrating this framework with real-time monitoring systems, proactive maintenance strategies can be implemented, leading to safer and more efficient structural health management.

A novel damage index for crack identification of a drilling riser during deployment based on the modal vibration energy flow

Early detection of structural damage especially small cracks in marine deepwater drilling risers is of paramount importance. Small cracks are always hard to detect because of weak vibration signals if using traditional damage indices, such as natural frequencies and mode shapes including modal displacements, cross-section slopes, and curvatures. Especially when the deepwater riser is during deployment, the variable suspension riser length and strong sea environment noise make the small crack identification more difficult. To address this problem, this study formulates a new concept of damage detection by integrating the vibration energy flow theory into the vibration-based damage detection (VDD) technique. A novel damage-sensitive feature (DSF) named modal vibration energy flow (MVEF) is established for detecting and locating the small cracks in deepwater drilling risers. The capability of the approach is verified by using the same riser model and the numerical results in the literature. The sensitivities versus crack positions and depths, variable suspension riser length, and sea noise are studied in detail. Results show that the proposed index MVEF is more sensitive than the classic traditional DSFs, i.e., modal slope, modal strain (or curvature), and modal strain energy. In total, the MVEF approach can accurately identify the small cracks and is robust against noise interference, suitable for detecting and locating small cracks in the deepwater drilling riser during deployment.

A multitask SHM algorithm to identify damage with random severity and location in IPE beams using EMI technique

Existing electromechanical impedance (EMI) damage identification algorithms often face challenges in terms of generalizability. This paper presents a robust algorithm that can simultaneously estimate the region and severity of damage with random damage scenarios across a surface and any severity, rather than being limited to specific points and severities. The host structure was an I-beam. Simulated damage was introduced as a subtle added mass to evaluate the algorithm's effectiveness in early-stage damage identification. Initially, various EMI tests with different damage specifications were conducted, and validated through numerical simulation. Damage-sensitive features were extracted and were input into three ML models: support vector machine, random forest, and multilayer perceptron. An ensemble learning approach was employed to combine the individual predictions from these models. The algorithm achieved classification accuracies of 97.3 % and 94.4 % on the validation and test sets, respectively, for identifying damaged regions. The algorithm also quantifies damage severity, achieving R-squared values of 92 % and 88 % on the validation and test sets, respectively.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Structural damage detection based on transmissibility functions with unsupervised domain adaptation

Deep learning (DL) has been used for structural damage detection by training neural network models with a large amount of data. These trained models perform well when the test samples are from the data with an identical distribution of the training data. The distributions are always different due to numerical modelling errors and operational environmental varieties, and the data for damage scenarios is difficult to be obtained in practice. A novel method based on the joint maximum discrepancy and adversarial discriminative domain adaptation (JMDAD) for structural damage detection without labelled measurement data has been developed to address the above issues. To reduce the influence of external excitations in practice, the transmissibility function of measured structural responses is used for structural damage detection. The proposed network includes a feature generator, two classifiers and one discriminator. Firstly, the feature generator and domain discriminator are to extract features and merge their distributions at the domain level to overcome the issue of insufficient data from the target real structure. Secondly, the generator and two classifiers are optimised to align their distributions at the class level using the classification discrepancy between the two classifiers. As a result, the damage-sensitive features are extracted and aligned to eliminate modelling errors and operational environmental varieties between the source and target structures. Three case studies have been conducted in this paper, e.g., one case between two building structures with different storeys, another case between the numerical and experimental structures subjected to random and earthquake excitations and the application of Canton Tower. The results show that the proposed method is much robust to the measurement noise and operational environment and the structural damage is identified accurately without labelled measurement data from the target structure in operational environments.

Integrated Estimation of Stress and Damage in Concrete Structure Using 2D Convolutional Neural Network Model Learned Impedance Responses of Capsule-like Smart Aggregate Sensor.

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA's EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress-damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure.

Efficient data-driven nonlinear system identification for structural health monitoring: A proof-of-principle study

Structural health monitoring (SHM) is the process of detecting damage in structures. Typically, the damage-sensitive feature identification is based on fitting some models to the measured system response data. The parameters of these models or the prediction errors associated with these models then become the desired damage-sensitive features. Real-world structures usually exhibit significant nonlinear behaviors. Data-driven nonlinear system identification (NSI) is essential to obtain accurate models of structural dynamics for reliable damage detection. However, on the one hand, the data acquisition of real-world structures for damage detection is usually difficult or scarce. On the other hand, NSI generally needs to solve a nonlinear optimization problem that requires more computational time for convergence than linear system identification. Data efficiency and computational efficiency become two critical challenges in data-driven NSI. To address these challenges, this work presents a proof-of-principle study on developing a meta learning-based method for efficient data-driven NSI. Specifically, the meta learning algorithm leverages a previously collected database of similar but different systems to learn the meta-knowledge about how to identify a new system, enabling a fast identification/modeling with limited data. Numerical simulations are performed to validate the method on fundamental nonlinear dynamical systems widely seen in civil engineering. It is observed that the presented meta learning-based method outperforms the conventional method in terms of both data and computational efficiency. Limitations of this work are further discussed.

Confounder-adjusted covariances of system outputs and applications to structural health monitoring

Automated damage detection is an integral component of each structural health monitoring (SHM) system. Typically, measurements from various sensors are collected and reduced to damage-sensitive features, and diagnostic values are generated by statistically evaluating the features. Since changes in data do not only result from damage, it is necessary to determine the confounding factors (environmental or operational variables) and to remove their effects from the measurements or features. Many existing methods for correcting confounding effects are based on different types of mean regression. This neglects potential changes in higher-order statistical moments, but in particular, the output covariances are essential for generating reliable diagnostics for damage detection. This article presents an approach to explicitly quantify the changes in the covariance, using conditional covariance matrices based on a non-parametric, kernel-based estimator. The method is applied to the Munich Test Bridge and the KW51 Railway Bridge in Leuven, covering both raw sensor measurements (acceleration, strain, inclination) and extracted damage-sensitive features (natural frequencies). The results show that covariances between different vibration or inclination sensors can significantly change due to temperature changes, and the same is true for natural frequencies. To highlight the advantages, it is explained how conditional covariances can be combined with standard approaches for damage detection, such as the Mahalanobis distance and principal component analysis. As a result, more reliable diagnostic values can be generated with fewer false alarms.

Uncertainty quantification for damage detection in 3D-printed auxetic structures using ultrasonic guided waves and a probabilistic neural network

Auxetic structures hold significant potential for applications due to their outstanding properties. Ultrasonic waves and neural networks are the popular technologies used for structural health monitoring (SHM). To increase the reliability of the neural network output for SHM, comprehensive uncertainty quantification is needed for damage detection in unknown areas of auxetic structures. This paper presents the first comprehensive framework for health diagnosis and uncertainty quantification based on ultrasonic guided waves in two 3D-printed star hourglass honeycomb auxetic structures. The proposed framework integrates in-plane compression with simultaneous ultrasonic testing to receive ultrasonic signals across various deformation states. Additionally, fully elastic and elasto-plastic finite element simulations are conducted to analyze wave energy variations and mechanical responses in auxetic structure. Critical damage deformation is identified based on observed deformation patterns and variations in signal energy. The Hilbert transform is used to extract two damage-sensitive features, namely envelope and phase. These features serve as input data for the Flipout probabilistic convolutional neural network (FPCNN) model, which integrates pseudo-independent weight perturbations and a Gaussian probabilistic layer within the visual geometry group 13 architecture to predict structural deformations and associated uncertainties. The UQ framework effectively separates and quantifies the predictive variance of the FPCNN model into aleatoric and epistemic uncertainty. The framework’s effectiveness is demonstrated through the comprehensive approach, combining compression and ultrasonic tests, finite element simulation, and the FPCNN technique.

Bridge damage location and quantification under the moving vehicle loads based on deep learning multi-objective regression

The extraction of damage-sensitive features based on deep learning for the vibration response caused by vehicle-bridge interaction (VBI) has become a mainstream method in damage identification. However, most studies remain at the stage of qualitative damage assessment, not achieving precise numerical quantification of damage. They also typically involve multiple frequent runs using only a single dedicated vehicle. In this study, utilizing a deep neural network designed for multi-objective regression tasks, a novel method for bridge damage localization and quantification is presented with a multi-objective regressor designed to create a direct mapping between the curve of bridge deflection in time domain and the damage information matrix. The final quantitative damage values are obtained through statistical analysis. The method is evaluated on a simulated data set generated from VBI simulation using various 3D heavy vehicle models. The results demonstrate that this method can accurately locate and quantify damage even with unseen vehicle load conditions, damage scenarios, and measurement noise. The structure and depth affect the effectiveness of extracting damage-sensitive features from time-domain deflection during training. The study shows potential for real-time damage identification under normal operating conditions of real bridges in the rapid development of intelligent transportation systems.

An RFE-aided Transformer-SVM framework for multi-bolt connection loosening identification using wavelet entropy of vibro-acoustic modulation signals

To ensure structural safety and integrity, a novel framework is developed for detecting the loosening of multi-bolt connections using wavelet entropy of vibro-acoustic modulation (VAM) signals. Wavelet entropy is employed as the dynamic index to capture the intricate time-frequency characteristics that are indicative of the connection status. Taking the wavelet entropy vectors as input, the proposed framework distinguishes itself by integrating a Transformer model for high-dimensional feature extraction with the recursive feature elimination (RFE) for essential feature selection, followed by a support vector machine (SVM) model for classification. Specifically, the Transformer model with innovative positional encoding capability helps to extract the time-dependent transient features that are sensitive to the bolt loosening. The RFE process reduces the data dimensionality while discerning the diagnostic information for more accurate classification. Through the experiment on a four-bolt joint, the identification results with cross-validation showed high accuracy and robustness of the proposed framework across various loosening cases. It outperformed the traditional SVM, long short-term memory network (LSTM), convolutional neural network (CNN)-SVM models without and with RFE, as well as the Transformer-SVM model without RFE, achieving an accuracy increase of 15.72%, 11.74%, 9.47%, 5.49%, and 5.06%, respectively. The proposed framework was demonstrated to be able to learn the damage-sensitive features more effectively from wavelet entropy data, marking a significant advancement in the health monitoring of engineering structures with high-strength bolt connections.

Structural damage identification by using physics-guided residual neural networks

In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.

Quantitative characterization of fatigue damage in plate structures based on FSOM

For the problem of fatigue damage detection and damage degree assessment of plate structures, a quantitative damage assessment method based on the fast self-organizing feature mapping (FSOM) algorithm is proposed in this paper. The damage detection problem is transformed into a binary classification problem by extracting multidimensional damage features of the Lamb wave signal in plate to be detected and selecting damage sensitive features. Then, the FSOM network is used to identify the health state of the plate to be inspected, and the damage index is obtained by fusing the damage sensitive features using FSOM to quantitatively evaluate the damage level of the plate to be inspected. Simulation and experimental results show this method has a good dynamic tracking capability for the fatigue damage evolution of aluminum and composite plates, and can achieve quantitative assessment of fatigue damage of plate structures.

Optimal Sensor Placement with Minimal Predicted Posterior Variance

The performance of each monitoring system critically depends on the sensor layout and typical optimization criteria are based on the accessibility to the structure on site, experience with similar structures, and an optimal signal-to-noise ratio. More advanced criteria aim to optimize the feature extraction process, and this paper aims to develop a sensor placement strategy for optimal damage diagnosis. The approach is based on Bayesian updating and the optimization goal is to find a sensor layout that is the most sensitive to small changes in the examined system parameters. It is shown that such layouts manifest themselves through a minimum variance in the posterior distribution. Using Kalman filters, the posterior variance can be “predicted” based on the measurement error and numerical models, so no data from and no simulations in the damaged state are required. By employing a polynomial chaos expansion (PCE)-based Kalman filter, the method becomes applicable for structures with many measurement channels, multiple examined system parameters, and for a large number of sensor combinations. Gaussian distributions are assumed for both system inputs and outputs. For proof of concept, the sensor placement is optimized on a numerical cantilever beam, demonstrating that the method can efficiently find the optimal sensor layout. Moreover, arbitrary measurement quantities (inclinations, vibrations, etc.) or damage-sensitive features (statistical values, modal parameters, etc.) can be used as observations, and multiple system parameters can be considered at the same time. Among other factors, an optimal sensor layout leads to large measurement responses, a small measurement error, and more advanced statistical quantities that will be elaborated on in detail.

Predictive Probability of Localization Curves (P-POL) for Structures with Changing Environmental Conditions

One of the fundamental challenges in structural health monitoring (SHM) is the lack of data from the damaged state, which is required to verify the automated damage detection algorithms. In this paper, a recently developed approach is presented that allows one to assess the detectability of damages before they occur. The approach is based on so-called probability of detection curves that can be evaluated based on data and a model from the undamaged structure, in a “predictive” way. The method is based on four fundamental assumptions: the damage-sensitive features can be approximated through a normal distribution, the variance in the measurement remains constant for different damage scenarios, an analytical model of the examined structure is available for the sensitivity computation, and the relationship between measurements and structural changes can be linearized for small structural changes. In previous publications, this approach has already been applied to natural frequency and mode shape monitoring as well as ultrasonic testing. In this paper, it is applied to strain and inclination measurements from numerical case studies for the first time, demonstrating the universality of the method. Moreover, it is analyzed how changes in the environmental and operational variables (EOVs) affect the predicted POD. The results demonstrate that the predicted POD are valid even in the presence of environmental changes, but they depend on the user-defined hyperparameters of the algorithms that remove the environmental effects from the measurements. Therefore, the hyperparameter selection is critically discussed and an optimal monitoring strategy is outlined.