Structural Health Monitoring Applications Research Articles

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.

Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.

Advancement of data-driven SHM: a research paradigm on AE-based switch rail condition monitoring

The past ten years have witnessed the tremendous progress of structural health monitoring applications in civil infrastructures. This is particularly embodied in railway engineering. The increasing train speed brings greater challenges to safety and ride comfort, and the primary theme of maintenance has been gradually altered from offline inspection to online monitoring. Rail operators must get an in-time warning of potential structural defects before critical failure takes place. It is more favourable that the rail operators can take hold of the real-time status of the key components and infrastructures in railway systems. This paper summarizes a long-term research series by the authors’ research team on online monitoring of rail tracks at turnout areas utilizing acoustic emission-based sensing technique, and more importantly, successively advancing signal processing methods and data-driven analysing frameworks, covering Bayesian inference, convolutional neural networks, transfer learning and task similarity analysis. The proposed algorithms tackle noise interference brought by wheel-rail impacts, great uncertainties in an open environment, and insufficiency of monitoring data, and realize comprehensive monitoring of rail tracks in turnout areas from basic crack detection to regressive condition assessment step-by-step.

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

This article proposes a deep learning-assisted nondestructive evaluation (NDE) technique for locating and sizing a coating delamination using ultrasonic guided waves. The proposed technique consists of sending a propagating guided wave into a coated plate with a transducer and measuring the corresponding time-domain signals by receivers at several locations at downstream distances from the source transducer. The received time-domain signals are then provided to a trained machine-learning (ML) algorithm, which subsequently outputs the location and size of any delamination flaws between the transducer and receivers. Numerical simulations show that the proposed NDE technique yields accurate results with high throughput, once the ML algorithm is well trained. Although training the ML algorithm is time-consuming, this training only needs to be done once for a given sample configuration. The results of this article demonstrate that the proposed technique has great potential for characterizing delamination flaws in practical NDE and structural health monitoring (SHM) applications.

Enhancing data sparsity in spectral signals using wavelet decomposition for improved compression and storage efficiency

Wavelet decomposition (WD) is integrated into the Compressive Sensing (CS) model to enhance data sparsity. This approach aims to ensure efficiency and quality in the received data after compression and reconstruction processes. The proposed WD-CS model is developed for reflected spectra signals from Fiber Bragg Gratings-based extensometers, which were subjected to induced deflections simulating the underground soil movement. WD decomposes the input signal before compressive measurement, followed by transferring and storing the compressed data in the cloud. The spectral data were reconstructed using Orthogonal Matching Pursuit (OMP) followed by wavelet reconstruction. The impact of wavelet decomposition on the quality of compression and reconstruction is assessed and compared against that of the standard CS model. The findings indicate that the WD-CS model can improve data sparsity by a factor of three and compressibility by a factor of ten without degenerating the reconstructed data quality. The error in the Bragg wavelength shift of the reconstructed spectra is minimal extracted using Pseudo-high Resolution Interrogation (PHRI) method. The proposed WD-CS model is useful in improving data compression for FBG spectra as well as storage efficiency, which make it potentially applicable in FBG-based sensor networks for long-term structural health monitoring applications.

Monitoring damage growth and topographical changes in plate structures using sideband peak count-index and topological acoustic sensing techniques

Some topographies in plate structures can hide cracks and make it difficult to monitor damage growth. This is because topographical features convert homogeneous structures to heterogeneous one and complicate the wave propagation through such structures. At certain points destructive interference between incident, reflected and transmitted elastic waves can make those points insensitive to the damage growth when adopting acoustics based structural health monitoring (SHM) techniques. A newly developed nonlinear ultrasonic (NLU) technique called sideband peak count – index (or SPC-I) has shown its effectiveness and superiority compared to other techniques for nondestructive testing (NDT) and SHM applications and is adopted in this work for monitoring damage growth in plate structures with topographical features. The performance of SPC-I technique in heterogeneous specimens having different topographies is investigated using nonlocal peridynamics based peri-ultrasound modeling. Three types of topographies – “X” topography, “Y” topography and “XY” topography are investigated. It is observed that “X” and “XY” topographies can help to hide the crack growth, thus making cracks undetectable when the SPC-I based monitoring technique is adopted. In addition to the SPC-I technique, we also investigate the effectiveness of an emerging sensing technique based on topological acoustic sensing. This method monitors the changes in the geometric phase; a measure of the changes in the acoustic wave’s spatial behavior. The computed results show that changes in the geometric phase can be exploited to monitor the damage growth in plate structures for all three topographies considered here. The significant changes in geometric phase can be related to the crack growth even when these cracks remain hidden for some topographies during the SPC-I based single point inspection. Sensitivities of both the SPC-I and the topological acoustic sensing techniques are also investigated for sensing the topographical changes in the plate structures.

Notch corrosion fatigue behaviour and simplified strain energy density-based notch fatigue life assessment of Grade-A shipbuilding steel

Corrosion fatigue is a key challenge in lifecycle management of ship hull structures. Although sea wave fatigue stresses are linear-elastic, highly-damaging elastic–plastic conditions occur at geometrical discontinuities or notches. Estimation of notch stress-strains to characterize notch fatigue behaviour conventionally requires large computational efforts. Also, constant strain energy density (SED), as a driving force, is far more effective in realistic applications versus constant stress and strain fatigue life approaches. Identified research gaps were the development of a simplified SED approach for fatigue life assessment of hole-notched structures. Fatigue studies were conducted on hole-notched specimens of Grade-A shipbuilding steel in air and corrosion mediums. Strong synergistic interactions of corrosion and fatigue damage mechanisms were studied by scanning electron microscope (SEM) fractography. Notch corrosion fatigue life was reduced by 70% from notch air fatigue life due to environmental effect. Two new parameters are proposed to improve the efficiency of notch fatigue life assessment, namely “fatigue notch strain energy density concentration factor (K_SED) and “fatigue notch toughness”. This improved fatigue life assessment method was demonstrated as equally reliable but much simpler to implement, eliminating the complex computational analysis. This improved method has promising applications in real-time structural health monitoring (SHM) of ship hull structures.

Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques

This paper presents a novel method for determining the fundamental frequency and modeling slender masonry structures, which is particularly relevant in the context of structural dynamics. This research evaluates the effectiveness of machine learning techniques, specifically artificial neural networks (ANN), and regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNLR), in predicting the fundamental frequency of structures, considering both their geometrical and mechanical properties. The objective is to provide a comprehensive comparison of these methods, emphasizing their benefits, limitations, and potential applications in structural health monitoring. The ANN model demonstrated superior performance to linear and non linear regression methods. The findings could significantly impact both theoretical understanding and practical applications in this field. Moreover, this work paves the way for future research, potentially leading to the creation of more accurate and efficient predictive models for slender masonry structures. This methodology ensures a thorough understanding of the model’s behavior and its responsiveness to variations in input parameters.

A metasurface for suppressing mode conversion and achieving directional reflection of shear horizontal wave

The lowest-order shear horizontal (SH0) guided wave in a plate is crucial for nondestructive testing and structural health monitoring due to its nondispersive nature. However, mode conversion at the interface for the SH0 wave complicates the received information and increases the difficulty of signal analysis. In this work, an elastic metasurface is proposed to suppress mode conversion at the interface and to reflect the SH0 wave directionally. The metasurface comprises a series of rectangular elements with a specific phase gradient covering the 2π phase range. The effectiveness of the proposed metasurface is confirmed through finite element simulations and experimental validation. The results indicate that the metasurface can significantly reduce the amplitude of the reflected lowest-order symmetric Lamb wave when compared to the reflection without the metasurface, while simultaneously allowing the reflected SH0 wave to propagate at the designed angle. Furthermore, the metasurface maintains its functionality for SH0 wave with incident angles varying from 0° to 25°. This study provides a viable approach for suppressing mode conversion and modulating the reflection behavior of SH0 wave, which is expected to find applications in nondestructive testing and structural health monitoring.

Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring

In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using many model evaluations, which in turn can become quite costly. We focus here on recent developments in the field of transport theory, where the problem is formulated as finding a deterministic, invertible mapping between some easy to evaluate reference density and the posterior. The resulting variational formulation can be solved with integration and optimization methods. We develop a general formulation for the application of transport maps to vibration-based structural health monitoring. Further, we study influences of different integration approaches on the efficiency and accuracy of the transport map approach and compare it to the Transitional MCMC algorithm, a widely used method for structural identification. Both methods are applied to a lower-dimensional dynamic model with uni- and multi-modal properties, as well as to a higher-dimensional neural network surrogate system of an airplane structure. We find that transport maps have a significant increase in accuracy and efficiency, when used in the right circumstances.

Innovative welding integration of acousto-ultrasonic composite transducers onto thermoplastic composite structures

Acousto-ultrasonic composite transducers (AUCTs), comprising piezoceramic materials in a reinforced polymeric matrix, show promise for structural health monitoring in composite structures. Challenges arise when integrating AUCTs onto highly loaded thermoplastic composites, especially low-surface-energy materials like polyaryletherketone composites. To address this, the study explores the viability of attaching AUCTs to low-melting polyaryletherketone carbon fiber-reinforced thermoplastic composite structures using ultrasonic welding. This welding technique forms a joint where the interface material fuses with the AUCT embedment and the structure matrix, providing a reliable and automatable process. The investigation includes a comparative analysis of an ultrasonic welded joint with an external energy director and a reference AUCT system integrated using a vacuum bagging oven procedure. Results highlight the potential of AUCT configurations integrated by ultrasonic welding as an alternative solution, acknowledging challenges that persist for further development and increased reliability in structural health monitoring applications.

Deep learning-based three-dimensional crack damage detection method using point clouds without color information

Automated high-precision crack detection on building structures under poor lighting conditions poses a significant challenge for traditional image-based methods. Overcoming this challenge is crucial to enhance the practical applicability of structural health monitoring and rapid damage assessment, especially in post-disaster scenarios like earthquakes. To address this challenge, this paper presents a deep learning-based three-dimensional crack detection method that utilizes light detection and ranging (LiDAR) point cloud data. Our method is specifically designed to address crack detection without relying on color information input, resulting in high-precision and robust apparent damage detection. The key contribution of this paper is the NL-3DCrack model, which enables automated three-dimensional crack semantic segmentation. This model comprises a feature embedding module, an incomplete neighbor feature extraction module, a decoder, and morphological filtering. Notably, we introduce an innovative incomplete neighbor mechanism to effectively mitigate the impact of outliers. To validate the effectiveness of our proposed method, we establish two three-dimensional crack detection datasets, namely the Luding dataset and the terrestrial laser scanner dataset, which are based on earthquake disasters. Experimental results demonstrate that our method achieves remarkable performance, with an intersection-over-union of 39.62% and 51.33% on the respective test sets, surpassing existing point cloud-based semantic segmentation models. Ablation experiments further confirm the effectiveness of our approach. In summary, our method showcases exceptional crack detection performance on LiDAR data using only XYZI channels. With its high precision and reliable results, it offers significant utility in real-world applications, contributing to improved structural health monitoring and rapid damage assessment after disasters, particularly in post-earthquake scenarios.

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Bridges are essential to infrastructure and transportation networks, but face challenges from heavier traffic, higher speeds, and modifications like busway integration, leading to potential overloading and costly maintenance. Structural Health Monitoring (SHM) plays a crucial role in assessing bridge conditions and predicting failures to maintain structural integrity. Vibration-based condition monitoring employs non-destructive, in situ sensing and analysis of system dynamics across time, frequency, or modal domains. This method detects changes indicative of damage or deterioration, offering a proactive approach to maintenance in civil engineering. Such monitoring systems hold promise for optimizing the management and upkeep of modern infrastructure, potentially reducing operational costs. This paper aims to assist newcomers, practitioners, and researchers in navigating various methodologies for damage identification using sensor data from real structures. It offers a comprehensive review of prevalent anomaly detection approaches, spanning from traditional techniques to cutting-edge methods. Additionally, it addresses challenges inherent in Vibration-Based Damage (VBD) SHM applications, including establishing damage thresholds, corrosion detection, and sensor drift.

Bamboo structures: Innovative methods and applications for structural health monitoring and dissemination

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked by CBMF. In recent years, the hierarchical Bayesian modeling framework (HBMF) has gradually attracted attention. This framework incorporates an additional layer of hyperparameters and utilizes multiple sets of experimental data to describe the variability in structural parameters caused by changes in experimental conditions. Unfortunately, the increase in hyperparameters and data size leads the HBMF to face the dilemma of exceedingly high computational costs. To address this issue, this study proposes an improved hierarchical Bayesian modeling framework (IHBMF). To enhance the computational efficiency of IHBMF, the importance sampling method is employed to simplify the computation of the likelihood function for hyperparameters. Subsequently, a surrogate model based on arbitrary polynomial chaos expansion is introduced to further reduce the computational cost of IHBMF. The framework's accuracy and efficiency were validated through analysis of a simply supported beam and a steel pedestrian bridge. The results demonstrate that IHBMF not only delivers a more accurate quantification of structural parameter uncertainty than CBMF but also exhibits higher computational efficiency compared to HBMF, showcasing its superior capability in structural health monitoring applications.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning

Huge amounts of data can be generated during long-term monitoring performed by structural health monitoring (SHM) and structural integrity management applications. Monitoring data can be corrupted, and the presence of abnormal data can distort information during signal processing, extract incorrect characteristics during system identification, produce false conclusions during damage detection, and ultimately lead to misjudgment of structural conditions during diagnosis and prognosis. Therefore, developing effective techniques to autonomously detect and classify anomalies becomes necessary and significant. Generally, conventional physics-based strategies can be straightforward, but their performance highly depends on prior knowledge of measurement. Recently, data-driven methods leveraging machine learning (ML) have been used to directly handle the task. This study proposes an ML-based classifier and improves it by incorporating the human-in-the-loop (HITL) learning. The classifier is built on a shallow neural network with high performance to address potential online or real-time applications for long-term monitoring. First, a field monitoring dataset is introduced, and various anomalies are defined to investigate the effectiveness. To further enhance the performance of the proposed classifier, the mislabels in the monitoring dataset are examined, and a correction technique is performed. Additionally, HITL ML is developed to overcome the disadvantages of the conventional correction technique. As a result, the proposed procedure can improve both the classifier and the field dataset, and the proposed classifier can now function as a fundamental component in achieving a continuous and autonomous SHM system.

Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework

This article proposes a consistent and general approach to train physics-augmented neural networks with observable data to enrich and represent nonlinear history-dependent material behaviors in terms of both state equations and evolution laws. In this learning strategy consistent with thermodynamics, the constitutive model is expressed using two potentials (free energy and dissipation potential) which are represented by input-convex neural networks, thus automatically satisfying the principles of thermodynamics. The neural network is trained thanks to an unsupervised procedure that does not rely on strain–stress pairs but needs only partial strain or displacement measurements inside the structure, moreover with uncertain boundary conditions. This method is based on the minimization of the modified Constitutive Relation Error functional, and it extends previous works on this error measure for neural networks to the case of history-dependent behaviors, which requires to design a specific minimization procedure. Given that neural networks for typical structural health monitoring applications often need to be trained online, there is here a significant emphasis placed on automatically and adaptively tuning crucial hyperparameters such as learning rate or weighting between losses.The method is evaluated on elastoplastic and elastoviscoplastic test cases with synthetic data collected from optic fiber or full-field measurements. It is shown that the method can properly learn hidden behaviors, achieves high robustness to noise level, and low sensitivity to user-defined hyperparameters.

A proposed tacho-less order tracking technique suitable for railway vehicle condition monitoring applications

To perform a condition monitoring for rotating machines with variable running speed, order tracking is considered as an effective technique suitable for their non-stationary vibration signals. A tachometer signal is necessary to perform a computed order tracking (COT) technique; however, in some applications it's not possible to add tachometer hardware due to its complexity or shortage of power. To improve the applicability of order tracking techniques, some tacho-less order tracking techniques have been introduced in the last few years. These techniques are working off-line by inspecting the vibration signals in time-frequency domain to extract the machine rotating speed. In this paper, a new tacho-less technique suitable for continuous structural health monitoring applications of railway vehicles, is proposed. In the presented approach, a coarse angular speed of the railway axle is estimated firstly using estimation train speed. Then this coarse angular speed is used to improve the applicability of the traditional tacho-less techniques to be able to work online. The train speed is estimated from two acceleration signals obtained from vertical vibration of axle-boxes from same side of train. The sequential properties of the two signals are used to estimate the delay time of one signal with respect to the other one by means of cross-correlation function. By knowing the horizontal distance between the two sensors collected the vibration signals, the train speed can be estimated. This modification of tacho-less technique improves the effectiveness and robustness of the traditional maxima tracking strategy to be performed automatically. Thereafter, signal processing technique based on the computed order tracking as well as averaging technique can be applied to extract the useful information from noisy and non-stationary signals. This technique has been proposed in a framework of a project investigating crack detection using vibration measurements, therefore, vibration signals of cracked axle have been processed using tacho-based method and using the proposed tacho-less technique and a good agreement has been found between the two techniques.