Vibration-based Methods Research Articles

Structural damage identification employing hybrid intelligence using artificial neural networks and vibration-based methods

The basis of vibration-based damage detection method is that when there are changes in physical properties of a structure, there will also be changes in vibration characteristics such as natural frequencies, mode shapes and damping ratios. Artificial neural network has become one of the most powerful approaches capable of pattern recognition, classification, and nonlinear modelling employing computational intelligence techniques to tackle damage detection as a complex problem. In this paper, ensemble neural networks based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations of I-beam with triple-point damage cases were carried out to generate the natural frequencies and mode shapes of structures. In the procedure of damage identification, five different neural networks corresponding to mode 1 to mode 5 were constructed, and then an approach based on a neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble neural network has the superiorities of all the individual networks from different vibrational modes. The ensemble network produced better damage identification outcomes than the individual networks and the results revealed the ability of the ensemble neural networks to identify damage.

Some Studies on Verifying the Applicability of Free Vibration-based Modes Shapes Method to Rectangular Shaped Cracks in a Cantilever Beam

Cracks weaken structures. When the crack size increases in service, the structure becomes weaker than its earlier condition. Lastly, the structure may break down due to a small crack. Therefore, crack detection and classification is a fundamental issue. Many aspects of defects have already been addressed, but the application of non-destructive testing methods to structural materials has become more widespread. For a long time, vibration methods based on Natural Frequency and Mode Shapes have been used for possible cracks detection in the beams. The impact of arbitrary and random defect geometry on applying these methods has been overlooked. This study focuses on a mode shapes-based vibration analysis of a cracked cantilever beam to investigate this issue. The effects of crack geometries on mode shapes are examined theoretically and numerically using a new crack model (Rectangular shaped crack), which differs from the well-known V-shaped crack. A MATLAB code is written to obtain the natural frequencies and mode shapes for all cracked instances of beams. The mode shapes result of both the new (Rectangular), and V-shaped models are compared, and it is found that the results are less sensitive to the geometry change.

Investigation of Time-Varying Cable Tension of Bridges Using Time-Frequency Reassignment Techniques Based on Structural Health Monitoring Data

Cables have been increasingly utilized in modern long-span or tied-arch bridges as the main bearing structures. Real-time identification of time-varying cable tension is essential for assessing the service performance of bridges. Vibration-based methods have been an increasing research focus in recent decades. However, a long time interval is needed to estimate structural frequency using vibration-based methods, increasing the calculating time of cable tension. The time-varying cable tension is thus difficult to extract. This study proposes a time-frequency reassignment-based algorithm to reduce the detection time to address this issue. Combined with a time-frequency analysis tool and vibration theory of cables, the algorithm can identify the time-varying frequency and further quickly calculate the time-varying cable tension within 12.8 s. The features of the proposed algorithm are mainly threefold: identifying the time-varying frequencies with high precision; without some prior knowledge of vibration; having no other requirements for sensor modes. Moreover, the experimental validation is conducted using a quasi-static loading in a workshop and a dynamic field test on Sutong Bridge, respectively. The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service.

A fusion CNN driven by images and vibration signals for fault diagnosis of gearbox

Abstract Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.

Contemporary Approaches to the Vibration Diagnostics of Rotating Shafts

Rotating shafts are the most vulnerable part of steam turbines. They are subjected to a wide range of static and dynamic loading combined with high temperature. Different types of damage of rotating shafts in most cases are the result of this long-term combined loading. To avoid catastrophic failure of structural elements during operation of turbines, it is urgent to use reliable damage detection methods. A lot of investigations demonstrated that most appropriate as applied to the turbine shafts in operation are the vibration-based methods, in particular, the change of natural frequencies, the appearance of non-linear resonances, the change of vibrational spectrum and others. The paper contains the comparative analysis of most effective approaches to the vibration diagnostics of damage in rotating shafts of steam turbines.

RC Medium-Rise Building Damage Sensitivity with SSI Effect.

Global vibration-based methods in the field of structural health monitoring are intended to capture structural stiffness changes of buildings or other civil engineering structures. Natural frequencies of buildings or bridges are commonly used parameters to monitor these stiffness changes. Therefore, it is essential to clarify the limit at which this method is no longer sensitive enough to be useful for structural health monitoring purposes. This paper numerically investigates the effect of structural damage and soil–structure interaction on cellular-type reinforced concrete buildings’ natural frequencies. These buildings are a common housing stock of Eastern Europe but are rarely investigated in this context. Comparisons with a reinforced concrete frame and infill structure building are made. Finite element models representing three structural system types of nine-story reinforced concrete buildings were used for the numerical simulations. Furthermore, a five-story finite element model was used for a damage sensitivity comparison. It is established that, for cellular-type structure buildings to detect damage comparable to that investigated in the paper, structural health (fixed base model frequency) should be monitored directly. Then, a statistical significance level for frequency changes of no more than 0.1% should be adopted. Conversely, the rocking frequency is a very sensitive parameter to monitor building base condition changes. These changes are often a cause of the cracking of building elements.

Improving accuracy of cavitation severity recognition in axial piston pumps by denoising time–frequency images

The vibration signal is a good indicator of cavitation in axial piston pumps. Some vibration-based machine learning methods have been developed for recognizing pump cavitation. However, their fault diagnostic performance is often unsatisfactory in industrial applications due to the sensitivity of the vibration signal to noise. In this paper, we present an intelligent method for recognizing the cavitation severity of an axial piston pump in a noisy environment. First, we adopt short-time Fourier transformation to convert the raw vibration data into spectrograms that act as input images of a modified LeNet-5 convolutional neural network (CNN). Second, we propose a denoising method for the converted spectrograms based on frequency spectrum characteristics. Finally, we verify the proposed method on the dataset from a test rig of a high-speed axial piston pump. The experimental results indicate that the denoising method significantly improves the diagnostic performance of the CNN model in a noisy environment. For example, using the denoising method, the accuracy rate forcavitation recognition increases from 0.52 to 0.92 at a signal-to-noise ratio of 4 dB.

Tension Estimation Method for Cable With Damper Using Natural Frequencies With Uncertain Modal Order

In the maintenance of cable structures, such as cable-stayed bridges, it is necessary to estimate the tension acting on the cables. In current Japanese practice, the cable tension is estimated from the cable’s natural frequency using vibration-based methods. However, in recent years, dampers have been installed onto the cables to suppress aerodynamic vibrations. Because the damper changes the cable’s natural frequencies, the methods used for cables without dampers are not appropriate for cables with dampers. With this background, the authors previously proposed a method (Method 0F) for estimating the tension of a cable with a damper from the natural frequencies and their modal order. Method 0F partially ignores the imaginary part of the complex natural frequencies to simplify the problem. This study proposes a new method (Method 1F) that does not ignore the imaginary part of a complex natural frequency. Method 1F still needs both the natural frequencies and their modal order to be input. If the modal order is not correctly specified, the accuracy deteriorates. Therefore, a new method (Method 2F) that only requires the natural frequencies is also proposed. The validity of the proposed methods was confirmed by numerical simulations and experiments. The numerical and experimental verifications confirmed that the new methods were superior compared with previous methods, and that Method 2F has the highest effectiveness.

Spall Fault Quantification Method for Flight Control Electromechanical Actuator

Flight control electro-mechanical actuators (EMAs) are among the primary onboard systems that significantly influence the reliability and safety of unmanned aerial vehicles. Recent reliability studies have shown that the ball-screw element of a flight control EMA is subject to oscillating operating conditions that may initiate rapid degradation, such as fatigue spall defects. Accordingly, detecting and quantifying such faults are crucial for developing efficient fault prognostic and remaining useful life estimation capabilities. In this study, a vibration-based fault quantification method is developed to quantify the fatigue faults of a ball-screw mechanism of an EMA. The method is based on identifying the ball passing instants through a localized surface defect on the vibrational jerk rather than the vibrational acceleration measurement. The jerk is numerically determined from conventional accelerometers using a Savitzky–Golay differentiator. This method was successfully tested for ball bearings and it is adjusted in this paper for ball-screw faults. The experimental validation is investigated on a set of fault-seeded samples on NASA’s Ames Research Center Flyable Electro-Mechanical Actuator test stand.

Modal Frequencies Associations with Musculoskeletal Components of Human Legs for Extracorporeal Bone Healing Assessment Based on a Vibration Analysis Approach.

Reliable and quantitative assessments of bone quality and fracture healing prompt well-optimised patient healthcare management and earlier surgical intervention prior to complications of nonunion and malunion. This study presents a clinical investigation on modal frequencies associations with musculoskeletal components of human legs by using a prototype device based on a vibration analysis method. The findings indicated that the first out-of-plane and coupled modes in the frequency range from 60 to 110 Hz are associated with the femur length, suggesting these modes are suitable quantitative measures for bone evaluation. Furthermore, higher-order modes are shown to be associated with the muscle and fat mass of the leg. In addition, mathematical models are formulated via a stepwise regression approach to determine the modal frequencies using the measured leg components as variables. The optimal models of the first modes consist of only femur length as the independent variable and explain approximately 43% of the variation of the modal frequencies. The subsequent findings provide insights for further development on utilising vibration-based methods for practical bone and fracture healing monitoring.

Railway track support condition assessment—Initial developments on a vehicle-based monitoring solution through modal analysis

Nowadays, there are multiple initiatives showing a renewed interest on railway transport of goods and passengers around the world. Thus, an efficient management of railway infrastructures, both at the operational level and in terms of economic profitability, is not only desirable but also corresponds to an area of ongoing research. In order to contribute to these efforts, an alternative and novel methodology to evaluate railway track support conditions is presented here, based on modal analysis of the characteristic frequencies of the multi-element system composed by a railway infrastructure and an instrumented vehicle moving over it. This methodology belongs to the group of vibration-based structural damage identification methods, and is focused on observing the characteristic frequencies of this multi-element system, which can be correlated with changes in the physical properties of the railway infrastructure under analysis. An important feature of the proposed methodology is that it should enable the collection of information regarding the conditions of the substructure of a railway infrastructure. By performing this assessment of a railway infrastructure over its length, and over time by comparing different rides over the same railway stretch, important information can be gathered regarding the support conditions of the track. This paper presents a complete description on the current stage of development of the proposed methodology, along with the theoretical model that serves as the basis to interpret the collected data. Preliminary verification of this methodology is performed through the analysis of two case studies regarding the passage of an instrumented vehicle over two underpasses. The results obtained so far show that the proposed methodology can provide relevant information regarding the support conditions of railway tracks.

Effectiveness of damage identification in composite plates using damage indices based on smoothing polynomials and curvelet transform: A comparative study

Identification of a structural damage becomes critically important step of proper maintenance of structural elements being in operation according to the widely accepted damage tolerance philosophy in numerous industrial branches related to the construction engineering. Within the variety of available testing methods, vibration-based methods, due to their effectiveness and low cost, are used in such inspection. However, to detect small or early damage in structures, additional processing of the measurement results is necessary. In this paper, two principally different approaches of damage identification in composite plates based on the damage indices and the curvelet transform were analyzed and compared in the light of evaluation of sensitivity to damage, robustness to measurement and processing noise, and accuracy of estimation of a damage. The obtained results of this comparative study clearly indicate the superiority of the curvelet transform, which allowed not only properly identify damage thanks to the enhanced directional selectivity, but simultaneously reduce measurement noise, which makes it an excellent candidate for effective identification of damage in various engineering structures.

Measurement method of natural frequencies and tension forces for cables based on elasto-magnetic sensors calibrated by frequencies

Vibration-based (VB) method and elasto-magnetic (EM) method are usually used to measure cable forces of cable-supported bridges. For the VB method, it is difficult to accurately identify each order natural frequency of the cable disturbed by random excitations, and there are also no precise selection criteria between the taut string model and the hinged beam model. For the EM method, it is not convenient to calibrate EM sensors on bridges in service due to unknown cable forces. To address these issues, a vibration-based elasto-magnetic (VBEM) method is proposed. In this method, a numerical model describing the tie between each order natural frequency and induced voltage was constructed first, and then, a new cable force formula with nominal flexural stiffness was derived. To verify the VBEM method, a steel strand experimental platform was built and the load applied to the steel strand was achieved by a jack. At 18 °C, the first three order natural frequencies of the steel strand and corresponding induced voltage were recorded for each load. According to the obtained experimental data, the VBEM method is tested and analyzed. The results show that the VBEM method exhibits the ability to identify each order natural frequency of the steel strand with high precision; the introduction of nominal flexural stiffness makes the hinged beam model cover the taut string model, resulting in tension force measurement with satisfactory accuracy; the constructed models do not contain tension forces, and this will make it very beneficial to calibration of EM sensors on bridges in service.

Analysis of vibration-based diagnostic methods for health monitoring of key components in helicopter transmissions

Analysis of vibration-based diagnostic methods for health monitoring of key components in helicopter transmissions

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

Vibration-based diagnostics of electrical engineering complexes motors over 40 kW

A vibration-based diagnostics method of large-power 1JAH5 (Siemens) DC motors over 660 kW used in paper machines is proposed in the article. When studying vibration spectrograms it was found that 8 Hz frequency shock loads considerably prevail over vibration, which corresponds to this motor rotor speed. The cause of vibration initiation generated by the motor rotor spinning was determined by theoretical methods. Vibration on the one hand appears as a result of bearings wear accompanied by bearing cage deformation and their shaft spinning axes reciprocal displacement, and on the other hand, by shaft eccentricity radii enhancement which causes dynamic loads increase affecting bearing units. The authors of the paper have come to the conclusion that in order to lower vibration of the motor it is necessary to replace the bearings with the new ones, the former due to a long-term service (over 30 years), vibration and shock load effects having exhausted their life span.

Vibration characteristics of a rotor-bearing system caused due to coupling misalignment – a review

In this paper, the authors presented various insights into theoretical and experimental analysis carried out in past, to understand the vibration characteristics of a misaligned rotor-bearing system. The literature presented by various researchers was reviewed methodically. Firstly, the literature review based on theoretical vibration analysis of misaligned rotor system with emphasis on finite element method has been presented. Secondly, various vibration-based analysis methods and the description of experimental measurement techniques have been discussed in detail. Apart from the above, distinct tools used for the detection of fault analysis of the rotor system is also reviewed systematically, which may be useful for preventive maintenance of the rotating machinery used in several industries.

Calibration of triaxial accelerometers by constant rotation rate in the gravitational field

We extend the use of the intrinsic properties calibration method for triaxial accelerometers that we reported previously from discrete angular steps to using a constant rotation rate to produce a time varying sinusoidal excitation in the earth’s gravitational field. We show that this extension yields the low frequency calibration response of the device under test. Whereas traditional vibration-based methods using shakers generally exhibit an increased measurement uncertainty with decreased excitation frequency, we show that this approach does not. We report results obtained from a commercial triaxial digital accelerometer from DC up to a 0.5 Hz rotation rate. The maximum rotation rate that we report is limited by our rotation stage; but we expect that the method can be extended to higher rotation rates with an upper limit constrained by what can be tolerated as a maximum centripetal acceleration.

Bridge pier scour level quantification based on output-only Kalman filtering

Soil scour near a bridge pier foundation is one of the leading causes of bridge failures. Traditional vibration-based scour monitoring methods are nearly incapable of quantifying scour levels using a single acceleration response without knowledge of excitation information. In this paper, a new output-only scour level prediction method is introduced via the integration of an unscented Kalman filter (UKF), random decrement (RD), and newly derived continuous Euler–Bernoulli beam addressing river water, traffic loads, and the linear and nonlinear behavior of sediments around the pier as external effects. We conducted extensive simulation studies and applied the method to an existing medium-span bridge with a steel girder and concrete deck in service in the province of Manitoba, Canada. These studies show that our proposed method can accurately estimate scour levels using only one accelerometer, which was validated with an independent bathymetric survey of the soil level at the pier foundation. Furthermore, three different linear and nonlinear soil profiles representing the soil behavior around the pier were also investigated as case studies in the scour level estimation process. The results confirm that a cubic function exhibits the best performance in quantifying the scour level around bridge piers.

A review on vibration-based structural pipeline health monitoring method for seismic response

A review on vibration-based structural pipeline health monitoring method for seismic response