Vibration-based Monitoring Research Articles

A new method for earthquake-induced damage identification in historic masonry towers combining OMA and IDA

This paper presents a novel method for rapidly addressing the earthquake-induced damage identification task in historic masonry towers. The proposed method, termed DORI, combines operational modal analysis (OMA), FE modeling, rapid surrogate modeling (SM) and non-linear Incremental dynamic analysis (IDA). While OMA-based Structural Health Monitoring methods using statistical pattern recognition are known to allow the detection of small structural damages due to earthquakes, even far-field ones of moderate intensity, the combination of SM and IDA-based methods for damage localization and quantification is here proposed. The monumental bell tower of the Basilica of San Pietro located in Perugia, Italy, is considered for the validation of the method. While being continuously monitored since 2014, the bell tower experienced the main shocks of the 2016 Central Italy seismic sequence and the on-site vibration-based monitoring system detected changes in global dynamic behavior after the earthquakes. In the paper, experimental vibration data (continuous and seismic records), FE models and surrogate models of the structure are used for post-earthquake damage localization and quantification exploiting an ideal subdivision of the structure into meaningful macroelements. Results of linear and non-linear numerical modeling (SM and IDA, respectively) are successfully combined to this aim and the continuous exchange of information between the physical reality (monitoring data) and the virtual models (FE models and surrogate models) effectively enforces the Digital Twin paradigm. The earthquake-induced damage identified by both data-driven and model-based strategies is finally confirmed by in-situ visual inspections.

EVALUATION OF VIBRATION-BASED GLOBAL STRUCTURAL HEALTH MONITORING METHOD FOR MEDIUM-RISE BUILDINGS

The automated monitoring of a building’s structural health during its exploitation is a way to extend its design life without compromising structural safety. In turn, it helps increase the rate of building renovation works compared to demolition works, which reduces future construction and demolition waste levels.This research explores the vibration-based global monitoring method application to structurally stiff medium-rise reinforced concrete buildings by analysing predicted building vibration amplitudes and spectrum under regular city traffic excitation. These predictions are based on the results obtained from finite element calculations of building models with variated structural stiffness and inertial mass of the building.Regular traffic-generated ground frequency spectrum differs from the first natural frequencies of medium-rise reinforced concrete buildings, and the vibration energy is low. Nevertheless, it is found that the structural identification of such building dynamic parameters is still possible, particularly natural frequencies. It was found that the ratio between fundamental frequency for the fixed base model of the building and elastic spring foundation model is the decisive parameter for selecting the building part to be monitored. Structural health monitoring vibration-based methods are also a promising technology for medium-rise mass house buildings when tailored according to some damage sensitive feature.

3D torsional experimental strain modal analysis for structural health monitoring using piezoelectric sensors

Modal analysis is a dynamic response-based experimental structural identification procedure widely used for condition assessment and structural health monitoring (SHM). Piezoelectric sensors are smart material-based dynamic strain sensors being progressively implemented in vibration-based monitoring techniques. However, these have been so far limited to preliminary investigations on 1D and 2D structures with regard to modal analysis. Strain modal analysis using piezoelectric sensors has so far utilized only lower bending modes for frequency and mode shape-based SHM. This paper aims to evaluate the piezoelectric sensors for modal response measurement, modal parameter identification, and structural health monitoring of 3D structures, especially using torsional modes. It reports a comparative experimental study between the piezoelectric sensors and the accelerometers for dynamic response measurement, modal parameter extraction and damage identification and localization. Experimental modal analysis using impact hammer-based excitation is performed on two lab-sized specimens under different boundary conditions. Displacement mode shapes from both the sensors are used to determine the location and the severity of the damage. Torsional and bending modes are studied distinctly for sensitivity towards damage monitoring. Modal assurance criteria (MAC), coordinated MAC (CoMAC), and modal strain energy (MSE) are adopted as damage indicators. Results of the study show that the piezoelectric sensors are equivalent to accelerometers for response measurements and modal parameter extraction. They possess enhanced sensitivity towards damage monitoring as compared to accelerometers. Torsional modes perform better than bending modes for damage localization and severity quantification. Bending modes from both sensors are ineffective in locating the damage precisely. Modal strain energy (MSE) based damage index from torsional modes acquired from piezoelectric sensor performs best in terms of sensitivity towards damage location and exhibits peak severity, especially for incipient damage. The study establishes piezoelectric sensors as reliable and cost-effective modal response sensors.

Fully automated peak-picking method for an autonomous stay-cable monitoring system in cable-stayed bridges

Stay cables are the most important components of cable-stayed bridges, in which bridge loads are directly carried. Because the stay cables dominate the alignment of the bridges as well as their internal force distribution, cable tension forces are crucial to both construction and maintenance. In this regard, a vibration-based monitoring system is considered as a cost-effective method to monitor the cables, and they require the extraction of modal frequencies via peak-picking in the frequency domain. Manual peak-picking is susceptible to human errors, making it challenging for real-time and continuous monitoring. For autonomous monitoring, this study develops a fully automated peak-picking method for stay cables. The proposed method combines information derived from two sources without any prerequisite implementations. The proposed method is extensively evaluated with three methods using real field data. The experimental results demonstrate that the proposed method outperforms the other methods in terms of accuracy and computational efficiency.

Vibration-Based Monitoring for Transverse Cooperative Working Performance of Assembled Concrete Multi-Girder Bridge: System Design, Implementation and Preliminary Application

The hinge joints of the assembled concrete multi-girder bridge (ACMGB) are the main components of transverse transmission of loads and the decline of their transmission capacity would lead to the degradation of the transverse cooperative working performance (TCWP) of the multi-beam system. As the key working performance of this kind of bridges, its severe degradation would result in the single-girder-bearing phenomenon inducing bridge collapse. In order to detect TCWP degradation timely, this paper proposes a vibration-based monitoring scheme of the ACMGB, where structural vibration information are collected by accelerometers and dynamic strain guages at the bottom of girders in mid-span. These monitoring data are then used for identifying transverse mode shape of the multi-beam system and its derivated index in order to evaluate TCWP. This scheme has been implemented on three adjacent ACMGBs in Tongji Road Viaduct and the preliminary analysis of monitoring data proves the effectiveness and rationality of the monitoring scheme proposed in this paper, which has the potential of the TCWP evaluation.

Vibration-based monitoring of engineering metal structures during technological operations

The process of creating a vibration monitoring system for a mine headframe is considered. This building is a large metal structure that provides lifting ore from the mine and transfer it to the factory for processing. The structure is exposed to aggressive corrosive effects, which leads to its destruction. The developed system is aimed at preventing emergency situations caused by damage to structural elements. This system analyzes the vibrational response of the structure to technological operations that provide excitation of a wide range of natural frequencies. The procedure for finding time intervals corresponding to technological operations is considered. The features of the dynamic impact corresponding to one of the technological operations - the dropping of ore into the storage are shown. Methods of processing vibrograms, which allow determining the natural frequencies of a structure, are considered.

Retrofittable vibration-based monitoring of milling processes using wavelet packet transform

An important aspect of the overall quality of machined parts is surface roughness, which depends on cutting parameters, tool condition, and machine vibrations. Online surface roughness prediction in milling operations can reduce set up time and assist in determining economic cutting parameters. However, the adoption of existing solutions in industrial production is inhibited by lacking integration in an open and retrofittable architecture. In this contribution, a solution for surface roughness estimation by vibration monitoring is developed as part of a retrofitting kit. Wavelet packet transform is used to filter the vibration signal, then the roughness of the generated surface is estimated. The approach is tested in milling experiments.

Vibration-Based Thermal Health Monitoring for Face Layer Debonding Detection in Aerospace Sandwich Structures

This paper investigates the potential of a novel vibration-based thermal health monitoring method for continuous and on-board damage detection in fiber reinforced polymer sandwich structures, as typically used in aerospace applications. This novel structural health monitoring method uses the same principles, which are used for vibration-based thermography in combination with the concept of the local defect resonance, as a well known non-destructive testing method (NDT). The use of heavy shakers for applying strong excitation and infrared cameras for observing thermal responses are key hindrances for the application of vibration-based thermography in real-life structures. However, the present study circumvents these limitations by using piezoelectric wafer active sensors as excitation source, which can be permanently bonded on mechanical structures. Additionally, infrared cameras are replaced by surface temperature sensors for observing the thermal responses due to vibrations and damage. This makes continuous and on-board thermal health monitoring possible. The new method is experimentally validated in laboratory experiments by a sandwich structure with face layer debonding as damage scenario. The debonding is realized by introduction of an insert during the manufacturing process of the specimen. The surface temperature sensor results successfully show the temperature increase in the area of the debonding caused by a sinusoidal excitation of the sandwich structure with the PWAS at the first resonance frequency of the damage. This is validated by conventional infrared thermography. These findings demonstrate the potential of the proposed novel thermal health monitoring method for detecting, localizing and estimating sizes of face layer debonding in sandwich structures.

Implementation of Singular Spectrum Analysis in Industrial Robot to Detect Weak Position Fluctuations

A fault or mechanical flaw causes several feeble swings in the position signal. Identification of such swings by encoders can help to identify machine performance and health status and provide a convenient alternative to a vibration-based monitoring system. In operations, the trend is usually several orders higher than the interested magnitude swings, thus increasing the difficulty of identifying feeble swings without signal deformity. Moreover, the swings can be intricate, and the amplitude can be changed under a nonstationary operating condition. Singular spectrum analysis (SSA) for detecting feeble position swings from the rotary encoder signal is suggested in this paper to address this issue. It allows the complex signal of the encoder to be reduced to a variety of explainable noise-containing components, a collection of periodic oscillations, and a trend. The numerical simulation reveals the achievement of the technique. It demonstrates that the SSA is superior to the empirical mode decomposition in terms of accuracy and ability. In addition, rotary encoder signals from the robot arm are evaluated to identify the causes of oscillation at junctions during industrial robot movements. The proposed route for the robotic arm is proven to be feasible and reliable.

A low quiescent power wireless rotating machinery condition monitoring system

Vibration-based monitoring of rotating machinery is rapidly evolving within the aerospace industry with priority on detecting impending failures. The workhorse of such monitoring system remains a piezoelectric-based accelerometers which requires a wired-harnesses, connectors, significant power, and signal conditioning, etc. Raytheon Technologies Research Center (RTRC) along with Collins Aerospace and Sandia National Laboratory have jointly developed an Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE). 
 This is a low power alternate for a conventional wired vibration-based monitoring system. This self-contained sensor system includes: (1) a low quiescent power sensing element with a wake-up module, (2) a wireless communication module, and (3) a coin-cell battery. Leveraging work performed under Defense Advanced Research Projects Agency (DARPA) N-Zero program. This wireless health monitoring system can operate in a quiescent low power mode (~10nW) for a period of several years without servicing. With an exceedance above a preset vibration level (at designate characteristic frequencies), the sensor wakes up and wirelessly sends a warning of a precursor-to-failure.
 The ARISE sensor and wake-up module package has been validated with a replicated vibration environment acquired from a selected rotating machinery subject to progressive damage at the Structural Dynamics Laboratory at RTRC. The failure precursor is successfully detected by the sensor which triggers the wake-up module.
 This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) Micro Technology Office (MTO), under Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE) program.

Long-term vibration-based monitoring and seismic performance assessment of a wind turbine

In this study, long-term vibration-based monitoring of a 900-kW onshore wind turbine is presented. Modal properties such as frequency, shape, and damping ratios were obtained using enhanced frequency domain decomposition method. Change in modal parameters due to environmental and operational conditions was determined. Modal parameters after cleansing such effects were obtained by multiple linear regression analyses. Then, finite element model of the wind turbine was developed including piles and soil springs; and updated using identified modal parameters. After all, seismic fragility curves of the structure with the limit state being local buckling were developed considering different levels of wind speed. Stress level at which local buckling occurs was determined by pushover analysis of the turbine using a nonlinear finite element model. In the context of this study, it was observed that identified modal values had significant scattering and level of wind speed significantly changed the seismic fragility curve of the wind turbine.

The role of modal parameters uncertainty estimation in automated modal identification, modal tracking and data normalization

During the last decade, many vibration-based structural health monitoring systems have been successfully implemented in different structures such as bridges, towers, stadia and wind turbines, with the aim of studying the structure dynamics and its evolution over time, eventually detecting the occurrence of novel structural behaviour that may indicate the presence of damage.Such vibration-based monitoring systems generally rely on the identification of modal properties, which are then used as monitoring features. Therefore, from operational modal analysis to the tracking of those features and finally to data normalization, many processing steps occur that depend on the accuracy of the identified modal properties. Thus, the estimation of the uncertainties associated with the identified modal properties increases the robustness of this process.In this context, data obtained from the continuous dynamic monitoring of a concrete arch dam has been used to evaluate the gains of quantifying the uncertainties of modal properties, evaluating in particular the effect of taking these uncertainties into consideration when performing automated operational modal analysis, modal tracking and data normalization. Nevertheless, it is observed that the most significant gains of considering estimates uncertainties occur when these quantities are used for removing outliers during modal tracking.

Nonnegative matrix factorization-based blind source separation for full-field and high-resolution modal identification from video

Traditional modal analysis requires physically attached sensors for data acquisition and vibration-based monitoring. Although traditional modal analysis presents well-established techniques for dynamics analysis, they can impose mass-loading effects on lightweight structures and increase budgetary demands on the maintenance of such data acquisition systems. Recently video-based techniques have become of increasing interest in the identification of the dynamic properties of infrastructures with arbitrary complexity. However, most applications rely on frame by frame tracking of fixed speckle targets to derive time-varying physical parameters. This imposes serious limitations for real-world applications, especially in scenarios where the structure is out of reach. Therefore, to address these issues, we propose a novel output-only operational modal analysis method based on vision-based blind source separation scheme. The proposed algorithm makes use of each pixel as a potential measurement point. This enables an increase in the spatial density of sensors conventionally used on a structure by orders of magnitude. This simultaneous processing of all pixel time-series derives full-field high-resolution mode shapes instead of low spatial resolution mode shapes achieved when measuring a limited number of discrete locations with typical sensors. Compared to other approaches, we propose a blind source separation scheme simpler than the ones based on phase extraction and complex steerable pyramids that still capable of disentangling local structural vibration from video measurement only. Moreover, a simple method to magnify and visualize independent vibration modes is introduced using the extracted modal information only. We validate our method by laboratory experiments on a bench-scale building structure and a cantilever beam. The results demonstrate that the proposed technique can decompose high-resolution modal parameters, visualize and reconstruct even those weakly-excited vibration modes.

Automated operational modal analysis and ambient noise deconvolution interferometry for the full structural identification of historic towers: A case study of the Sciri Tower in Perugia, Italy

Structural Health Monitoring (SHM) based upon Operational Modal Analysis (OMA) constitutes an increasingly mature and widespread technology in the conservation of historic structures. Such techniques, while proved effective for assessing the global integrity of structures, may fail at detecting local damage with little influence on the modal features of the system. In this context, the analysis of propagating waves throughout the structure features a synergistic approach to OMA with superior capabilities for data-driven damage identification. Although some promising results have been reported in the literature on the application of Seismic Interferometry to reinforced-concrete structures, works concerning the continuous monitoring of ambient vibrations in historic structures are virtually non-existent. In this light, this paper proposes the coupled application of automated OMA and Ambient Noise Deconvolution Interferometry for the full structural system identification of historic structures, and evaluates the advantages of this technology through a validation case study of the Sciri Tower in Perugia, Italy. A continuous vibration-based monitoring system deployed in the tower during three weeks allows us to assess the effectiveness of the proposed approach. The reported results demonstrate the robustness of the monitoring system for identifying environmental effects on the spatial distribution of wave velocities, and shed light into the dispersion relation of the tower.

Wireless-Based Identification and Model Updating of a Skewed Highway Bridge for Structural Health Monitoring

Vibration-based monitoring was performed on a short-span skewed highway bridge on the basis of wireless measurements. By means of operational modal analysis, highly accurate modal results (frequencies and mode shapes) were extracted by using a self-developed wireless acquisition system, for which the performance was verified in the field. In order to reproduce the experimental modal characteristics, a refined finite element model was manually tuned to reduce the idealization errors and then updated with the sensitivity method to reduce the parametric errors. It was found that to build a reliable Finite element (FE) model for application in structural health monitoring, the effects of superelevation and boundary conditions of a skewed bridge should be taken into account carefully.

Cluster-Based Vibration Analysis of Structures With GSP

This article describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of microelectromechanical accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme, which consists of unconventional and nonoverlapped sensing configurations. Signal processing techniques for inter- and intracluster data assembly are introduced to allow for a full-scale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, reveals high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes are correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach that favors the implementation of autonomous smart monitoring systems.

A compressive MUSIC spectral approach for identification of closely-spaced structural natural frequencies and post-earthquake damage detection

Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited structures using acceleration measurements acquired at rates significantly below the Nyquist rate. The approach adopts the deterministic co-prime sub-Nyquist sampling scheme, originally developed to facilitate telecommunication applications, to estimate the autocorrelation function of response acceleration time-histories of low-amplitude white-noise excited structures treated as realizations of a stationary stochastic process. Next, the standard multiple signal classification (MUSIC) spectral estimator is applied to the estimated autocorrelation function enabling the identification of structural natural frequencies with high resolution by simple peak picking in the frequency domain without posing any sparsity conditions to the signals. This is achieved by processing autocorrelation estimates without undertaking any (typically computationally expensive) signal reconstruction step in the time-domain, as required by various recently proposed in the literature sub-Nyquist compressive sensing-based approaches for structural health monitoring, while filtering out any broadband noise added during data acquisition. The accuracy and applicability of the proposed approach is first numerically assessed using computer-generated noise-corrupted acceleration time–history data obtained by a simulation-based framework examining white-noise excited structural systems with two closely-spaced modes of vibration carrying the same amount of energy, and a third isolated weakly excited vibrating mode. Further, damage detection potential of the developed method is numerically illustrated using a white-noise excited reinforced concrete 3-storey frame in a healthy and two damaged states caused by ground motions of increased intensity. The damage assessment relies on shifts in natural frequencies between the pre-earthquake and post-earthquake state. Overall, numerical results demonstrate that the considered approach can accurately identify structural resonances and detect structural damage associated with changes to natural frequencies as minor as 1% by sampling up to 78% below Nyquist rate for signal to noise ratio as low as 10dB. These results suggest that the adopted approach is robust and noise-immune while it can reduce data transmission requirements in acceleration wireless sensors for natural frequency identification and damage detection in engineering structures.

Identification of unbalance in a rotor-bearing system using Kalman filter–based input estimation technique

Unbalance is a common machinery fault, which occurs because of uneven distribution of mass of a rotating component about its axis. Vibration-based monitoring techniques have been widely accepted for machinery fault diagnosis. This article presents experimental verification of a recently developed Kalman filter–based method for the identification of unbalance in rotor systems. The method is tested on an experimental test rig for different unbalance configurations and shaft speeds. The proposed technique is a model-based method, which requires a mathematical model of the rotor system along with response measurements. A rigid rotor model is used, and measured accelerations at bearing pedestals are used for unbalance parameter estimation. Bearing stiffnesses are estimated using a frequency domain parameter estimation technique with measured unbalance responses. Sensitivity analysis is also performed by altering the values of these estimated stiffnesses.

Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission

The consideration of wireless acceleration sensors is highly promising for cost-effective output-only system identification in the context of operational modal analysis (OMA) of large-scale civil structures as they alleviate the need for wiring. However, practical monitoring implementations for OMA using wireless units suffer a number of drawbacks related to wireless transmission of densely sampled acceleration time-series including the energy self-sustainability of the sensing nodes. In this work, two recently proposed approaches for output-only modal identification addressing the above issues through balancing monitoring accuracy with data transmission costs are comparatively studied and numerically assessed using field recorded acceleration datasets from two different structures: (i) an operating on-shore wind turbine, (ii) an open to traffic highway bridge. One approach utilizes non-uniform-in-time deterministic multi-coset sampling at sub-Nyquist rates to capture structural response acceleration time-series under ambient excitation assuming stationary signal conditions. In this approach, a power spectrum blind sampling technique is used to estimate the response acceleration power spectral density matrix from the low-rate sampled measurements and is coupled with the Frequency Domain Decomposition method of OMA. The other is a spectro-temporal compressive sensing approach which recovers response acceleration signals through time-series reconstruction in the time domain from sub-Nyquist non-uniform-in-time randomly sampled measurements. Prior knowledge of signal structure in the spectral domain is exploited through smart on-sensor operations and sensor/server communication. The benefits and limitations of the considered approaches are discussed and demonstrated by processing the field recorded datasets for different levels of signal compression and by estimating battery lifetime gains at a single sensor achieved by reduced data transmission. It is concluded that the two approaches are readily applicable in OMA of large-scale structures and can be used complementarily depending on the requirements of any particular acceleration monitoring campaign: time-series extraction for further interrogation vs. solely modal properties estimation.

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.