Modern Structural Health Monitoring Research Articles

A novel shape sensing approach based on the coupling of Modal Virtual Sensor Expansion and iFEM: Numerical and experimental assessment on composite stiffened structures

Shape sensing, i.e. the reconstruction of the displacement field of a structure from discrete strain measurements, is becoming crucial for the development of a modern Structural Health Monitoring framework. Nevertheless, an obstacle to the affirmation of shape sensing as an efficient monitoring system for existing structures is represented by its requirement for a significant amount of sensors. Two shape sensing methods have proven to exhibit complementary characteristics in terms of accuracy and required sensors that make them suitable for different applications, the inverse Finite Element Method (iFEM) and the Modal Method (MM). In this work, the formulations of these two methods are coupled to obtain an accurate shape sensing approach that only requires a few strain sensors. In the proposed procedure, the MM is used to virtually expand the strains coming from a reduced number of strain measurement locations. The expanded set of strains is then used to perform the shape sensing with the iFEM. The proposed approach is numerically and experimentally tested on the displacement reconstruction of composite stiffened structures. The results of these analyses show that the formulation is able to strongly reduce the number of required sensors for the iFEM and achieve an extremely accurate displacement reconstruction.

Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring

Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and high-dimensional, especially for large structure and machinery. In condition-based monitoring (CBM) of rotating machines, e.g., gas turbine, rotor fault diagnosis serves a significant role in the system reliability, safety, and efficiency, which helps reduce potential damage to the on-rotor structures and avoid catastrophic failures. For multi-channel vibration measurement, classical rotor diagnosis approaches typically involve data fusion techniques based on cross spectral analysis for identifying spectral correlation, e.g., cross power spectral density, and/or matrix analysis tool for dimensionality reduction, e.g., principal component analysis. The operation on vectors or matrices may limit their effectiveness for higher-order array or tensor data. To circumvent this limitation, the third order vibrational spectral tensor is generated by representing the multi-channel acceleration spectra as the three-way array. Through nonnegative Tucker decomposition (NTD), the spectral tensor is decomposed into multiple principal factors: the spectral factor, segmental factor, channel-wise factor, and the dense tensor core. The correlations across the characteristic spectral contents and the sensor channels are revealed by the factorization, which enables the diagnosis of rotor fault and facilitates fault localization by identifying the dominant channels of the characteristic spectral factor. The method is validated on an experimental rotor testbed where the faulty channel with crack, rub, or misalignment fault, is effectively localized via 4-channel vibration measurements, which presents a promising approach for multi-sensor fusion and fault diagnosis in rotating machinery health monitoring.

Arbitrary-Order Sensitivity Analysis in Wave Propagation Problems Using Hypercomplex Spectral Finite Element Method

Many modern structural health monitoring (SHM) systems use piezoelectric transducers to induce and measure guided waves propagating in structures for structural damage detection. To increase the detection capabilities of SHM systems, gradient-based optimization of sensor placement is frequently necessary. However, available numerical differentiation methods for mechanical wave propagation problems suffer from truncation and subtraction errors and are difficult to extend to high-order sensitivities. This paper addresses these issues by introducing an approach to obtain highly accurate numerical sensitivities of arbitrary order in mechanical wave propagation problems. The hypercomplex time-domain spectral finite element method (ZSFEM) couples the hypercomplex Taylor series expansion method with the time-domain spectral finite element method. We show how ZSFEM can be implemented within the commercial finite element package ABAQUS/Explicit. For verification, we compared the numerical and analytical results of the displacement and its sensitivities with respect to mechanical parameters, geometry, and boundary conditions for a rod subjected to a sudden, distributed axial load. First- and second-order sensitivities were obtained with normalized root mean square deviations below 4×10−3. Mesh convergence analyses revealed that p-refinement offered better convergence rates than h-refinement for the outputs and their sensitivities. Also, the sensitivities obtained with ZSFEM were compared with finite differences showing higher accuracy and step-size independence (e.g., no iteration is needed to determine the step size that minimizes the error). For simplicity, ZSFEM was presented only for one-dimensional truss elements, but the method is general and can be applied to other elements.

Management of road bridge networks in Italy by means of integrated SHM systems

Management of stocks with large number of bridges is a relevant issue for the Italian roadway system. In the last years the use of integrated SHM (Structural Health Monitoring) systems is being introduced within the framework of the modern BMS (Bridge Management Systems) with the aim of implementing a continuous damage identification strategy. In this framework, ambient vibration-based SHM has received most attention owing to its minimal intrusiveness, non-destructive character and global damage identification capabilities. A lot of research effort has been devoted in the last years to SHM techniques but ease of use, interpretability and minimal intervention of expert judgement are essential issues for the fruitful application of SHM in real and wide bridge networks. ANAS S.p.A., the main road managing Authority in Italy, is implementing a huge monitoring program on its bridge network with the main purpose of addressing the recent indications of the Italian guidelines for assessing and monitoring of bridges, and the University of Padova, together with the University of Perugia, Politecnico of Milan, University of Pisa and the other Universities of FABRE Consortium are supporting ANAS for the real application of modern SHM techniques. According to these actual needs, a new software suite called P3P has been developed and is being applied to a huge stock of bridges distributed in all the Italian territory.

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

A Versatile Analog Electronic Interface for Piezoelectric Sensors Used for Impacts Detection and Positioning in Structural Health Monitoring (SHM) Systems

Continuous monitoring of mechanical impacts is one of the goals of modern SHM systems using a sensor network installed on a structure. For the evaluation of the impact position, there are generally applied triangulation techniques based on the estimation of the differential time of arrival (DToA). The signals generated by impacts are multimodal, dispersive Lamb waves propagating in the plate-like structure. Symmetrical S0 and antisymmetrical A0 Lamb waves are both generated by impact events with different velocities and energies. The discrimination of these two modes is an advantage for impact positioning and characterization. The faster S0 is less influenced by multiple path signal overlapping and is also less dispersive, but its amplitude is generally 40–80 dB lower than the amplitude of the A0 mode. The latter has an amplitude related to the impact energy, while S0 amplitude is related to the impact velocity and has higher frequency spectral content. For these reasons, the analog front-end (AFE) design is crucial to preserve the information of the impact event, and at the same time, the overall signal chain must be optimized. Large dynamic range ADCs with high resolution (at least 12-bit) are generally required for processing these signals to retrieve the DToA information found in the full signal spectrum, typically from 20 kHz to 500 kHz. A solution explored in this work is the design of a versatile analog front-end capable of matching the different types of piezoelectric sensors used for impact monitoring (piezoceramic, piezocomposite or piezopolymer) in a sensor node. The analog front-end interface has a programmable attenuator and three selectable configurations with different gain and bandwidth to optimize the signal-to-noise ratio and distortion of the selected Lamb wave mode. This interface is realized as a module compatible with the I/O of a 16 channels real-time electronic system for SHM previously developed by the authors. High-frequency components up to 270 kHz and lower-frequency components of the received signals are separated by different channels and generate high signal-to-noise ratio signals that can be easily treated by digital signal processing using a single central unit board with ADC and FPGA.

Material and strain sensing uncertainties quantification for the shape sensing of a composite wing box

The shape sensing, i.e. the reconstruction of the displacement field of a structure from discrete strain measures, is a key tool for the support and development of the modern Structural Health Monitoring frameworks and has received a huge attention in the last few decades. The parallel increase in the use of composite materials in the aerospace industry has consequently generated the need to investigate the applicability of the shape sensing methods to this peculiar kind of materials. In fact, the manufacturing complexity of the composite materials can result in a significant variability in the characteristics of the material. Therefore, a study on the propagation of this kind of uncertainty on the performance of the shape sensing methods is paramount. The uncertainties in the strain measurements can influence the shape sensing results and must be also considered. This paper, for the first time, investigates the propagation of these two sources of inputs’ uncertainty on the performance of three shape sensing methods, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the Ko’s Displacement theory. Using the Monte Carlo Simulation (MCS) with Latin Hypercube Sampling (LHS), the robustness of the three methods with respect to the inputs’ variability is evaluated on the reconstruction of the displacement field of a composite wing box. The MM shows a significant robustness and the iFEM, although more affected by the uncertainties, is the method that achieves the best accuracy. The Ko’s displacement theory, on the other hand, is the less accurate and the less robust.

Denoising of Hydrogen Evolution Acoustic Emission Signal Based on Non-Decimated Stationary Wavelet Transform

Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently without degrading the original properties. The hydrogen evolution experiments on carbon steel pipelines are carried out for AE data acquisition. Simulations on experimentally acquired AE signals and randomly generated synthetic signals with different levels of noise are performed by the ND-SWT method for noise removal. Results show that our proposed method can effectively eliminate Gaussian white noise as well as noise from the vibration and frictional activity and provide efficient noise removal solutions for SHM applications with minimum reconstruction error, to extract meaningful AE signals from the large-scale noisy AE signals during monitoring and inspection.

Dynamic maintenance planning of a hydro-turbine in operational life cycle

Life cycle assessment (LCA) has been an emerging feature of modern structural health monitoring techniques that aims at evaluating equipment degradation process and it can be used for development of dynamic maintenance plans. A novel framework based on LCA is proposed in this paper for dynamic maintenance planning of hydro-turbines. Using a Hidden Markov Model (HMM) and the inspection data of hydro-turbine runner cracks from actual operations, the transition probability matrix and sojourn time of different crack states are obtained. This is utilized for predicting the expected remaining useful life (RUL) and the formulation of maintenance intervals. Moreover, maintenance plans are developed from the influence of cracks in different states leading to critical conditions. The proposed method, as demonstrated in the case study, efficiently updates the maintenance intervals and reduces the likelihood of failure events. The results of this paper provide a valid maintenance framework for achieving higher operational safety of hydropower stations with minimal resource losses.

IoT sensors for modern structural health monitoring. A new frontier

The problem of determining the structural safety level of buildings and civil engineering infrastructures (CEIs) is raising growing concern worldwide. Most of the reinforced concrete constructions have a design life not greater than 100 years, and today it is necessary to face the problem of assessing their level of safety and structural integrity. Such problem is even more pressing when a construction is subjected to extreme environmental conditions. The long-term goal of this study is the realization of wireless lowcost devices, and a data management software, for the structural health monitoring of buildings and CEIs, with remotely controlled sensors embedded in, or installed on, the structural elements, to measure stresses together with accelerations. Once equipped with such system, each construction can become part of the Internet of Things, permitting users and authorities to be alerted in case structural safety is diminished or compromised. A crucial aspect is the unaltered preservation of measurement data over time, which cannot just rely on third parties, and for which it is necessary the exploitation of suitable data-protection technologies. This study have been carried out by experimental testing and validation, both in lab and on site, of the monitoring devices designed and realized. Results show that it is possible to realize low-cost monitoring systems, and related installation techniques, for integration in every new or existing buildings and CEIs

Vibration-based SHM with up-scalable and low-cost Sensor Networks

Structural health monitoring (SHM) is becoming increasingly attractive for its potentialities in many application contexts, such as civil and aeronautical engineering. In these scenarios, modern SHM systems are typically constituted by a multitude of sensor nodes. Such devices should be based on low-cost and low-power solutions both to ease the deployment of progressively denser sensor networks and to be compatible with a permanent installation; this allows real-time monitoring while reducing the global maintenance costs. Among the developed inspection methodologies, operational modal analysis (OMA) is an efficient tool to assess the integrity of vibrating structures. This article describes a sensor network that is based on either microelectromechanical systems (MEMS) accelerometers or cost-effective piezoelectric devices to extract strictly synchronized modal parameters. The performances of the two sensing technologies are evaluated in two different setups, to assess the reliability in the estimation of modal features even in the presence of potential damages. Particular attention was given to the mode shape reconstruction issue from piezoelectric signals, primarily encompassing a purposely developed modal coordinate tuning procedure. Moreover, the consistency of the obtained results paves the way to a more compact and affordable monitoring system exploiting piezoelectric-driven modal analysis.

Correlation of mission type to cyclic loading as a basis for agile military aircraft asset management

Military attack aircraft are susceptible to the harmful effects of widespread fatigue damage caused by cyclic loading of structural components, which leads to airframe retirement. Modern structural health monitoring techniques use a multitude of sensors and high data collection rates. Some legacy airframes, which are most susceptible to fatigue damage due to their age, possess a counting accelerometer technology with few sensors and low data capture rates. The data provided by these 40-year old devices are crucial to understanding fleet health and can be used to extend structural lifetime for aging aircraft. Existing literature has addressed counting accelerometer usefulness, but a profound three-decade gap in research has led to a chasm between the current wealth of available data and tool development for utilizing those data. This research uses 11 years of A-10 Thunderbolt II counting accelerometer data to prove that mission type, mission duration and aircraft type correlate to aircraft loading patterns. It is shown that a mission type model can therefore influence fleet management strategies and the structural lifetime extension for aging aircraft.

Fast Bayesian modal identification of structures using known single-input forced vibration data

Modal identification aims to identify the modal parameters of constructed structures based on vibration data. Forced vibration tests allow one to obtain data with a higher signal-to-noise ratio compared with free or ambient vibration tests. Modal identification techniques for forced vibration data exist, but they do not provide a rigorous quantification of the remaining uncertainties of the modal parameters, which is becoming important in modern structural health monitoring and uncertainty propagation. This paper develops a Bayesian approach that properly accounts for uncertainty in accordance with probability logic for modal identification using forced vibration data. The posterior probability density function of the modal parameters to be identified given the data is derived based on the assumed model and the collected data. An efficient algorithm is developed that allows practical implementation in the case of a single shaker. It is applicable for both separated and closely-spaced modes even with a large number of measured degrees of freedom. The proposed method is verified and investigated using synthetic and field test data. Copyright © 2013 John Wiley & Sons, Ltd.

Structural Surface Crack Monitoring Method Based on Electrical Potential Technique and Modern Surface Technology

Crack monitoring plays a great role in modern structural health monitoring, however, most of the conventional crack inspections have disadvantages in terms of the accuracy, expense, reliability, durability and level of instrumentation required. Thus, development of a simple and reliable crack inspection technique that allows continuous monitoring has been desired. In this paper, electrical potential technique and modern surface technology are employed together to develop a new structural surface crack monitoring method. A special crack monitoring coating sensor based on electrical potential technique was deposited on the hot spot of the structure by modern surface technology. The sensor consists of three layers: the isolated layer, the sensing layer and the protective layer. The isolated layer is prepared by anodic oxidation technology, the sensing layer is made of ion plated copper, and the protective layer is made of silicone. The thickness of each layer is at micrometer magnitude. The electrical conductivity of the sensor is very stable, and the fatigue performance of the specimen with or without coating sensor is nearly unchanged. The crack monitoring experiment result shows that there are two sudden rises of the coating sensor electrical potential values, corresponding to different stages of the crack initiation and propagation. Since the width of the surface coating sensor is only 0.5 mm, this crack monitoring sensor can detect the propagation of cracks less than 0.5 mm long. The method proposed takes the simplicity of electrical potential technique and can monitor surface crack of nearly all kinds of structures precisely. The results of this paper may form the basis of a new crack monitoring system.

Biologically-Inspired Condition Monitoring of Civil Engineering Structures

Long-term deterioration of civil engineering structures is a serious problem of crucial societal and economical relevance. In these years, civil engineering structures such as bridges or wind turbines are reaching or exceeding their original anticipated lifespan. Consequently, there is an urgent need for applying modern structural health monitoring systems to ensure serviceability and structural safety through efficient as well as cost-effective monitoring. In this contribution, a novel generation of biologically-inspired structural health monitoring systems is introduced, synergistically coupling sophisticated engineering concepts and technologies stemming from artificial intelligence research. To exemplify the proposed concepts in more detail, the design and the implementation of a biologically-inspired and self-managing (i.e. self-configuring, self-optimizing, self-protecting and self-healing) structural health monitoring system prototype is presented. Practicability, reliability and performance of the prototype are corroborated by means of structural and environmental data taken from different engineering structures in tandem with intensive laboratory tests.

A correlation between pulse diagnosis of human body and health monitoring of structures

The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.