Structural Condition Monitoring Research Articles

Digital mapping of corrosion risk in coastal urban areas using remote sensing and structural condition assessment: case study in cyprus

AbstractAtmospheric corrosion is one of the main factors leading to performance deterioration of reinforced concrete buildings; and, hence, periodic structural condition monitoring is required to assess and repair the adverse effects of corrosion. However, this can become a cumbersome and expensive task to undertake for large populations of buildings, scattered in large urban areas. To optimize the use of available resources, appropriate tools are required for the assessment of corrosion risk of reinforced concrete construction. This paper proposes a framework for the production of digital corrosion risk maps for urban areas; Cyprus was used as a case study. This framework explored multi-temporal satellite remote sensing data from the Landsat sensors as well as corrosion risk factors derived from the results of a recently completed research project, entitled “STEELCOR”. This framework was used to develop two corrosion risk scenarios within Geographical Information Systems, and to produce corrosion risk maps for three coastal cities of Cyprus. The thematic maps indicated that, for slight corrosion damage, the distance of reinforced concrete buildings from the coast was more influential than the building age. While, for significant corrosion damage, the maps indicated that the age of RC buildings was more influential than the distance from the coast.

Identification of the operating parameters for the mechanical systeIdentification system using EMD algorithm

The subject of paper was a research on mechanical system with pneumatic coupling, consisting of a compressor turbine (T I ) and of so-called free driving turbine (T II ). The purpose of the tests is to determine the applicability of Empirical Mode Decomposition (EMD) algorithm to identify operating parameters of the mechanical system, which is necessary to build a system for structure condition monitoring with use of vibroacoustic method. There has been assumed possibility of easy separation of working frequencies of these components on order to estimate amplitudes generated by them.

Health monitoring of joint conditions in steel truss bridges with relative displacement sensors

This paper investigates the feasibility and effectiveness of using a recently developed relative displacement sensor for the structural health monitoring of joint conditions in steel truss bridges. The developed relative displacement sensor is an innovative design offering some advantages and unique features, and is a much easier and economical method for structural health monitoring due to the simplicity of its direct measurement of relative displacement without the requirement for a stable reference point. To investigate the performance of applying the developed relative displacement sensors for structural joint condition monitoring, a steel truss bridge model is fabricated in the laboratory and installed with the relative displacement sensors to detect the health conditions of joint connections. The dynamic relative displacement measurements are analyzed with a time-frequency analysis method, i.e. continuous wavelet transform, which is a well-practiced signal processing technique to identify the structural condition change, namely the loosen bolt damage in the joint connection of steel truss bridges under ambient vibrations. The sensitivity range of the developed sensor is also investigated to see how sensitive the sensor is to identify the local bolt damage. Relative displacement measurements of the steel truss bridge models under free vibration tests from both undamaged and damaged states are also analyzed, and a damage index based on the change in the percentages of a specific wavelet packet component to the total wavelet packet energy between the undamaged and damaged states is used to detect the existence of the loosen bolt damage in steel truss bridges. Experimental studies demonstrate that the developed relative displacement sensor has a sensitive performance to identify and assess the joint conditions in steel truss bridges.

Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review

In 2007, the EU established challenging goals for all Member States with the aim of obtaining 20% of their energy consumption from renewables, and offshore wind is expected to be among the renewable energy sources contributing highly towards achieving this target. Currently wind turbines are designed for a 25-year service life with the possibility of operational extension. Extending their efficient operation and increasing the overall electricity production will significantly increase the return on investment (ROI) and decrease the levelized cost of electricity (LCOE), considering that Capital Expenditure (CAPEX) will be distributed over a larger production output. The aim of this paper is to perform a detailed failure mode identification throughout the service life of offshore wind turbines and review the three most relevant end of life (EOL) scenarios: life extension, repowering and decommissioning. Life extension is considered the most desirable EOL scenario due to its profitability. It is believed that combining good inspection, operations and maintenance (O&M) strategies with the most up to date structural health monitoring and condition monitoring systems for detecting previously identified failure modes, will make life extension feasible. Nevertheless, for the cases where it is not feasible, other options such as repowering or decommissioning must be explored.

Piezoresistive Strain Sensors Based on Carbon Nanotube Networks: Contemporary approaches related to electrical conductivity

Since they were discovered in 1991 [2], CARBON nanotubes (CNTs) have attracted enormous attention because of their remarkable properties [1], such as high electrical conductivity, ultrasmall diameter, and large aspect ratio. In addition, CNTs exhibit significant electromechanical properties [3] that could be useful in applications for piezoresistive-type sensors such as strain gauges, pressure sensors, chemical and biological sensors, microelectronic devices, and structural condition monitoring through strain sensing [4]. In addition to the application of a single CNT in various nanoelectromechanical applications, CNT researchers have gradually moved toward potential applications of CNTs as filler candidates in various types of multifunctional material systems, representing a new direction with broad applications [5], [6].

Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

Aspects of structural health and condition monitoring of offshore wind turbines.

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

Development of a Numerical Model to Predict Wear in Grouted Connections for Offshore Wind Turbine Generators

In order to better understand the long term implications of the grout wear failure mode in large-diameter plainsided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the requirement for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

A Strategy for Service Delivery Systems

Servitization requires a strategy formulation for the alignment of service expectation, organization structure, and product condition monitoring.

The Use of Modal Testing within Nuclear Weapon Dismantlement Verification

The technical verification of a possible future nuclear arms control agreement is a complex challenge for technology developers. The focus of this article is on the use of modal testing techniques as a method for maintaining a chain of custody over containerized treaty accountable items (TAI) and monitoring equipment. Modal testing is a specialized form of resonant vibration analysis often used for the purpose of structural identification, condition monitoring, and damage detection. From a chain of custody perspective, it was postulated that a modal vibration signature might be used to identify a particular treaty accountable container or container/object system, or provide evidence of tampering. This article considers the advantages and disadvantages of modal testing as a potential chain of custody tool. Experimental results are discussed relating to deployment, tamper indication, unique identification and data analysis methodology.

RETRACTED ARTICLE: The Sacred Mountain of Varallo renaissance complex in Italy: damage analysis of decorated surfaces and structural supports

Acoustic Emission (AE) is a Non-Destructive Inspection Technique, widely used for monitoring of structural condition of different materials like concrete, masonry and rocks. It utilizes the transient elastic waves produced by each fracture occurrence, which are captured by sensors on the external surface.

A new design method for a strain sensor using the cross-section modification of a coaxial cable

Strain sensors have been used extensively in structural condition monitoring. In this paper, based upon microwave network theory, a novel design method for a real-time strain monitoring sensor is proposed by using the variable cross section of a coaxial cable to detect the strain-induced frequency shift of the peak frequency at which the S parameter exhibits a peak value. With this method, a dual variable-cross-section sensor is designed and fabricated using coaxial cable as the fabrication element. The S-parameters of the sensor obtained analytically by transmission theory are almost consistent with those obtained by numerical simulation. In experiments, the sensitivity of the sensor achieved 5MHz/mɛ with the original segment length of 10.345mm and peak frequency of 5.0GHz, and the resolution achieved is 140.25μɛ. The relative small deviation of nearly 3.2% from the theoretical results to the experimental results adequately validated the feasibility of the design method. Adjusting the diameter of the dielectric layer, the length and cascaded periods of the variable cross-section, the quality factor and sensitivity of the sensor can be optimized. Due to the simplicity and ruggedness of the cable based sensor it has great potential application in structural condition monitoring compared to existing sensors.

Influence of damage in the acoustic emission parameters

Acoustic emission (AE) is a Non Destructive Inspection Technique, widely used for monitoring of structural condition of different materials like concrete, masonry and rock. It utilizes the transient elastic waves after each fracture occurrence, which are captured by sensors on the surface. Several parameters of the AE behavior enlighten the damage stage within the material. These may be the cumulative AE activity, which is connected to the density of cracks and the emission energy which is connected to the cracks’ intensity. Additionally, AE waveform parameters like duration and frequency content depend on the motion of the crack tip and therefore, carry information about the mode of the crack. Study of the AE indices enlightens the fracture process, enabling predictions on the remaining life. However, the experimental conditions crucially affect the waveforms captured by the sensors. Specimen size, as well as sensor type and sensors separation distance exercise strong influence in the acoustic emission parameters. Since AE features like amplitude and energy are used for characterization purposes in the framework of an energy density approach and frequency is used in cracking mode classification schemes, the influence of the above mentioned experimental parameters should be certainly taken into account in order to lead to more accurate results and increase reliability. This would help to expand the use of AE in situ which so far is hindered by geometric and other technical reasons that allow only a case-specific approach. In the present paper fracture experiments in different specimen sizes of cementitious and rock materials are described while the sensor location relatively to the cracking zone is altered. The aim of this study is to validate the use of cracking characterization in laboratory and check the extension for similar schemes in real size structures with a multiscale methodology.

Advanced Tools for Damage Detection in Wind Turbines

The paper summarises some advanced damage detection approaches used for Structural Health Monitoring (SHM) and Condition Monitoring (CM) of wind turbine systems. In the signal processing part, recent time-frequency analysis methods will be presented and examples of their application on condition monitoring of gearboxes will be given. In the pattern recognition part, examples of damage detection in blades will be used to introduce different algorithms for novelty detection.

Review of Nondestructive Testing Methods for Condition Monitoring of Concrete Structures

The deterioration of concrete structures in the last few decades calls for effective methods for condition evaluation and maintenance. This resulted in development of several nondestructive testing (NDT) techniques for monitoring civil infrastructures. NDT methods have been used for more than three decades for monitoring concrete structures; now it has been recognized that NDT plays an important role in the condition monitoring of existing RC structures. NDT methods are known to be better to assess and evaluate the condition of RC structures practically. This paper reviewed several available NDT methods developed and used in the last few decades.

Structural Vibration Monitoring Using Cumulative Spectral Analysis

This paper describes a resonance decay estimation for structural health monitoring in the presence of nonstationary vibrations. In structural health monitoring, the structure's frequency response and resonant decay characteristics are very important for understanding how the structure changes. Cumulative spectral analysis (CSA) estimates the frequency decay by using the impulse response. However, measuring the impulse response of buildings is impractical due to the need to shake the building itself. In a previous study, we reported on system damping monitoring using cumulative harmonic analysis (CHA), which is based on CSA. The current study describes scale model experiments on estimating the hidden resonance decay under non-stationary noise conditions by using CSA for structural condition monitoring.

Structural Health Monitoring of Solid Rocket Motors' Propellant Using Polymer Optical Fibers

Polymer Optical Fibers are proposed for the monitoring of structural health condition of energetic materials in solid rocket motors. This photonic solution provides the necessary electromagnetic immunity and inherent electrical safety characteristics in the highly explosive environment of SRM's propellant. Furthermore the use of POF's intrinsic elasticity in contrast to silica fibers makes possible the monitoring of excessive strains apparent in the behavior of the energetic propellant. Both jacketed and unjacketed fibers were employed demonstrating excellent strain transfer characteristics able to monitor consistently propellant's behavior under strain.

The three-stage artificial neural network method for damage assessment of building structures

AbstractBuilding structures are often huge and composed of a number of elements. It may not be possible to make modal measurements along the large number of degrees of freedom. Structural damage detection therefore becomes much more challenging both in terms of measurement and subsequent analyses. Accordingly, a problem in structural damage detection is requirement of a systematic and effective method. Among the developed damage detection techniques, artificial neural networks (ANNs) have become promising tools recently. The main drawback of using ANNs in structural condition monitoring is the requirement of enormous computational effort. To address this issue, a novel technique is proposed using “damage index” derived from frequency response functions (FRFs) with the three-stage ANN method to detect damage. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points. Then using these features, damage indices of damage cases of the structure are...

The Three-Stage Artificial Neural Network Method for Damage Assessment of Building Structures

Building structures are often huge and composed of a number of elements. It may not be possible to make modal measurements along the large number of degrees of freedom. Structural damage detection therefore becomes much more challenging both in terms of measurement and subsequent analyses. Accordingly, a problem in structural damage detection is requirement of a systematic and effective method. Among the developed damage detection techniques, artificial neural networks (ANNs) have become promising tools recently. The main drawback of using ANNs in structural condition monitoring is the requirement of enormous computational effort. To address this issue, a novel technique is proposed using “damage index” derived from frequency response functions (FRFs) with the three-stage ANN method to detect damage. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points. Then using these features, damage indices of damage cases of the structure are identified. Damage indices corresponding to different damage locations and severities are introduced to ANNs. The effectiveness of the proposed method is validated using the finite element model of a 10-storey framed structure. The results show that the principal component analysis based damage index is suitable for structural damage detection.

Dynamic characteristics and model updating of damaged slab from ambient vibration measurements

This paper reports a field investigation using ambient vibration testing on a damaged floor slab of a reinforced concrete frame building. Due to unexpected heavy rainfall, the hill slope at the rear of building failed triggering a major landslide and causing major damage to the perimeter beams and parts of the slab on the first floor. The modal parameters namely natural frequencies and mode shapes were acquired using output only identification technique and the results obtained from the undamaged and damaged floor slabs were compared. It was observed that there was a 25–53% drop in natural frequencies of the damaged slab compared to the undamaged slab, with a much bigger drop for the lower modes. The irregularities in mode shapes identified correlates with the location of the cracks as revealed from visual examination on the damaged slab. Two finite element models of the slab were created using a finite element software package. The damaged slab was updated manually so as to match the modal parameters obtained experimentally. The results from this study further highlight the possibility and feasibility of using non-destructive vibration testing for condition monitoring of structures over more conventional testing techniques.