Reliable Structural Health Monitoring Research Articles

Continuous baseline growth and monitoring for guided wave SHM

It has been shown by many authors that temperature effect compensation is a necessity for the realization of a reliable structural health monitoring (SHM) system. However, there exist practical issues related to the acquisition of baseline signals, required for temperature compensation methods to work effectively, such as degenerate baseline signals recorded at the same temperature and a long acquisition period before monitoring is started. An alternative approach for the acquisition of baseline signals is presented in this paper. The algorithm makes the acquisition an evolutionary process, integrated in the damage detection procedures. The algorithm is shown to be successful in generating a concise set of baselines that guarantees efficient performance of temperature compensation methods, leading to computation time and computer memory savings. Its capabilities in detecting simulated abrupt and slowly evolving damages have also been tested.

서해대교 건전성 모니터링을 위한 데이터 분석 및 건전성지수

It is important to collect reliable measured data for proper bridge health monitoring. However, in reality incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In case of sensor malfunction, parts of measured data are missing and thus health monitoring cannot be carried out reliably. Due to environmental effects such as temperature variation, dynamic characteristics of natural frequencies may change as if the structure is damaged. The paper proposes a systematic procedure of data processing and data analysis for reliable structural health monitoring. Also, it applies the Mahalanobis distance as a health index computed statistically using revised data. The proposed procedure has been examined using numerically simulated data from a truss structure and then applied to a set of field data measured from Seohae cable-stayed bridge.

Review of flaws and damages in space launch vehicle: Structures

A space launch vehicle is a complex engineering structure. It consists of several structural and system components that are stably stored for extended periods and that at the launch moment, must function precisely. However, this presents a number of unique challenges as well as the possibility of various flaws and damages during the manufacturing, assembly, or ground handling phase. Mechanical and chemical complexity, long service lives, aging materials, and designs with small margins are typical of space launch vehicle components. The performance and characteristics of the components can be significantly affected by degradation resulting from such flaws and damages, and this degradation in turn might lead to failure of the entire space mission. Damage detection at the earliest possible stage enables implementation of possible preventive measures, and evaluation of component reliability and readiness either at the manufacturing level or during field inspections. This review study lists such possible flaws/damages for various space launch vehicle components. This information could be beneficial for determining and developing reliable nondestructive evaluation and structural health monitoring techniques.

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex "bottleneck" structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a "grand challenge" due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.

MUSIC‐ANN Analysis for Locating Structural Damages in a Truss‐Type Structure by Means of Vibrations

Abstract: This article presents a methodology for damage detection, location, and quantification based on vibration signature analysis and a comprehensive experimental study to assess the utility of the proposed structural health monitoring applied to a five‐bay truss‐type structure. The MUSIC algorithm introduced first by Jiang and Adeli for health monitoring of structures in 2007 is fused with artificial neural networks for an automated result. The developed methodology is based on feeding the amplitude of the natural frequencies as input of an artificial neural network, being the novelty of the proposed methodology its ability to identify, locate, and quantify the severity of damages with precision such as: external and internal corrosion and cracks in an automated monitoring process. Results show the proposed methodology is effective for detecting a healthy structure, a structure with external and internal corrosion, and a structure with crack. Therefore, the proposed fusion of MUSIC‐ANN algorithms can be regarded as a simple, effective, and automated tool without requiring sophisticated equipment, toward establishing a practical and reliable structural health monitoring methodology, which will help to evaluate the condition of the structure, in order to detect damages early and to make the corresponding maintenance decisions in the structures.

Structural Health Monitoring of Composite Material Typical of Wind Turbine Blades by Novelty Detection on Vibration Response

New generations of offshore wind turbines are playing a leading role in the energy arena. One of the target challenges is to achieve reliable Structural Health Monitoring (SHM) of the blades. Fault detection at the early stage is a vital issue for the structural and economical success of the large wind turbines. In this study, experimental measurements of Frequency Response Functions (FRFs) are used and identification of mode shapes and natural frequencies is accomplished via an LMS system. Novelty detection is introduced as a robust statistical method for low-level damage detection which has not yet been widely used in SHM of composite blades. Fault diagnosis of wind turbine blades is a challenge due to their composite material, dimensions, aerodynamic nature and environmental conditions. The novelty approach combined with vibration measurements introduces an online condition monitoring method. This paper presents the outcomes of a scheme for damage detection of carbon fibre material in which novelty detection approaches are applied to FRF measurements. The approach is demonstrated for a stiffened composite plate subject to incremental levels of impact damage.

Development of a wireless sensor network system for suspension bridge health monitoring

A wireless sensor system has been suggested and tested in the place of forty five sensors of five types among the sensors used extensively in suspension bridges to prove the viability of wireless sensor network in actual implementation. ZigBee (IEEE 802.15.4) is used for short-distance communications among sensors and Code Division Multiple Access (CDMA) for long-distance wireless communications with remote locations. A data logger is integrated with a sensor and ZigBee into a one-channel data logger that combines sensor, logger and digital communication modules to allow for the addition or replacement of a sensor with ease. Lastly, the wireless sensor network-based bridge health monitoring system proposed herein is developed in the form of an application that is intended to ensure universal applicability and designed to maximize the ease of wireless sensor network. ► Reliable bridge structural health monitoring using wireless sensor network. ► Wireless sensor network test on a large suspension bridge. ► ZigBee for local sensor network and CDMA for long distance data transferring. ► Antenna type and memory buffer are important for the network reliability.

Assessment of Vibration-based Damage Identification Methods Using Displacement and Distributed Strain Measurements

Accurate measurement and monitoring of vibration characteristics is critical for proper detection of the location and severity of damage. However, experimentally measured modal properties are inevitably corrupted by measurement noise and errors, which could render most vibration-based structural damage identification algorithms unreliable for civil structural health monitoring (SHM). This article, through computer simulation and experimental investigation of a simply supported beam, comparatively evaluates the performance of these techniques for practical civil SHM by using displacement modes from accelerometers and long-gage distributed strain measurements. Most of all the techniques proved unreliable for damage identification using noisy measurements from accelerometers, while successful with distributed strain measurements. The findings reveal that long-gage distributed strain measurements are much more efficient choice over the traditional measurement techniques for reliable civil SHM. It may therefore be concluded that the performance of some algorithms might be improved for application to civil infrastructure by using distributed strain fiber optic sensing measurement techniques.

Stochastic change detection in uncertain nonlinear systems using reduced-order models:classification

A reliable structural health monitoring methodology (SHM) is proposed to detect relativelysmall changes in uncertain nonlinear systems. A total of 4000 physical tests were performedusing a complex nonlinear magneto-rheological (MR) damper. With the effective (or‘genuine’) changes and uncertainties in the system characteristics of the semi-active MRdamper, which were precisely controlled with known means and standard deviation of theinput current, the tested MR damper was identified with the restoring force method(RFM), a non-parametric system identification method involving two-dimensionalorthogonal polynomials. Using the identified RFM coefficients, both supervised andunsupervised pattern recognition techniques (including support vector classification andk-means clustering) were employed to detect system changes in the MR damper. Theclassification results showed that the identified coefficients with orthogonal basis functioncan be used as reliable indicators for detecting (small) changes, interpreting the physicalmeaning of the detected changes without a priori knowledge of the monitoredsystem and quantifying the uncertainty bounds of the detected changes. Theclassification errors were analyzed using the standard detection theory to evaluate theperformance of the developed SHM methodology. An optimal classifier designprocedure was also proposed and evaluated to minimize type II (or ‘missed’) errors.

Application of Multiplexed FBG and PZT Impedance Sensors for Health Monitoring of Rocks.

Reliable structural health monitoring (SHM) including nondestructiveevaluation (NDE) is essential for safe operation of infrastructure systems. Effectivemonitoring of the rock components of civil infrastructures such as tunnels and cavernsremains challenging. The feasibility of employing smart optical fibre sensor (OFS) andpiezoelectric impedance sensor made up of lead zirconate titanate (PZT) forcomprehensive health monitoring of rocks, covering load history monitoring/retrieval aswell as damage assessment is presented in this paper. The rock specimens are subjected tocyclic loading and their conditions are continuously monitored using OFS and PZTsensors. OFS based multiplexed fibre Bragg grating (FBG) sensors are surface bonded onthe rock specimens. Their strain sensing performance is compared with the conventionalelectric strain gauges (ESGs). In addition, PZT patches are also bonded on the specimensto study the damage pattern during different loading cycles. Unlike the FBGs or ESGs,PZT patches are used as bi-functional sensors and actuators, enabling them to be efficientdetectors of incipient damages using the principle of electromechanical impedance. Theexperimental study demonstrated superior performance of these smart FBG and PZTimpedance sensors. This work is expected to be useful for SHM based NDE application ofrock structures such as caverns and tunnels.