scholarly journals Robust structural health monitoring under environmental and operational uncertainty with switching state-space autoregressive models

2018 ◽  
Vol 18 (2) ◽  
pp. 435-453 ◽  
Author(s):  
Anthony Liu ◽  
Lazhi Wang ◽  
Luke Bornn ◽  
Charles Farrar
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Structural Response ◽  
Probability Models ◽  
Time Step ◽  
Operational Conditions ◽  
Structural Health ◽  
Novel Approach ◽  
Definition Of

Existing methods for structural health monitoring are limited due to their sensitivity to changes in environmental and operational conditions, which can obscure the indications of damage by introducing nonlinearities and other types of noise into the structural response. In this article, we introduce a novel approach using state-space probability models to infer the conditions underlying each time step, allowing the definition of a damage metric robust to environmental and operational variation. We define algorithms for training and prediction, describe how the algorithm can be applied in both the presence and absence of measurements for external conditions, and demonstrate the method’s performance on data acquired from a laboratory structure that simulates the effects of damage and environmental and operational variation on bridges.

scholarly journals Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data

2011 ◽  
Vol 467 (2133) ◽  
pp. 2712-2732 ◽  
Author(s):  
Elizabeth J. Cross ◽  
Keith Worden ◽  
Qian Chen
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Variability ◽  
Stationary Time Series ◽  
New Approach ◽  
Operational Conditions ◽  
Structural Health ◽  
Original Dataset ◽  
Novel Approach ◽  
Static Responses

Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions, the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changing environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, which can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration , a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linear combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residual created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmental and operational conditions.

An Evaluation of Damage Features Extraction from Attractors in Structural Health Monitoring

2011 ◽  
Vol 105-107 ◽  
pp. 738-741
Author(s):  
Chao Xu ◽  
Dong Wang
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Prediction Error ◽  
Structural Response ◽  
Accurate Information ◽  
Variance Ratio ◽  
Damage Level ◽  
Structural Health ◽  
Local Variance

Structural health monitoring provides accurate information about structure’s safety and integrity. The vibration-based structural health monitoring involves extracting a feature which robustly quantifies damage induced change to the structure. Recent work has focused on damage features extracted from the state space attractor of the structural response. Some of these features involve prediction error and local variance ratio. In the present paper, a five degree of freedom spring damper system forced by a Lorenz excitation is used to evaluate these two typical damage features. Their ability of identification damage level and location is characterized and compared.

scholarly journals Toward the development of standardized procedures for structural health monitoring

2019 ◽  
Author(s):  
Maria Pina Limongelli
Keyword(s):  
Structural Health Monitoring ◽  
Nondestructive Testing ◽  
Health Monitoring ◽  
Structural Response ◽  
Health Conditions ◽  
Practical Implementation ◽  
Civil Structures ◽  
Structural Health ◽  
The World ◽  
Limited Diffusion

<p>Monitoring of structural health conditions is performed using different methods that range from periodic surveys including nondestructive testing at selected locations, to permanent monitoring using network of sensors continuously recording the structural response. These procedures aim at providing detection of possible faults or deterioration processes in order to optimally manage civil structures and infrastructures over the lifecycle. To date several guidelines have been published by different countries all over the world but protocols to apply SHM are generally not defined nor enforced. This is likely to be of the reasons that stand behind the limited diffusion and implementation of SHM for routine operations of condition assessment. In this paper building the principal aspects of the SHM process are presented and the need of the development of protocols for the different phases of the SHM process, from design to practical implementation and use are outlined.</p>

An On-Going Monitoring Project of a New Timber Structure

2013 ◽  
Vol 778 ◽  
pp. 757-764 ◽  
Author(s):  
Francesca Lanata
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
Environmental Changes ◽  
Construction Material ◽  
Structural Response ◽  
Timber Structures ◽  
The Real ◽  
Structural Health

Structural design, regardless of construction material, is based mainly on deterministic codes that partially take into account the real structural response under service and environmental conditions. This approach can lead to overdesigned (and expensive) structures. The differences between the designed and the real behaviors are usually due to service loads not taken into account during the design or simply to the natural degradation of materials properties with time. This is particularly true for wood, which is strongly influenced by service and environmental conditions. Structural Health Monitoring can improve the knowledge of timber structures under service conditions, provide information on material aging and follow the degradation of the overall building performance with time.A long-term monitoring control has been planned on a three-floor structure composed by wooden trusses and composite concrete-wood slabs. The structure is located in Nantes, France, and it is the new extension to the Wood Science and Technology Academy (ESB). The main purpose of the monitoring is to follow the long-term structural response from a mechanical and energetic point of view, particularly during the first few service years. Both static and dynamic behavior is being followed through strain gages and accelerometers. The measurements will be further put into relation with the environmental changes, temperature and humidity in particular, and with the operational charges with the aim to improve the comprehension of long-term performances of wooden structures under service. The goal is to propose new improved and optimized methods to make timber constructions more efficient compared to other construction materials (masonry, concrete, steel).The paper will mainly focus on the criteria used to design the architecture of the monitoring system, the parameters to measure and the sensors to install. The first analyses of the measurements will be presented at the conference to have a feedback on the performance of the installed sensors and to start to define a general protocol for the Structural Health Monitoring of such type of timber structures.

Lamb Waves Signal Analysis in Structural Health Monitoring Systems

2011 ◽  
Vol 368-373 ◽  
pp. 2402-2405
Author(s):  
Nai Zhi Zhao ◽  
Chang Tie Huang ◽  
Xin Chen
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Changes ◽  
Lamb Waves ◽  
Detection Algorithm ◽  
Operational Conditions ◽  
Structural Health ◽  
Detection Algorithms ◽  
Baseline Subtraction

Many of the wave propagation based structural health monitoring techniques rely on some knowledge of the structure in a healthy state in order to identify damage. Baseline measurements are recorded when a structure is pristine and are stored for comparison to future data. A concern with the use of baseline subtraction methods is the ability to discern structural changes from the effects of varying environmental and operational conditions when analyzing the vibration response of a system. The use of a standard baseline subtraction technique may falsely indicate damage when environmental or operational variations are present between baseline measurements and new measurements. A procedure was outlined for the method, including excitation and recording of Lamb waves, and the use of damage detection algorithms. In this paper, several tests are performed and the results are used to help develop the damage detection algorithms previously described, and to evaluate the performance of the instantaneous baseline SHM technique. Analytical testing is first performed by feeding known input signals into each damage detection algorithm and analyzing the output data. The results of the analytical testing are used to help develop the damage detection algorithms.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

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