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.

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.

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.

Structural Health Monitoring of Aerospace Structures with Sol-Gel Spray Sensors

2007 ◽  
Vol 347 ◽  
pp. 505-510 ◽  
Author(s):  
Abdelhakim Ouahabi ◽  
Marc Thomas ◽  
Makiko Kobayashi ◽  
Cheng Kuei Jen
Keyword(s):  
Numerical Simulation ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sol Gel ◽  
Metallic Plate ◽  
New Approach ◽  
Specific Location ◽  
Structural Health ◽  
Spray Technique ◽  
Vibratory Signals

A new approach is proposed for conducting structural health monitoring, based on newly developed piezoceramic sensors. They are fabricated by a sol-gel spray technique. The potential application of these sensors may be broad. These sensors have been evaluated for structural health monitoring studies. The purpose of the present study aims the detection and the localization of defects by the means of these new piezoceramic sensors. Nine sensors were integrated onto a metallic plate with moving masses. The plate was excited by an impact at a specific location and the vibratory signals from sensors were recorded simultaneously. The analysis of signals obtained from nine locations was correlated with a numerical simulation in order to identify at each time the location of the mass.

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