shm_presentation (nestor castaneda)
Post on 20-Jul-2016
33 Views
Preview:
DESCRIPTION
TRANSCRIPT
Structural Health Monitoring:from algorithms to implementations
Nestor E. CastanedaGraduate Research Assistant
School of Civil EngineeringCollege of EngineeringPurdue University
Outline
Introduction: Structural health monitoring (SHM) A static-based SHM algorithm A vibration-based SHM algorithm Wireless sensor role (WS) in SHM
Previous implementations Current SHM - WS research at the Intelligent
Infrastructure Systems Laboratory (ISSL) Concluding remarks
Introduction: Structural health monitoring (SHM)
Structural health monitoring allows the engineer to use sensing of the structural responses in conjunction with appropriate data aggregation and model updating techniques to evaluate the condition of a structure
STATIC - BASED
DYNAMIC - BASED
Introduction: Structural health monitoring (SHM)
Dynamic measurements of the systems as they vibrate under the influence of ambient and service loads are used to characterize the structural condition at any given time
Localization of damage is achieved by comparing characterizations in the pre- and post-damage states.
Full automation of the data aggregation and analysis is pursued for real-world applications.
A static-based SHM algorithm
Damage Identification Based on Dead Load Redistribution: Methodology
Shenton and Hu. (2006). Journal of Structural Engineering
Hypothesis: Dead load is redistributed when damage occurs in the structure.
Procedure: Static strain measurements due to dead load are used as input to
the identification procedure. The identification scheme is defined as a constrained optimization problem.
A static-based SHM algorithm
10,/)( UDU EIEIEI
Severity of damage:
AA MxqxVxM 2
2)(
)('' xMEIw
axCxCxM
xqxV
wEI AAU 0;2246 21
2431
axaCxCxM
xqxV
wEI AAD ;2246 43
2432
LxaCxCxM
xqxV
wEI AAU ;2246 65
2433
0)(;0)(;0)0(;0)0( '33
'11 LwLwww
)()();()( '2
'121 awawawaw
)()();()( '3
'232 awawawaw
DU EIyxMxEIyxMx /)()(;/)()(
Damage location:Damage zone length:
a
Analytical model of damaged fixed-fixed beam
A static-based SHM algorithm
Analytical model with elements of discrete length
k
jmj
mj
tjaf
1
),,(
Minimize:
Subjected to:
LLa ;10;0
However, niiaanLlnLl i ,.......1);1(//
Therefore, minimize:
k
jmj
mj
tj
iaf1
),(
Subjected to: 10;,......,1 nai
A vibration-based SHM algorithm
Vibration-based Damage Detection of Structures by Genetic Algorithm Hao and Xia. (2002). Journal of Computing in Civil Engineering
Hypothesis: Structural damage is usually evidenced as localized modification on the stiffness configuration of an structure, leading to a change on the modal parameter values.
Procedure: Modal parameters, calculated from vibration data, are used as input to the identification procedure. The identification scheme is defined as a optimization problem and solved using a real-coded genetic algorithm
A vibration-based SHM algorithm
EA,
Damage identification scheme: optimization problem
Minimize: EATEAEA VVWVVJVVWJ )()()(22
where:
Frobenius norm
Changes in the modal parameters
Stiffness reduction factor (SRF)
Diagonal positive definite matrix of the weight for each term
V
WAnalytical and experimental data
Subjected to: 01
Number of measured points:j-th component of the i-th mass normalized mode shape:
Undamaged and damaged states:i-th eigenvalue:Number of measured modes:
Three objective functions are proposed:
Frequency (Eigenvalue) changes
Mode shape changes:
Frequency changes combined with mode shape changes:
2
0
0
1
2 })({
E
Ui
Ui
Di
A
i
iinm
iiWJ
21
0
1
2 })({
np
j
EUij
Dij
Aijij
nm
iiWJ
21
0
1
2
2
0
0
1
2 })({})({
np
j
EUij
Dij
Aijij
nm
ii
E
Ui
Ui
Di
A
i
iinm
ii WWJ
nmi
DU ,
np
ij
A vibration-based SHM algorithm
Wireless sensor role in SHM The SHM system based on Wireless Sensor Networks (WSN) has shown considerable promise.
It has several advantages over most traditional SHM systems: 1. Low production and maintenance cost.2. Fast installation 3. Reprogrammable software and convenient reconfiguration.
Using WSN, a dense deployment of measurement points in a SHM system is possible, which helps to refine the damage detection results
Wireless sensor role in SHM
1. On-board microprocessor2. Sensing capability3. Wireless communication4. Battery powered5. low cost
Berkeley Mote Mica2 (2004)
BTnode rev3 (2004)
U3 (2002)Prototype by Lynch (2002)1. On-board microprocessor2. Sensing capability3. Wireless communication4. Battery powered5. low cost
iMote2 (2004)
Wireless sensor role in SHM
Despite the potentiality offered by WS, some hardware limitations needs to be addressed when pursuing real SHM implementations using wireless sensors. Some of these hardaware limitations are associated to:
Wireless communication Time synchronization among sensors Reduced processing and memory capacity Power management
Previous implementations
WS nodes deployed on one of the beam girders (after Gangone et al, 2007)
Clarkson University researchers have implemented a wireless sensor system for modal identification of a full-scale bridge structure in New York
Previous implementations
Layout of nodes deployed on The Golden Gate Bridge (after Kim et al., 2007)
At the University of California, Berkeley researchers have designed and deployed a wireless sensor network on the Golden Gate Bridge.
Researchers at the UIUC have experimentally validated a SHM system employing a smart sensor network deployed on a scale three-dimensional truss model
Previous implementations
SHM implementation under hierarchical architecture (after Spencer and Nagayama, 2006)
Current SHM – WS research at the Intelligent Infrastructure Systems Laboratory (ISSL)
Researchers have primarily focused on developing Structural health monitoring (SHM) strategies to detect, locate and quantify damage, often using centralized data processing strategies
However, communication and power requirements of such centralized techniques do not match the capabilities offered by current wireless sensor technology
Research efforts at the ISSL are associated to develop distributed processing systems capable of fully utilizing wireless sensor embedded processing capacities to reduce communication load and energy consumption
ISSL : https://engineering.purdue.edu/IISL/
Damage Location Assurance Criterion (DLAC) DLAC approach identifies damage by evaluating the linear
correlation between frequency change vectors obtained by experimental measurements and an analytical model.
jT
jT
jT
jDLAC
2
Experimental frequency change vectors
Analytical frequency change vectors
healthydamagehealthy /
ahealthy
aj
ahealthyj /
DLAC implementation using wireless sensors
N : # SamplesW:# NF
Where: (N >> W)
DLAC implementation using wireless sensors
DLAC implementation using wireless sensors
DLAC implementation using wireless sensors
The implementation initializes the process by grouping the entire WSN in leaf sensor communities, each having cluster or leader nodes.
A first network composed only by cluster nodes perform an initial distributed modal identification, whose results are fed to a level-1 flexibility-based damage detection technique to localize regions of potential damage.
A second distributed modal identification is then performed by a reconfigured network that is composed by clusters and corresponding leaf sensor communities included and surrounded the determined regions of damage.
Finally, updated modal parameters are fed into a level-2 flexibility-based damage detection technique to detect damaged locations. CLUSTER NODES
LEAF NODES IMPLEMENTATION LAYOUT
Evaluation of a distributed flexibility-based damage detection technique for WS
C1,C2,C3,C4,C5 DEFINE SENSOR COMMUNITIESREGION OF DAMAGE
C1 C2 C3 C4 C5
Flexibility-based damage detection strategies
The Angles-between-String-and-Horizon (ASH) flexibility-based damage detection technique is proposed for structures dominated by beam-like behavior. The method computes the changes in angles between string-and-horizon of beam elements induced by the presence of damage.
The Axial Strain (AS) flexibility-based damage detection technique is proposed for structures mainly dominated by truss behavior. The idea is that if members in a structure are dominated by axial forces, the axial strain will be a better damage indicator than deflection.
Both techniques are supposed to be subsequently applied to refine the extent of potential damage locations up to an
accurate detection.
Distributed implementation
The evaluation is performed using a 3D steel truss structure. Wired sensors, deployed on the truss frontal panel joints and idealized as wireless sensor units, are employed to acquire horizontal and vertical acceleration data with Fs=250 Hz. Each damage scenario is recreated by replacing “damaged” members
with members having a reduced area of 52.7% of the original.
Experimental validation and results
The proposed two-level damage detection strategy is then used by considering the truss under two types of structural behaviors:
1. When considered globally, the truss is assumed to behave as a beam. Therefore the ASH method is used as level-1 damage detection technique with bay as a potentially damaged region.
2. Once damaged regions are detected, the AS method is used as level-2 damage detection technique having truss members be potentially damaged.
Experimental validation and results
CLUSTER NODESLEAF NODES
MAGNETIC SHAKER
TRUSS FRONTAL PANEL
D3 D1 D3
D2 D1
BAY # 6BAY # 1 BAY # 14
C1,C6 AND C13 DEFINE 1th 6th and 13th SENSOR COMMUNITIES
C6C1 C13
Experimental results4TH BAY 11TH AND 12TH BAY
0 5 10 150
0.2
0.4
0.6
0.8
1
1.2
x 10-5Level 1 - Damage Detection Results
Truss Bay Number
AS
H F
lexi
bilit
y D
amag
e In
dica
tor
7 8 9 101112131415161718190
1
2
3
4
5
6x 10-6
Level 2 - Damage Detection Results for Elements 12 and 41
Truss Element Number
AS
Fle
xibi
lity
Dam
age
Indi
cato
r
30 35 40 45 50 550
0.5
1
1.5
2
2.5 x 10-5
Truss Element Number
AS
Fle
xibi
lity
Dam
age
Indi
cato
r
Concluding remarks
SHM is a technique involving a set of procedures to determine the condition of a civil structure providing spatial and quantitative information about structural damage
Ability to continuously monitor the integrity of civil infrastructure offers the opportunity to reduce maintenance and inspection costs, and ensure a more reliable inspection than traditional methodologies
However, SHM algorithms must be robust enough to account for real implementation issues that can reduce their usefulness. Corruption of data due to experimental uncertainties, characterization of environment, reliable analytical models or small damage influence on early stages must be considered
Concluding remarks
Wireless sensors have become a promising and novel solution for SHM applications during recent times, due to their low implementation costs and embedded computational capacities.
However, SHM algorithms must be co-designed in parallel with WS hardware limitations to ensure power efficiency and scalability in the network and guarantee successful monitoring results
top related