Damage Identification of Beams Using Static Test Data

2003 ◽  
pp. 101-115 ◽  
Author(s):  
Mario Paola ◽  
Cristiano Bilello
Keyword(s):  
Test Data ◽  
Damage Identification ◽  
Static Test

Structural damage identification using static test data and changes in frequencies

2001 ◽  
Vol 23 (6) ◽  
pp. 610-621 ◽  
Author(s):  
X. Wang ◽  
N. Hu ◽  
Hisao Fukunaga ◽  
Z.H. Yao
Keyword(s):  
Test Data ◽  
Structural Damage ◽  
Damage Identification ◽  
Static Test ◽  
Structural Damage Identification

Damage identification from static tests by eigenparameter decomposition and sparse regularization

2019 ◽  
Vol 19 (5) ◽  
pp. 1351-1374
Author(s):  
Zhong-Rong Lu ◽  
Junxian Zhou ◽  
Li Wang ◽  
Jike Liu
Keyword(s):  
Test Data ◽  
Damage Identification ◽  
Regularization Parameter ◽  
Goal Function ◽  
Structural Safety ◽  
Sparse Regularization ◽  
Static Tests ◽  
Static Test ◽  
Static Data ◽  
Identification Approach

Identifying the damages from test data is central to assuring the structural safety. The static model is the simplest model to describe the mechanical behavior of the structure where only the stiffness is involved and it is independent of the mass and the complex damping. As a result, damage identification based on the static data will not be deteriorated by the inexact damping and the possible error in the mass. Notwithstanding, the major difficulty regarding damage identification with static test data is that the amount of the static data is quite limited and insufficient with respect to the amount of damage parameters, rendering the identification very sensitive to the measurement noise. Attempting to circumvent this difficulty, a novel damage identification approach is developed in this article where the sparse regularization is introduced to implicitly enforce the sparsity constraint of the damage locations. Moreover, in order to work well with the sparse regularization, a new goal function is established by resorting to the eigenparameter decomposition for which the decoupling feature would make the sparse regularization be tackled immediately with closed-form solutions. Then, the alternating minimization approach is used to get the solution of the new goal function and the threshold setting method is simply called to determine a proper regularization parameter. Numerical and experimental examples are studied to testify the feasibility, accuracy, and robustness of the proposed damage identification approach.

Structural Element Stiffness Identification from Static Test Data

1991 ◽  
Vol 117 (5) ◽  
pp. 1021-1036 ◽  
Author(s):  
Masoud Sanayei ◽  
Stephen F. Scampoli
Keyword(s):  
Test Data ◽  
Structural Element ◽  
Static Test ◽  
Stiffness Identification

scholarly journals Diagnostics of actuation system by Hadamard product of integrated motor current residuals applied to electro-mechanical actuators

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sreedhar Babu G ◽  
Sekhar A.S. ◽  
Lingamurthy. A
Keyword(s):  
Test Data ◽  
Hadamard Product ◽  
Current Data ◽  
Diagnostic Information ◽  
Gear Train ◽  
Static Test ◽  
Motor Current ◽  
Actuation System ◽  
Three Samples ◽  
Control Surfaces

The paper presents diagnostics methodology that can identify the event of occurrence of fault in the actuator or the linkage system of the flight control actuation system driven by Linear Electromechanical Actuators (LEMA). The standard data analysis like motor current signature analysis (MCSA) is good at identifying the incipient faults within the elements of the actuators in situations where-in the actuators are driving control surfaces. But in back driven cases, where-in LEMA is driven back by control surfaces, the faults outside the LEMAs are difficult to be detected due to higher mechanical advantages of transmission elements like roller screws, gear train and linkage arms scaling down their effects before reaching the motor. One such event occurred in a ground test, wherein the jet vanes were sheared when back driven by excessive gas dynamic forces. Neither the motor current nor the LEMA position feedback data has any clue of the instance of occurrence of such shearing. The case study is discussed in detail and diagnostics solution for such failures is proposed. A new methodology to pin point the event of occurrence is arrived at based on ground static test data of four independent channels. The same is reassured for its applicability using lab experiments on three samples mimicking the failure. The method's applicability is also extended for extracting events in actual flight, by comparing the flight telemetry data with the mimicked lab level (dry runs) data. The methodology uses the analysis of LEMA motor current data to arrive at the vital diagnostic information. The current data of LEMA directly cannot be interpreted due to non-stationary nature arising from variable speed and its pulsating form because of the pulse width modulation (PWM) switching, threshold voltages and closed loop dynamics of the servo. Hence the motor current is integrated using cumulative trapezoidal method. This integrated data is spline curve fitted to arrive at residuals vector. The Hadamard product is used on the residuals vector to amplify the information and suppress the noise. Further, normalizing is done to compare data across tests and samples. With this, necessary diagnostic information was extracted from static test data. The method is extended for extracting diagnostics information from actual flight using comparison analysis of, the test data in actual environment with mimicked lab level dry runs. It is also verified for applicability in faults directly driven by actuators in lab level experiments on three samples.

Simulation of Static Test of Air-Spring

2006 ◽  
Vol 11-12 ◽  
pp. 713-716 ◽  
Author(s):  
Feng Xiang Li ◽  
Wei Min Yang ◽  
Yu Mei Ding
Keyword(s):  
Test Data ◽  
Parameter Method ◽  
Load Step ◽  
Air Spring ◽  
Static Test ◽  
Remarkable Effect ◽  
Inner Pressure ◽  
In The Beginning ◽  
Multiple Load ◽  
Pressure Output

A method for testing an air-spring was advanced in the beginning. Then ANSYS was utilized to simulate the static test. In the simulation, multiple load steps solution was carried out through APDL *DO-LOOP and array parameter method. Scalar parameter PRESSURE was established as inner pressure tracking parameter to update inner pressure. Table parameter PRESSURES was established as inner pressure output parameter to export inner pressure of each load step. Comparison was done between simulation result and test data to prove the feasibility of simulation. Finally, some major parameters such as cord angle and initial inner pressure were taken into account, which had remarkable effect on the air-spring’s performance.

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