A SCALABLE NEURAL-NETWORK MODULAR-ARRAY ARCHITECTURE FOR REAL-TIME MULTI-PARAMETER DAMAGE DETECTION IN PLATE STRUCTURES USING SINGLE SENSOR OUTPUT

2012 ◽  
Vol 11 (04) ◽  
pp. 1250024 ◽  
Author(s):  
SANJAY GOSWAMI ◽  
PARTHA BHATTACHARYA
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Real Time ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Principal Component ◽  
Damage Classification ◽  
Damage Scenarios ◽  
Array Architecture ◽  
Damage States

A scalable modular neural network array architecture has been proposed for real time damage detection in plate like structures for structural health monitoring applications. Damages in a plate like structure are simulated using finite element method of numeric system simulation. Various damage states are numerically simulated by varying Young's modulus of the material at various locations of the structure. Transient vibratory loads are applied at one end of the beam and picked at the other end by means of point sensors. The vibration signals thus obtained are then filtered and subjected to wavelet transform (WT) based multi resolution analysis (MRA) to extract features and identify them. The redundant features are removed and only the principal features are retained using principal component analysis (PCA). A large database of principal features (the feature base) corresponding to different damage scenarios is created. This feature base is used to train individual multi layer perceptron (MLP) networks to identify different parameters of the damage such as location and extent (Young's modulus). Individually trained MLP units are then organized and connected in parallel so that different damage parameters can be identified almost simultaneously, on being fed with new signal feature vectors. For a given case, damage classification success rate has been found to be encouraging. The main feature of this implementation is that it is scalable. That is, any number of trained MLP units capable of identifying a certain parameter of damages can be integrated into the architecture and theoretically it will take almost the same time to identify various damage parameters irrespective of their numbers.

scholarly journals Separating damage from environmental effects affecting civil structures for near real-time damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Principal Component ◽  
Detection Methods ◽  
Real Time Monitoring ◽  
Civil Structures ◽  
Data Set ◽  
Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

The impact of body position and exercise on the measurement of liver Young’s modulus by real-time shear wave elastography

2021 ◽  
pp. 1-10
Author(s):  
Xia Jiang ◽  
Li Li ◽  
Hong-Yuan Xue
Keyword(s):  
Healthy Volunteers ◽  
Real Time ◽  
Young’S Modulus ◽  
Shear Wave ◽  
Supine Position ◽  
Young's Modulus ◽  
Body Position ◽  
Shear Wave Elastography ◽  
Lateral Position ◽  
Left Lateral Position

BACKGROUND: In the past ten years, liver biopsies have been used as a method to accurately diagnose the stage of fibrosis. OBJECTIVE: This study aimed to evaluate whether body position and exercise affect the measurement of liver Young’s modulus of healthy volunteers by real-time shear wave elastography (RT-SWE). Methods: RT-SWE was used to measure liver Young’s modulus in the supine and left lateral positions of 70 healthy volunteers at rest and measure the liver Young’s modulus in the lying position before exercise, and at zero, five, and ten minutes of rest after exercise. RESULTS: The liver Young’s modulus in the left lateral position was significantly higher than in the supine position (P< 0.05), and the measured value in the supine position was more stable than the left lateral position. The liver Young’s modulus measured at zero minutes after exercise was significantly higher than that measured before exercise (P< 0.05). The liver Young’s modulus measured at five minutes after exercise was significantly higher than that measured at zero minutes after exercise (P<0.05) and was not statistically different from the measured value before exercise (P> 0.05). The liver Young’s modulus measured at ten minutes after exercise was significantly higher from that measured at zero minutes after exercise (P< 0.05) and was not statistically different from the measured value at five minutes after exercise (P> 0.05). CONCLUSION: Body position and exercise have a significant impact on the measurement of liver Young’s modulus. It is recommended that the examinees take a supine position during the measurement, and measurement should be conducted at least ten minutes after exercise.

scholarly journals A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Mahmoud M. Reda Taha
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Damage Classification ◽  
Integrated Method ◽  
Damage Recognition ◽  
Wavelet Energy ◽  
Challenging Tasks ◽  
Damage States ◽  
Artificial Neural Network Ann ◽  
American Society

Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.

Damage Classification in CFRP Laminates Using Principal Component Analysis (PCA) Approach

2012 ◽  
Vol 225 ◽  
pp. 189-194
Author(s):  
Mohamed Thariq Hameed Sultan ◽  
Azmin Shakrine M. Rafie ◽  
Noorfaizal Yidris ◽  
Faizal Mustapha ◽  
Dayang Laila Majid
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Impact Damage ◽  
Research Work ◽  
Principal Component ◽  
Damage Classification ◽  
Cfrp Laminates ◽  
Work Related ◽  
Detection And Identification ◽  
The Impact

Signal processing is an important element used for identifying damage in any SHM-related application. The method here is used to extract features from the use of different types of sensors, of which there are many. The responses from the sensors are also interpreted to classify the location and severity of the damage. This paper describes the signal processing approaches used for detecting the impact locations and monitoring the responses of impact damage. Further explanations are also given on the most widely-used software tools for damage detection and identification implemented throughout this research work. A brief introduction to these signal processing tools, together with some previous work related to impact damage detection, are presented and discussed in this paper.

A New Damage Index Using FRF Data, 2D-PCA Method and Pattern Recognition Techniques

2017 ◽  
Vol 17 (08) ◽  
pp. 1750090 ◽  
Author(s):  
F. Khoshnoudian ◽  
S. Talaei
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Damage Identification ◽  
Damage Index ◽  
Principal Component ◽  
Frame Structure ◽  
Damage Scenarios ◽  
Pattern Recognition Techniques ◽  
Damage Indices ◽  
Truss Bridge

A pattern recognition-based damage detection method using a brand-new damage index (DI) obtained from the frequency response function (FRF) data is proposed in this paper. One major issue of using the FRF data is the large size of input variables. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices by applying a data reduction technique called the two-dimensional principal component analysis (2D-PCA). The proposed damage indices can be used as the unique patterns. After introducing the damage indices, a dataset of damage scenarios and related patterns is composed. Pattern recognition techniques such as the artificial neural networks and look-up-table (LUT) method are employed to find the most similar known DI to the unknown DI obtained for the damaged structure. As the result of this procedure, the actual damage location and severity can be determined. In this paper, the 2D-PCA and LUT method for damage detection is introduced for the first time. The damage identification of a truss bridge and a two-story frame structure is performed for verification of the proposed method, considering all single damage cases as well as many multiple damage scenarios. In addition, the robustness of the proposed algorithm to measurement noise was investigated by polluting the FRF data with 5%, 10%, 15% and 20% noises.

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