A New Damage Alarm Index Based on Frequency Deviation for Structural Health Monitoring

2013 ◽  
Vol 351-352 ◽  
pp. 1269-1272
Author(s):  
Yan Sheng Song ◽  
Wei Ning Ni ◽  
Zong Guang Sun
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Steel Frame ◽  
Frequency Deviation ◽  
Test Cases ◽  
Normal Range ◽  
Structural Health ◽  
Damage Alarming

Based on the statistics probability of certain order frequenciy deviates from its normal range, this paper puts forward a new damage alarm index and corresponding damage alarming method for structural health monitoring. Demonstrating the feasibility of this method, this article introduces the damage alarming method to analize the benchmark steel frame in frequency domain. The results show the abnormal index and its corresponding alarming method defined in sense of statistics indicates the abnormity of corresponding test cases clearly.

Damage Alarming Method Based on Statistics to a Benchmark Structure

2011 ◽  
Vol 368-373 ◽  
pp. 2299-2302
Author(s):  
Yan Sheng Song ◽  
Zong Guang Sun ◽  
Fan Gu
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Test Cases ◽  
Statistic Method ◽  
Structural Health ◽  
Damage Alarming

This paper addresses a new damage alarming method of structural health monitoring (SHM) which utilizes statistic method on information in frequency domain. The emphasis in this paper is on the application of this method to the benchmark structure by introduced an abnormal index. The results show this method could indicate the abnormity of corresponding test cases clearly.

A New Abnormal Index Based on Frequency Change for Structural Health Monitoring

2012 ◽  
Vol 166-169 ◽  
pp. 1295-1298
Author(s):  
Yan Sheng Song ◽  
Zong Guang Sun ◽  
Nai Zhi Zhao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Frame ◽  
Frequency Change ◽  
Test Cases ◽  
Structural Health ◽  
Probability And Statistics

This paper demonstrates a new abnormal index based on frequency change for structural health monitoring (SHM) which utilizes probability and statistics method. And it was introduced to analyze a steel frame. The results show it could indicate the abnormity of corresponding test cases clearly.

Nonlinear Modeling of Cracked Beams for Impedance Based Structural Health Monitoring

2017 ◽  
Author(s):  
Naserodin Sepehry ◽  
Firooz Bakhtiari-Nejad ◽  
Mahnaz Shamshirsaz ◽  
Weidong Zhu
Keyword(s):  
Structural Health Monitoring ◽  
Linear System ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Impedance Method ◽  
Breathing Crack ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
System Equations

One of the main objectives of the structural health monitoring by piezoelectric wafer active sensor (PWAS) using electromechanical impedance method is continuously damage detection applications. In present work impedance method of beam structure is considered and the effect of early crack using breathing crack modeling is studied. In order to model the effect of a crack in beam, the beam is connected with a rotational spring in crack location. The Rayleigh–Ritz method is used to generate ordinary differential equation of cracked beam. Firstly, only open crack is considered that this is leads to linear system equation. In linear system, time domain system equations are converted to frequency domain, and then impedance of PWAS in frequency domain is calculated. Secondly, the breathing crack is modeled to be fully open or fully closed. This phenomenon leads to the nonlinear system equations. These nonlinear equations are solved using pseudo-arc length continuation scheme and collocation method for any harmonic voltage applied to actuator. Then impedance of PWAS is calculated. Two methods are used to detect early crack using breathing crack modeling on PWAS impedance. At the first, frequency response of breathing crack in the frequency range with its sub-harmonics is calculated. Second, only frequency response of one harmonic is computed with its super-harmonics. Finally, the detection method of linear is compared with nonlinear model.

scholarly journals SHMnet: Condition assessment of bolted connection with beyond human-level performance

2019 ◽  
Vol 19 (4) ◽  
pp. 1188-1201 ◽  
Author(s):  
Tong Zhang ◽  
Suryakanta Biswal ◽  
Ying Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Steel Frame ◽  
Mode Shapes ◽  
Repeated Impact ◽  
Traditional Methods ◽  
Bolted Connection ◽  
Structural Health ◽  
Sensor Locations

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet .

Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Eloi Figueiredo ◽  
Gyuhae Park ◽  
Kevin M. Farinholt ◽  
Charles R. Farrar ◽  
Jung-Ryul Lee
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Active Sensing ◽  
Response Functions ◽  
Active Sensors ◽  
Frequency Response Functions ◽  
Structural Health

In this paper, time domain data from piezoelectric active-sensing techniques is utilized for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, their ability to provide known repeatable inputs for active-sensing approaches to SHM makes the development of SHM signal processing algorithms more efficient and less susceptible to operational and environmental variability. However, to date, most of these techniques have been based on frequency domain analysis, such as impedance-based or high-frequency response functions-based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or other analysis for damage-sensitive feature extraction. Therefore, this study investigates the use of a time-series predictive model which utilizes the data obtained from piezoelectric active-sensors. In particular, time series autoregressive models with exogenous inputs are implemented in order to extract damage-sensitive features from the measurements made by piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were artificially imposed. The performance of this approach is compared to that of analysis based on frequency response functions and its capability for SHM is demonstrated.

A Structural Damage Alarm Index Based on the Statistical Probability of Abnormal Frequency Offset

2012 ◽  
Vol 226-228 ◽  
pp. 1214-1217
Author(s):  
Yan Sheng Song ◽  
Zong Guang Sun ◽  
Li Ye Sun
Keyword(s):  
Frequency Domain ◽  
Structural Damage ◽  
Frequency Offset ◽  
Steel Frame ◽  
Frame Structure ◽  
Test Cases ◽  
Statistical Probability ◽  
Damage Alarming ◽  
Steel Frame Structure

Based on the statistical probability of abnormal frequency offset, this paper puts forward a new structural damage alarm index. Demonstrating the feasibility of corresponding structural damage alarming method, this article introduces the index to analize a steel frame structure in frequency domain. The results show the abnormal index defined in sense of statistics indicates the abnormity of corresponding test cases clearly.

Uncertainty Modeling and Quantification for Structural Health Monitoring Features Derived from Frequency Response Estimation

2013 ◽  
Vol 569-570 ◽  
pp. 1148-1155
Author(s):  
Zhu Mao ◽  
Michael D. Todd
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Structural Engineering ◽  
Hypothesis Test ◽  
Transfer Model ◽  
Type I ◽  
Operating Characteristics ◽  
Structural Health

System identification in the frequency domain plays a fundamental role in many aspects of mechanical and structural engineering. Frequency domain approaches typically involve estimation of a transfer function, whether it is the usual frequency response function (FRF) or an output-to-output transfer model (transmissibility). The field of structural health monitoring, which involves extracting and classifying features mined from in-sit structural performance data for the purposes of damage condition assessment, has exploited many features for this purpose that inherently are derived from estimations of frequency domain models such as the FRF or transmissibility. Structural health monitoring inevitably involves a hypothesis test at the classification stage such as the (common) binary question: are the features mined from data derived from a reference condition or from data derived from a different (test) condition? Inevitably, this decision involves stochastic data, as any such candidate feature is compromised by error, which we categorize as (i) operational and environmental, (ii) measurement, and (iii) computational/estimation. Regardless of source, this noise leads to the propagation of error, resulting in possible false positive (Type I) errors in the classification. As such, the quantification of uncertainty in the estimation of such features is tantamount to making informed decisions based on a hypothesis test. This paper will demonstrate several statistical models that describe the uncertainty in FRF estimation and will compare their performance to features derived from them for the purposes of detecting damage, with ultimate performance evaluated by receiver operating characteristics (ROCs). A simulation and a plate subject to single-input/single-output vibration testing will serve as the comparison testbeds.

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