Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data

2012 ◽  
Vol 45 ◽  
pp. 166-176 ◽  
Author(s):  
X.W. Ye ◽  
Y.Q. Ni ◽  
K.Y. Wong ◽  
J.M. Ko
Keyword(s):  
Fatigue Life ◽  
Statistical Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Bridges ◽  
Life Assessment ◽  
Monitoring Data ◽  
Structural Health ◽  
Fatigue Life Assessment

RELIABILITY ASSESSMENT FOR AN AUTOMOBILE CRANKSHAFT UNDER RANDOM LOADING

2016 ◽  
Vol 78 (6-10) ◽  
Author(s):  
S.S.K. Singh ◽  
S. Abdullah ◽  
N.A.N. Mohamed
Keyword(s):  
Fatigue Life ◽  
Structural Health Monitoring ◽  
Markov Process ◽  
Health Monitoring ◽  
Reliability Assessment ◽  
Life Assessment ◽  
Loading History ◽  
Random Loading ◽  
Structural Health ◽  
Life Data

This paper presents the stochastic process for reliability  assessment based on the fatigue life data under random loading for structural health monitoring of an automobile crankshaft due tofatigue failure. This is based on reported failure of the component due to the effect of the random loads that acts on the component during its operating condition over a given period of time. Since there are significant limitations of the experimental analysis in terms of actual loading history, therefore, the reliability assessment is considered to be less accurate. Hence, the reliability assessment based on fatigue life data using the Markov process by incorporating loading data to synthetically generate loading history has been proposed in this study. The Markov process has the capability of continuously updating the loading history data to reduce the intervals between each data point for reliability assessment based on the fatigue life data. The accuracy of the proposed monitoring system for reliability assessment was validated through its statistical method. The reliability assessment from the Markov process corresponded well by providing an accuracy of more than 95% when compared towards the actual sampling data. The reliability of the crankshaft based on the fatigue life assessment provides a highly accurate  for the improvement and control of risk factors in terms of structural health monitoring by overcoming the extensive time and cost required for fatigue testing

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

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