Experimental activities on damage detection using magnetostrictive actuators and statistical analysis

2000 ◽  
Author(s):  
Ernesto Monaco ◽  
Gianluca Calandra ◽  
Leonardo Lecce
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Magnetostrictive Actuators

Shaft Crack Detection Using Statistical Analysis

2009 ◽  
Author(s):  
Zhuang Li ◽  
Lei Jin ◽  
Ning Zhang ◽  
Yang Zhou
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Crack Detection ◽  
Damage Identification ◽  
Vibration Data ◽  
Rotating Systems ◽  
Detection And Diagnosis ◽  
The Relationship ◽  
Cracked Shafts ◽  
Damage Types

Cracks and voids are common defects in rotating systems and are a precursor to fatigue-induced failure. The application of statistical analysis, as a tool for damage identification and health monitoring in rotating machinery, is investigated. Experimental vibration data were collected for a set of health and cracked shafts. Formal statistical models have been proposed to describe the relationship between the vibration signals and the existence of damage. Damage detection and diagnosis are implemented based on statistical estimation and hypothesis testing. Such a statistical model provides a screening technique to detect other damage types. As a result, the proposed methods can improve the power of damage detection.

Statistical analysis and ground-based testing of the on-orbit Space Shuttle damage detection sensors

2005 ◽  
Author(s):  
Brian H. Miles ◽  
Elizabeth A. Tanner ◽  
John P. Carter ◽  
Gary W. Kamerman ◽  
Robert Schwartz
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Space Shuttle ◽  
Orbit Space

scholarly journals Impact Damage Detection in CFRP Using Statistical Analysis of Resistance-Temperature Characteristics

2011 ◽  
Vol 5 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Yoshiro SUZUKI ◽  
Akira TODOROKI ◽  
Yoshihiro MIZUTANI ◽  
Ryosuke MATSUZAKI
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Impact Damage ◽  
Temperature Characteristics

scholarly journals Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 663 ◽  
Author(s):  
Eugene OBrien ◽  
Muhammad Arslan Khan ◽  
Daniel Patrick McCrum ◽  
Aleš Žnidarič
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Interaction Model ◽  
Statistical Properties ◽  
Vehicle Weight ◽  
Motion System ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Damage ◽  
Bridge Weigh In Motion

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

Multivariate statistical analysis for early damage detection

2013 ◽  
Vol 56 ◽  
pp. 273-285 ◽  
Author(s):  
João Pedro Santos ◽  
Christian Crémona ◽  
André D. Orcesi ◽  
Paulo Silveira
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Early Damage

J0405-1-1 Unsupervised damage detection in CFRP using statistical analysis of temperature characteristic of resistance

2009 ◽  
Vol 2009.6 (0) ◽  
pp. 361-362
Author(s):  
Yoshiro SUZUKI ◽  
Akira TODOROKI ◽  
Kosuke TAKAHASHI ◽  
Yoshihiro MIZUTANI ◽  
Ryosuke MATSUZAKI
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Temperature Characteristic
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