scholarly journals Vibration‐Data Analysis. Part II. Engineering Applications

1964 ◽  
Vol 36 (5) ◽  
pp. 1020-1020
Author(s):  
Julius S. Bendat
Keyword(s):  
Data Analysis ◽  
Engineering Applications ◽  
Vibration Data

Monitoring Offshore Pipelines Using Forced Transverse Vibration: Data Analysis

1995 ◽  
Author(s):  
Hugh E. M. Hunt
Keyword(s):  
Fluid Flow ◽  
Data Analysis ◽  
Transverse Vibration ◽  
Vibration Amplitude ◽  
Support Condition ◽  
Offshore Pipelines ◽  
Vibration Data ◽  
Wavelet Methods

Abstract Vibration methods are used to identify faults, such as spanning and loss of cover, in long off-shore pipelines. A pipeline ‘pig’, propelled by fluid flow, generates transverse vibration in the pipeline and the measured vibration amplitude reflects the nature of the support condition. Large quantities of vibration data are collected and analysed by Fourier and wavelet methods.

Development of Structural Identification Accuracy Indicators Using Fuzzy Logic

1997 ◽  
Author(s):  
Stanley E. Woodard ◽  
Richard S. Pappa
Keyword(s):  
Data Analysis ◽  
Expert System ◽  
Structural Damage ◽  
Identification Accuracy ◽  
Fuzzy Expert System ◽  
Eigensystem Realization Algorithm ◽  
Automatic Control Systems ◽  
Vibration Data ◽  
Fuzzy Membership Functions ◽  
Degree Of Confidence

Abstract A fuzzy expert system was developed for autonomous in-space identification of spacecraft modal parameters. The in-space identification can be used to validate analytical predictions, detect structural damage, or tune automatic control systems as required. A fuzzy expert system determines accuracy of vibration data analysis performed autonomously using the Eigensystem Realization Algorithm. Evaluation of the data analysis output is imprecise and somewhat subjective. The expert system was developed using the knowledge provided the co-developer of the Eigensystem Realization Algorithm. The accuracy indicator represents the analyst’s degree of confidence in the analysis results. The fuzzy membership functions of the expert system were parameterized and tuned using numerical optimization.

Introduction to Tensile Testing

2004 ◽  
pp. 1-12
Keyword(s):  
Plastic Deformation ◽  
Data Analysis ◽  
Uniaxial Tension ◽  
Tensile Testing ◽  
Tensile Tests ◽  
True Stress ◽  
Stress Strain ◽  
Engineering Applications ◽  
Test Methodology ◽  
Ultrasonic Techniques

Abstract Tensile tests are performed for several reasons. The results of tensile tests are used in selecting materials for engineering applications. Tensile properties often are used to predict the behavior of a material under forms of loading other than uniaxial tension. Elastic properties also may be of interest, but special techniques must be used to measure these properties during tensile testing, and more accurate measurements can be made by ultrasonic techniques. This chapter provides a brief overview of some of the more important topics associated with tensile testing. These include tensile specimens and test machines; stress-strain curves, including discussions of elastic versus plastic deformation, yield points, and ductility; true stress and strain; and test methodology and data analysis.

A Study on Applicability of Predictive Maintenance and Vibration Data Analysis by Fast Fourier Transform into Major Equipment of Submarine

2021 ◽  
Vol 4 (1) ◽  
pp. 14-21
Author(s):  
Donghoon Kim ◽  
Sang Woo Kang ◽  
Ji Hoon Lee ◽  
Kyung Mo Nam ◽  
Seong Hun Seong ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Data Analysis ◽  
Fast Fourier Transform ◽  
Predictive Maintenance ◽  
Vibration Data ◽  
Major Equipment

scholarly journals Vibration data analysis for a commercial aircraft

Automatica ◽  
1996 ◽  
Vol 32 (12) ◽  
pp. 1689-1700 ◽  
Author(s):  
T. McKelvey ◽  
T. Abrahamsson ◽  
L. Ljung
Keyword(s):  
Data Analysis ◽  
Commercial Aircraft ◽  
Vibration Data

scholarly journals IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

2020 ◽  
Author(s):  
Sana Talmoudi ◽  
Tetsuya Kanada ◽  
Yasuhisa Hirata
Keyword(s):  
Complex System ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Analysis Method ◽  
Prior Training ◽  
Training Process ◽  
Learning Methods ◽  
Vibration Data ◽  
Smart Industry

Abstract One of the main focuses of smart industry is machinery failure predictive solutions. To achieve this, IoT-based solutions have been widely deployed. However, data processing and decision making remain challenging. The absence of enough knowledge has been the primarily limitation of statistical methods and supervised learning methods. Therefore, unsupervised learning methods are gaining more popularity but still have limits to cover effectively the pre-signs of failures due to the complexity of training process and results visualization. Previously, we proposed a novel Big Data Analysis method on audio/vibration data to cover effectively the pre-signs of failures through data visualization without complex learning or processing. We validated our proposal on a demo system. In the present work, we are using part of the MIMII dataset to test our proposed analysis method on a real-world-like data and verify the validity of our proposal on a more complex system. We are showing that we can detect abnormal machine behaviors and predict failures without prior training or knowledge of the target monitored machine.

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