Fatigue Monitoring of Welded Details

2018 ◽  
pp. 107-127
Author(s):  
Yang Deng ◽  
Aiqun Li
Keyword(s):  
Fatigue Monitoring

scholarly journals Ultrasonic and thermographic fatigue monitoring on a full-scale CFRP aeronautical component after repairing

2021 ◽  
Vol 1038 (1) ◽  
pp. 012027
Author(s):  
V Dattoma ◽  
R Nobile ◽  
F Palano ◽  
F W Panella ◽  
A Pirinu ◽  
...  
Keyword(s):  
Full Scale ◽  
Fatigue Monitoring

Dual-Camera Vision System for Fatigue Monitoring

2012 ◽  
Vol 726 ◽  
pp. 226-232 ◽  
Author(s):  
Tomasz Giesko
Keyword(s):  
Fatigue Fracture ◽  
Vision System ◽  
Specimen Surface ◽  
Fatigue Monitoring ◽  
Computer Unit ◽  
2D Images

The article presents a dual-camera vision system for fatigue monitoring composed of a vision unit, a camera positioning set and a computer unit. Vision modules are mounted onto the 4DOF positioning sets, which allows for an easy determination of the position of the camera in relation to the sample. The application of motorized measurement lenses with changeable configuration, thanks to the alteration of the distance of observation and the vision angle, enables the adaptation of the system to different scales of observation of the fatigue processes in the specimen surface. Automatic focus setting is realised with the use of the implemented algorithm. The software developed allows for the analysis of fatigue fracture for two 2D images or the 3D stereovision image.

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

2011 ◽  
Vol 26 (7) ◽  
pp. 513-523 ◽  
Author(s):  
Nizar Lajnef ◽  
Mohamed Rhimi ◽  
Karim Chatti ◽  
Lassaad Mhamdi ◽  
Fred Faridazar
Keyword(s):  
Data Interpretation ◽  
Sensing System ◽  
Fatigue Monitoring ◽  
Smart Sensing

scholarly journals Famos 4 Wind a New Method for the Fatigue Monitoring of Wind Energy Plants

2015 ◽  
Vol 133 ◽  
pp. 84-89 ◽  
Author(s):  
Steffen Bergholz ◽  
Jürgen Rudolph ◽  
Adrian Willuweit
Keyword(s):  
Wind Energy ◽  
New Method ◽  
Fatigue Monitoring ◽  
Energy Plants

Fatigue Monitoring and Management across Different Industries

2016 ◽  
Vol 60 (1) ◽  
pp. 993-996 ◽  
Author(s):  
Ranjana K. Mehta ◽  
S. Camille Peres ◽  
Linsey M. Steege ◽  
Jim R. Potvin ◽  
Mike Wahl ◽  
...  
Keyword(s):  
Risk Factor ◽  
Management Practices ◽  
Oil And Gas ◽  
Physiological State ◽  
Work Conditions ◽  
Severe Injuries ◽  
Circadian Phase ◽  
Current State ◽  
Fatigue Monitoring ◽  
Performance Capability

Fatigue, often defined as a physiological state of reduced mental or physical performance capability resulting from sleep loss, circadian phase, or workload (physical or cognitive), has been implicated as a critical risk factor resulting in severe injuries and accidents. A great deal of research has been done into the identification, measurement, and management of fatigue, however it is still poorly understood. This may be due to the characteristics and variability of work conditions across different industries; for example, fatigue in manufacturing is largely related to physical demands, and in aviation fatigue is related to sleep and shift-work. This panel will comprise of academics and practitioners across manufacturing, healthcare, transportation, aviation, and oil and gas industries. Topics covered within each industry will include fatigue causes and consequences, existing fatigue monitoring/management practices, barriers to fatigue monitoring and management, and recommendations/discussions around improving the current state.

scholarly journals Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 621
Author(s):  
Elaheh Talebi ◽  
W. Pratt Rogers ◽  
Tyler Morgan ◽  
Frank A. Drews
Keyword(s):  
Health And Safety ◽  
Time Of Day ◽  
Weather Data ◽  
Data Sets ◽  
Leading Indicators ◽  
Safety Control ◽  
Model Complex ◽  
Fatigue Monitoring ◽  
The Impact ◽  
Operational Data

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

Ultrasonic and Eddy-Current Fatigue Monitoring of Austenitic Steel Welded Joints

2021 ◽  
Vol 57 (7) ◽  
pp. 570-578
Author(s):  
A. V. Gonchar ◽  
V. A. Klyushnikov ◽  
V. V. Mishakin ◽  
M. S. Anosov
Keyword(s):  
Austenitic Steel ◽  
Eddy Current ◽  
Welded Joints ◽  
Fatigue Monitoring
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