remaining fatigue life
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2021 ◽  
Author(s):  
Luiz Paulo Feijo ◽  
Suqin Wang ◽  
Christiane Machado

Abstract This paper focuses on Floating Production Installations, which are assets designed based on site-specific environmental conditions to determine their design service life. The longevity of these assets depends on the fatigue aspects related to the structural elements and mooring systems. Among the challenges involving the continued services of ageing assets is the integrity of these elements. When an asset reaches its end of design service life, Operators often decide to undergo a life extension process for safe continued operations. Alife extension process generally includes three phases: investigation, determination and implementation. Following a baseline inspection to determine the present conditions of the structures, engineering assessments are to be carried out to evaluate the fatigue damage through the lifecycle of the installation and therefore determine the remaining fatigue life. Collecting information to execute these assessments is challenging and can be automated with the use of digital technology. Digital tools allow an accurate collection of data, providing a continuous evaluation of the remaining fatigue life and supporting an informed decision-making process. Observing the operation of several aging assets and their structural behaviour, the parameters to be measured during the installation's lifecycle have been identified along with other aspects that also contribute to the determination of its continued service. The recommended data acquisition for relevant measurements is summarized in this paper. The application of sensors and monitoring systems on the installations allows measuring these parameters on a continuous basis, and consequently, Operators are able to determine the degradation pattern that the structure is subject to. An estimation of the remaining fatigue life can be achieved by using predictive analysis, which, along with insights of the future expected corrosion, provides Operators the necessary basis to implement corrective measures and mitigations to avoid the occurrence of a failure. This paper offers an innovative, forward-looking technology that allies physics-based processes with digital technology, supported by predictive analytics and continuous structural evaluation, to assess the integrity of an offshore asset in support of safe continued services.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haoju Hu ◽  
Huan Luo ◽  
Xiaoqiang Deng

In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large-scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short-term memory (LSTM) network has been proved to capture long-term dependencies in time-series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM-based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models.


2021 ◽  
Vol 5 (3) ◽  
pp. 76
Author(s):  
Ho Sung Kim ◽  
Saijie Huang

S-N curve characterisation and prediction of remaining fatigue life are studied using polyethylene terephthalate glycol-modified (PETG). A new simple method for finding a data point at the lowest number of cycles for the Kim and Zhang S-N curve model is proposed to avoid the arbitrary choice of loading rate for tensile testing. It was demonstrated that the arbitrary choice of loading rate may likely lead to an erroneous characterisation for the prediction of the remaining fatigue life. The previously proposed theoretical method for predicting the remaining fatigue life of composite materials involving the damage function was verified at a stress ratio of 0.4 for the first time. Both high to low and low to high loadings were conducted for predicting the remaining fatigue lives and a good agreement between predictions and experimental results was found. Fatigue damage consisting of cracks and whitening is described.


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