Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis

Carbon ◽  
2018 ◽  
Vol 139 ◽  
pp. 353-360 ◽  
Author(s):  
Siddhant Datta ◽  
Rajesh Kumar Neerukatti ◽  
Aditi Chattopadhyay
Author(s):  
Scot McNeill ◽  
Puneet Agarwal ◽  
Dan Kluk ◽  
Kenneth Bhalla ◽  
Tomokazu Saruhashi ◽  
...  

Recently, the Modal Decomposition and Reconstruction (MDR) algorithm was developed to accurately estimate fatigue damage in marine risers based on measured acceleration and angular rates at several locations. The greatest benefit for drilling risers can be derived by incorporating the method in an online, fully automated system. In this way, fatigue damage estimates are available to the crew on the rig in real-time for risk quantification and mitigation. To this end, the MDR routine was implemented for online assessment of fatigue damage along the entire riser from acceleration and angular rate measurements at typically 5–10 elevations. This paper discusses the architecture, highlights some measured data and provides results for modes, stress and fatigue damage rate for the Chikyu drilling vessel during two scientific drilling campaigns. These campaigns occurred at the Shimokita site (1180-meter water depth) and the Nankai trough site (1939-meter water depth). To the authors’ knowledge, real-time fatigue monitoring of the entire riser has not been accomplished previously. Robust incorporation of the MDR algorithm into an online computational environment is detailed, including incorporation of top tension and mud weight data from the rig, detection and removal of data errors, and streamlined flow of the data through the computational modules. Subsequently, it is shown by example how the measured accelerations and angular rates are used to determine excited modes, participating modes, stress distribution and fatigue damage along the entire Chikyu drilling riser in an online setting. The technology highlighted advances riser integrity management two steps forward by first using measured data at 5–10 locations and the MDR algorithm to reconstruct stress and fatigue damage along the entire riser, and secondly integrating this approach into a fully automated, real-time computational environment. As a result, drilling engineers are empowered with a tool that provides real-time data on the integrity of the drilling riser, enabling informed decisions to be made in adverse current or wave conditions. Measured data also serves as a benchmark for analytical model calibration activities, reducing conservatism in stress and fatigue in future deployments. Furthermore, cumulative fatigue damage can be tracked in each riser joint, enabling more effective joint rotation and inspection programs.


2015 ◽  
Author(s):  
Joseph N. Zalameda ◽  
Eric R. Burke ◽  
Michael R. Horne ◽  
James B. Bly

2009 ◽  
Vol 113 (1150) ◽  
pp. 799-810 ◽  
Author(s):  
A. Chattopadhyay ◽  
P. Peralta ◽  
A. Papandreou-Suppappola ◽  
N. Kovvali

Abstract The health monitoring and damage prognosis of aerospace hotspots is important for reducing maintenance costs and increasing in-service capacity of aging aircraft. One of the leading causes of structural failure in aerospace vehicles is fatigue damage. Based on the physical mechanism of damage nucleation and growth, a physics-based multiscale model is considered for fatigue damage assessment in metallic aircraft structures. A guided-wave based sensing approach is utilised to enable effective damage detection in a common structural hotspot: a lug joint. Finite element analysis is carried out with piezoelectric wafers bonded to the host structure and the simulated sensor signals are analysed. A damage classification strategy is developed, which integrates physically motivated time-frequency approaches with advanced stochastic modelling techniques. In particular, a variational Bayesian learning scheme is used to estimate the optimal model complexity automatically from the data, adapting the classifier for real-time use. Classification performance is studied as a function of signal-to-noise ratio and results are reported for the detection of fatigue crack damage in the lug joint. An adaptive hybrid prognosis model is proposed, which estimates the residual useful life of structural hotspots using damage condition information obtained in real-time.


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