Development of smart sensors for structural integrity monitoring and damage detection has been advanced remarkably in recent years. Nowadays optical fiber sensors have attracted many researchers' interests for their attractive features, such as multiplexing capability, durability, lightweight, electromagnetic interference immunity.
The other study is ongoing and has investigated new methods to improve deepwater monitoring and addresses installation of advanced sensors on already-deployed risers, flowlines, trees, and other subsea assets. These highly sensitive monitoring systems will provide operators a cost-effective method for detecting and responding to potential failures, flow assurance issues, and catastrophic events.
In the past, a major shortcoming of post-installed monitoring systems has been inadquately coupled between the sensor and structure. A significant achievement was recently made in bonding methods for sufficient coupling in Oil & Gas field environment. There are two challenges in calibration for the post installed sensors to determine "the baseline or zero state of stress" and "sensitivity of the fiber Bragg gratings on the clamped against the actual load changes" on the structure.
One of the effective monitoring system would be enhanced by calibration methods employing fundamental predictive methods to establish both baseline and sensitivity values. The transition layer between surface and deep water levels (thermocline) required to isolate temperature effects from structural data using temperature compensation for the strain sensors with developed and integrated into the software algorithm.
However, a further validation with the empirical measurement based that correlated methorogical environment load for years should be required since is a difficult challenge to identify and select which results would be the correct either "the theoretical analysis program" or "measured data from the field".
In this paper, the results of low frequency response for the structure Integrity monitoring from utilizing statistical data analysis that consisted with big data from meteorological environment data, accelerometer, tiltmeter and strain sensor from fixed platform in Oil & Gas field is presented.