DISTRIBUTED OPTICAL FIBER SENSOR FOR LONG-DISTANCE OIL PIPELINE HEALTH

A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installed along the pipeline acting as a sensor, The experiments show that the cable swells when exposed to oil and induced additional bending losses inside the fiber, and the optical attenuation of the fiber coated by a thin skin with periodical hardness is sensitive to deformation and vibration caused by oil leakage, tampering, or mechanical impact. The region where the additional attenuation occurred is detected and located by DOFS based on OTDR, the types of pipeline accidents are identified according to the characteristics of transmitted optical power received by an optical power meter, Another prototype of DOFS based on a forward traveling frequency-modulated continuous-wave （FMCW） is also proposed to monitor pipeline. The advantages and disadvantages of DOFSs based on OTDR and FMCW are discussed. The experiments show that DOFSs are capable of detecting and locating distant oil pipeline leakages and damages in real time with an estimated precision of ten meters over tens of kilometers.

A minimalist approach for detecting sensor abnormality in oil and gas platforms

Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.