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

Abstract

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.

Keywords

• Sensor fault detection
• Multistage turbine air compressor
• Multiple linear regression
• Neural network
• Oil and gas industry