Curve Similarity Analysis for Reducing the Temperature Uncertainty of Optical Sensor for Oil-Tank Ground Settlement Monitoring

Abstract

A nonuniform temperature field can deteriorate the performance of sensors, especially those working in the field, such as an optical sensor for oil-tank ground settlement (GS) monitoring. In this case, the GS monitoring employs hydraulic-level-based sensors (HLBS), which are uniformly installed along with the oil-tank basement perimeter and are all connected by hydraulic tubes. Then, the cylinder structure of the oil tank itself can create a strong temperature difference between the sensors installed in the sunlit front and those in the shadow. Practically, this sunlight-dependent difference can be over 30 °C, by which the thermal expansion of the measuring liquid inside the connecting hydraulic tubes keeps on driving a movement and, thereby, leads to fluctuations in the final result of the oil-tank GS monitoring system. Now, this system can work well at night when the temperature difference becomes negligible. However, temperature uncertainty is generated in the GS sensors due to the large temperature difference between the sensors in the daytime. In this paper, we measured the temperature where the sensor was located. Then, we compared the results of the GS sensors with their corresponding temperatures and fitted them with two separate curves, respectively. After observing the similarity in the tendency of the two curves, we found that there was a qualitative correlative relationship between the change in temperature and the uncertainty in the sensor results. Then, a curve similarity analysis (CSA) principle based on the minimum mean square error (MMSE) criteria was employed to establish an algorithm, by which the temperature uncertainty in the GS sensors was reduced. A practical test proved that the standard deviation was improved by 73.4% by the algorithm. This work could be an example for reducing the temperature uncertainty from in-field sensors through the CSA method.

Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring

“…In a previous publication (Ref. [ 6 ]), there is a roughly correlative relationship between temperature uncertainty and temperature variation for a specific sensor, although this correlation and its corresponding coefficient vary over time. Consequently, in a certain time period, only a limited amount of data may be available to effectively grasp and understand this relationship.…”

“…As illustrated in our previous publication Ref. [ 6 ], the temperature can directly influence the GS measurement result in daytime. Furthermore, the GS measurement results at night can be considered as the ground truth results, because the temperature environment is uniform without sunshine.…”

“…Our research group initially proposed an online GS monitoring method that combines optical low-coherence interferometry with a hydrostatic leveling system (HLS), and this has made significant progress. However, one major challenge being faced is the interference caused by non-uniform temperature fields [ 6 ]. During the daytime, solar irradiation can be impeded by the large size of the oil tank, resulting in significant temperature differences of up to between the sunlit and shaded areas in the summer [ 3 , 6 ].…”

“…However, one major challenge being faced is the interference caused by non-uniform temperature fields [ 6 ]. During the daytime, solar irradiation can be impeded by the large size of the oil tank, resulting in significant temperature differences of up to between the sunlit and shaded areas in the summer [ 3 , 6 ]. This temperature difference can impact the HLS directly and lead to considerable temperature uncertainty (large fluctuation of GS measurement).…”

“…In the pre-training stage, the roughly positive correlation relationship mentioned in Ref. [ 6 ] is used; then, the practical data are incorporated into the training process. This approach allows us to effectively address the complexity of the relationship between temperature uncertainty and temperature variation while mitigating the data shortage.…”