Spatial-derivative-based compression approach for distributed temperature data

Area of Science:

• Optical Sensing Technologies
• Data Science and Signal Processing

Background:

• Advanced distributed optical sensors generate large datasets, posing significant processing and storage challenges.
• Current data handling methods struggle to efficiently manage high-resolution, long-range measurements from optical fiber sensors.

Purpose of the Study:

• To introduce a novel data compression method specifically designed for distributed temperature sensing data.
• To address the challenge of processing and storing vast amounts of data from advanced optical sensor systems.

Main Methods:

• The proposed method utilizes the spatial derivative of the temperature signal to identify and eliminate redundant data points.
• This approach is applied to temperature data, accommodating both heating and cooling variations along the sensing fiber.

Main Results:

• An average data compression ratio of 1.5× was achieved through the removal of redundant spatial temperature variations.
• The compression method successfully preserved the spatial resolution of the temperature measurements.

Conclusions:

• The spatial derivative-based compression method offers a simple and effective solution for managing large datasets from distributed temperature sensors.
• This technique is versatile and applicable to various thermal profiles, enhancing the practicality of optical sensing data analysis.