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THERE NEEDED TO BE A WAY TO PROCESS GAS LEAK TESTING DATA FROM THE NEW SENSORS QUICKLY, WITHOUT COMPROMISING RELIABILITY.
The gas company had developed a new sensor that was able to detect gas leaks faster and more reliably, which would potentially save lives, in addition to cutting the higher expenses of older technologies. As the sensors began collecting data from the pipelines, they needed a machine learning algorith that could decipher the sensor’s data quickly, and reliably report potential leaks.
This project had great potential, but the testing model needed to be strong in order to submit the sensor for approval by the governing agencies. This approval would create the opportunity for the sensor to be used throughout the country, improving the industry for both the service providers and home owners.
The company hired OWLLytics to create a machine-learning algorithm that could be utilized to detect these leaks faster, reducing the amount of time it took to complete a gas leak test and process the results.