Conference Year
2016
Keywords
Mann-Kendall, data analysis, Low Refrigerant, Big data
Abstract
A hybrid algorithm of an enhanced version of Mann-Kendall trending and data analysis is proposed to solve the limitations of current technology in detecting and diagnosing cooling system refrigerant faults in general and refrigerant leakage specifically. A data abstraction mechanism is applied over feed of temperatures and power measurement to calculate and store only the significant information for further analysis. Next, an enhanced version of Mann-Kendall trending is applied periodically over the stored data to calculate the trend strength (upward or downward) for each measurement. Finally, a harmonic mean is utilized to balance the trends contribution and evaluate the result against a threshold value for potential faults.  Such an algorithm is expected to have an important positive impact, because it is designed to accurately detect low refrigerant at an early stage. This should help in the following ways: (a) to reduce the impact of refrigerant emissions on climate, and (b) to potentially reduce the U.S. energy use by more than 0.1–.02 quad per year. This algorithm is a robust first step towards leveraging the latest technology advancements, especially in computer science and mathematics, in order to vertically advance the field of cooling systems.Â