Novel data compression algorithm for process data

Data compression algorithms are used in process plants to store and transmit data for analysis purposes. Amount of data is increasing in process plants due to advances in automation and process monitoring technologies. Process data historians are used in plants to store, manage, retrieve and analyze process data. Process data historians use data compression algorithms to effectively manage large amount of data. Best practiced compression algorithm in process data historians has a severe drawback that it significantly alters the statistical properties of reconstructed data; this results in incorrect analysis results which have financial and safety implications for the process plants. Proposed compression algorithm is designed to preserve the critical statistical properties for process data analysis which supports operational decision making in process plants. Case studies are presented on real plant data and simulated data to compare the performance of proposed algorithm with best practiced algorithm used in process data historians.