Distributed adaptive process monitoring and fault diagnosis based on data techniques

Integrating data analysis, data interpretation and data mining techniques, proposed in this work is a distributed process monitoring and fault diagnosis system. Complex process is partitioned into a number of sub-processes according to fundamental structure of the process. Data analysis and data interpretation techniques are used to monitor sub-processes locally, while data mining to find implicit correlativity between sub-processes, The proposed system is demonstrated in an industrial lubricating de-waxing oil recovery system case.