Multi-Source Uncertain Information Fusion Method for Fault Diagnosis Based on Evidence Theory

Abstract

Because of the measurement error and impact of other external factors, the experimentally measured fault information of rotary machinery equipment is with randomness and uncertainty. The diagnosis result gotten with uncertain information will not be accurate. Multi-source information fusion and fault identification based on cloud model and D-S evidence theory is studied in this paper. The rough set theory is used to screen and reduce the multiple fault attribute, then get the fewest fault features which also satisfy the diagnosis. The multi-source information are fused by the calculation of cloud parameters and evidence theory. At last, two kinds of rolling bearing fault databases from experiments are performed, and the diagnosis results have proved the validity and feasibility of the proposed method.