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Evidence reasoning (ER) combined with dimensionless index method can be used in rotating machinery fault diagnosis. In ER algorithm, reliability is mainly obtained in two ways: distance-based method and correlation measure by set theory. In practice, the distance-based method cannot generate high-discrimination reliability in high-coincidence data like dimensionless index data. Therefore, correlation measure by set theory method is used in fault diagnosis more frequently. Because correlation measure by set theory only considers upper bound and lower bound of fault data, we add a regularization term to calculate the relationship between the inner data. Experience result shows that fault diagnosis accuracy had improved, which illustrates that the new reliability can describe data relationship better. [ABSTRACT FROM AUTHOR]

It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN) algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk. [ABSTRACT FROM AUTHOR]