Safety monitoring of machinery equipment and fault diagnosis method based on support vector machine and improved evidence theory

摘要

Safe and reliable operation of machinery is the primary requirement of enterprise production and the basis for realising the economic benefits. Some of the fault diagnosis methods use evidence theory to determine the fault type by collecting the vibration signals. However, in a complex operating environment, the evidence of vibration signal is high-conflict, so it is difficult to obtain the correct fault type. In order to solve this problem, an improved evidence theory-based fault diagnosis method is proposed. First, vibration sensors are used to monitor operating conditions of mechanical equipment and collect vibration signals, and then the dimensionless indicators of these vibration signals are calculated to build the feature dataset. Next, the support vector machine (SVM) is applied to the preliminary fault diagnosis, and the probability of various fault types obtained by the SVM primary fault diagnosis is used as the basic probability assignment (BPA) of evidence. Finally, the improved evidence combination rule based on the Tanimoto coefficient and information entropy is used to fuse the evidence, thus forming the final diagnosis result. The experiments show that the proposed method is effective, achieving the fault diagnosis accuracy of 93.33%.