Cleaning Method of Equipment Fault Database Based on ISOMAP Equidistant Mapping Clustering

With the development of artificial intelligence algorithms, monitoring data of power equipment is widely used in early fault diagnosis of equipment. Since the original monitoring data may contain some abnormal “dirty” data, which will seriously affect the accuracy of intelligent diagnosis of equipment faults, it is necessary to clean the equipment fault database. In this paper, the linear model and the proximity model are combined, and a data cleaning method based on ISOMAP(isometric feature mapping) equidistant map clustering is constructed. Firstly, the ISOMAP analysis method is used to extract the features of the original data, and on this basis, the DBSCAN(density-based spatial clustering of applications with noise) density clustering analysis is carried out. This method fully considers the nonlinear relationship and clustering characteristics between the characteristic quantities of power equipment, removes outliers, and improves data quality. This paper takes the cleaning of the fault database of the dissolved gas concentration in the oil of the transformer as an example. The results show that the fault diagnosis learning network trained by the cleaned database can improve the diagnosis accuracy by 17%. Compared with other data cleaning algorithm models, the classification and recognition rate of the method proposed in this paper is improved by 5%.