Method of health monitoring for complex equipment based on density clustering

Abstract

A density clustering-based health monitoring model for complex equipment is proposed for the complex equipment historical data which often has the characteristics of non-spherical shape. The local density and inter-class distance of each sample are estimated from the historical data, and the statistical properties of both are considered to determine the clustering center of the data. For the newly collected complex equipment health status monitoring data, if it is reachable with the density of the clustering center, the complex equipment is considered to be in a healthy state, otherwise it is in a non-healthy state. An actual complex equipment data set is analyzed by numerical simulation techniques, as well as visualization techniques such as scatter plots, box plots and parallel coordinate systems are used to verify the reliability of the calculated results, and the simulation results show that the proposed method can effectively monitor the health status of complex equipment.

Keywords

• density clustering|health monitoring|local density|distance among classes|parallel coordinate|density arrived