Fault Warning Technology Based on Multivariate Statistical Analysis

Traditional data-based modeling methods require obtaining many fault samples for fault warning. However, during the operation of the equipment, there is a large amount of normal operating state data collected while there is a small amount of failure sample data. Therefore, the reliability of failure samples is not high during the operation of the equipment. Multiple statistical analysis and state monitoring techniques, such as principal component analysis, can construct fault warning models with only data under normal working conditions. This article combines kernel transformation with principal component analysis to construct a kernel principal component analysis method suitable for small amounts of fault data conditions, thereby achieving effective early warning of equipment operation abnormalities and faults. This article proposes a fault identification method based on multivariate contribution rate graph to address the difficulty of identifying fault sources, achieving precise identification and localization of fault sources under abnormal working conditions. The research results of this article can lay the foundation for establishing an early warning model for equipment failures.