Research on Fault Detection Method for Special Equipment Under the Condition of Sample Missing

In the fault diagnosis method of data-driven method, it is difficult to obtain fault data and high cost of experiment due to the particularity of special equipment and health condition for a long time at the beginning of operation. Based on the analysis of slow-changing parameters such as temperature and pressure collected under normal operation, this paper establishes signal prediction models under different conditions and puts forward a historical view. The dynamic threshold method of measuring data eliminates the false alarm and improves the ability of early fault detection at the initial stage of equipment operation, and provides a new idea for fault detection under the condition of only normal samples. It provides scientific and accurate support for fault early warning theory and method of special equipment and realizes the direction of special equipment from regular maintenance and preventive maintenance to condition-based maintenance change.

Automated summary

This paper proposes a data-driven fault diagnosis method, which establishes signal prediction models based on the analysis of parameters such as temperature and pressure under normal operation. By using the dynamic threshold method, the ability of early fault detection at the initial stage of equipment operation is improved, providing scientific support for fault early warning and maintenance of special equipment.