Application of Continuous Wavelet Transform and Statistical Process Control Chart in Gear Health State Assessment

Timely detection of gear faults is of great significance for improving equipment operating efficiency and economic benefits. Firstly, the original vibration data of the gearbox is subjected to continuous wavelet transform analysis. Subsequently, typical signals within one rotation period of the gear are extracted based on time synchronous averaging. Secondly, a vector autoregressive model is used to model the healthy data and obtain the residuals of the overall dataset. Furthermore, the residual data were used to extract characteristic parameters that can reflect the progressive degradation of gears. Subsequently, the T
²
control chart and cumulative sum (CUSUM) chart were utilized to identify the health status of gears. Different applicable conditions for each control chart were analyzed, as well as appropriate analysis of different parameters. The research results demonstrate that using the T
²
control chart can more precisely point the occurrence of faults at data file number 14 and 71, thus, the effectiveness and advantages of statistical process control chart in detecting gear health status are affirmed.