A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Abstract

Conventional order tracking, which relies on a reference signal, is a common tool for rotary machinery fault diagnosis under speed fluctuation working conditions. However, it is inconvenient to install a speed sensor under certain circumstances. In this paper, we present a two-stage method to detect variable speed rolling bearing faults without a tachometer. In the first stage, the spline-kernelled chirplet transform (SCT) is applied to calculate the time-frequency distribution and extract the instantaneous rotation frequency. In the second stage, angle synchronous averaging is employed to resample the raw signal and detect fault features. The comparison investigations between SCT and other time-frequency analysis methods are performed by a numerical signal with a nonlinear instantaneous frequency. The experimental studies of variable speed rolling bearings with the outer and inner race faults are further performed. The results demonstrate that the proposed method can well detect rolling bearing faults without a key-phase signal from strong background noise under varying speed conditions.